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| author | dschult <dschult@colgate.edu> | 2023-01-04 02:03:56 +0000 |
|---|---|---|
| committer | dschult <dschult@colgate.edu> | 2023-01-04 02:03:56 +0000 |
| commit | df2486e5dc8529b3dcb0cc9ce01b6e7a358bff1d (patch) | |
| tree | 93fde08f4e07dd9dd3a6f860d8470dda6d86df32 | |
| parent | 7bd598bdb6b0ad1a1b9f546c77f3060b1b34a9c6 (diff) | |
| download | networkx-df2486e5dc8529b3dcb0cc9ce01b6e7a358bff1d.tar.gz | |
Deploying to gh-pages from @ networkx/networkx@49f20fe11d2a804a83d538759e06511eb90ad5a3 🚀
125 files changed, 618 insertions, 618 deletions
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Binary files differindex 486863c4..fab6469d 100644 --- a/_images/sphx_glr_plot_subgraphs_thumb.png +++ b/_images/sphx_glr_plot_subgraphs_thumb.png diff --git a/_images/sphx_glr_plot_words_001.png b/_images/sphx_glr_plot_words_001.png Binary files differindex 5459aa37..80411d5d 100644 --- a/_images/sphx_glr_plot_words_001.png +++ b/_images/sphx_glr_plot_words_001.png diff --git a/_images/sphx_glr_plot_words_thumb.png b/_images/sphx_glr_plot_words_thumb.png Binary files differindex eef15abb..0548ec0d 100644 --- a/_images/sphx_glr_plot_words_thumb.png +++ b/_images/sphx_glr_plot_words_thumb.png diff --git a/_images/tutorial-35.png b/_images/tutorial-35.png Binary files differindex 5ae1b78d..d7651092 100644 --- a/_images/tutorial-35.png +++ b/_images/tutorial-35.png diff --git a/auto_examples/3d_drawing/plot_basic.html b/auto_examples/3d_drawing/plot_basic.html index f9381a52..a8a85335 100644 --- a/auto_examples/3d_drawing/plot_basic.html +++ b/auto_examples/3d_drawing/plot_basic.html @@ -540,7 +540,7 @@ to download the full example code</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.115 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.080 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-3d-drawing-plot-basic-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/79beefddd68fa45123e60db5559f52aa/plot_basic.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_basic.py</span></code></a></p> diff --git a/auto_examples/3d_drawing/sg_execution_times.html b/auto_examples/3d_drawing/sg_execution_times.html index 41010b76..3452e135 100644 --- a/auto_examples/3d_drawing/sg_execution_times.html +++ b/auto_examples/3d_drawing/sg_execution_times.html @@ -463,11 +463,11 @@ <section id="computation-times"> <span id="sphx-glr-auto-examples-3d-drawing-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this heading">#</a></h1> -<p><strong>00:00.115</strong> total execution time for <strong>auto_examples_3d_drawing</strong> files:</p> +<p><strong>00:00.080</strong> total execution time for <strong>auto_examples_3d_drawing</strong> files:</p> <table class="table"> <tbody> <tr class="row-odd"><td><p><a class="reference internal" href="plot_basic.html#sphx-glr-auto-examples-3d-drawing-plot-basic-py"><span class="std std-ref">Basic matplotlib</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_basic.py</span></code>)</p></td> -<td><p>00:00.115</p></td> +<td><p>00:00.080</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="mayavi2_spring.html#sphx-glr-auto-examples-3d-drawing-mayavi2-spring-py"><span class="std std-ref">Mayavi2</span></a> (<code class="docutils literal notranslate"><span class="pre">mayavi2_spring.py</span></code>)</p></td> diff --git a/auto_examples/algorithms/plot_beam_search.html b/auto_examples/algorithms/plot_beam_search.html index fd39a01d..8e931372 100644 --- a/auto_examples/algorithms/plot_beam_search.html +++ b/auto_examples/algorithms/plot_beam_search.html @@ -612,7 +612,7 @@ the progressive widening search in order to find a node of high centrality.</p> <img src="../../_images/sphx_glr_plot_beam_search_001.png" srcset="../../_images/sphx_glr_plot_beam_search_001.png" alt="plot beam search" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>found node 73 with centrality 0.12598283530728402 </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.297 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.230 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-algorithms-plot-beam-search-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/ccbccb63fd600240faf98d07876c0e92/plot_beam_search.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_beam_search.py</span></code></a></p> diff --git a/auto_examples/algorithms/plot_betweenness_centrality.html b/auto_examples/algorithms/plot_betweenness_centrality.html index 6b047341..acc8c179 100644 --- a/auto_examples/algorithms/plot_betweenness_centrality.html +++ b/auto_examples/algorithms/plot_betweenness_centrality.html @@ -582,7 +582,7 @@ using WormNet v.3-GS.</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 5.712 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 3.635 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-algorithms-plot-betweenness-centrality-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/b3018a1aab7bffbd1426574de5a8c65a/plot_betweenness_centrality.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_betweenness_centrality.py</span></code></a></p> diff --git a/auto_examples/algorithms/plot_blockmodel.html b/auto_examples/algorithms/plot_blockmodel.html index 39c8060b..5d1c1f2e 100644 --- a/auto_examples/algorithms/plot_blockmodel.html +++ b/auto_examples/algorithms/plot_blockmodel.html @@ -579,7 +579,7 @@ used is the Hartford, CT drug users network:</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.494 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.392 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-algorithms-plot-blockmodel-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/efbe368eaa1e457c6c03d3f5a636063a/plot_blockmodel.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_blockmodel.py</span></code></a></p> diff --git a/auto_examples/algorithms/plot_circuits.html b/auto_examples/algorithms/plot_circuits.html index 2b7cb1e1..2a26da29 100644 --- a/auto_examples/algorithms/plot_circuits.html +++ b/auto_examples/algorithms/plot_circuits.html @@ -603,7 +603,7 @@ fourth layer.</p> <img src="../../_images/sphx_glr_plot_circuits_001.png" srcset="../../_images/sphx_glr_plot_circuits_001.png" alt="((x ∨ y) ∧ (y ∨ ¬(z)))" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>((x ∨ y) ∧ (y ∨ ¬(z))) </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.159 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.107 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-algorithms-plot-circuits-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/bd2ce07c5ba253eb7b45764c94237a4c/plot_circuits.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_circuits.py</span></code></a></p> diff --git a/auto_examples/algorithms/plot_davis_club.html b/auto_examples/algorithms/plot_davis_club.html index 4079ab3f..508c7b75 100644 --- a/auto_examples/algorithms/plot_davis_club.html +++ b/auto_examples/algorithms/plot_davis_club.html @@ -639,7 +639,7 @@ The graph is bipartite (clubs, women).</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.105 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.077 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-algorithms-plot-davis-club-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/6a1e333663010969e61d07b33c7845f0/plot_davis_club.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_davis_club.py</span></code></a></p> diff --git a/auto_examples/algorithms/plot_dedensification.html b/auto_examples/algorithms/plot_dedensification.html index 5ba987e6..9fe0eedb 100644 --- a/auto_examples/algorithms/plot_dedensification.html +++ b/auto_examples/algorithms/plot_dedensification.html @@ -593,7 +593,7 @@ would result in fewer edges in the compressed graph.</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.381 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.255 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-algorithms-plot-dedensification-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/868e28431bab2565b22bfbab847e1153/plot_dedensification.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_dedensification.py</span></code></a></p> diff --git a/auto_examples/algorithms/plot_iterated_dynamical_systems.html b/auto_examples/algorithms/plot_iterated_dynamical_systems.html index 1bd4a113..d5b16cee 100644 --- a/auto_examples/algorithms/plot_iterated_dynamical_systems.html +++ b/auto_examples/algorithms/plot_iterated_dynamical_systems.html @@ -699,7 +699,7 @@ fixed points are [] <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"fixed points are </span><span class="si">{</span><span class="n">fixed_points</span><span class="p">(</span><span class="n">G</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.128 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.096 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-algorithms-plot-iterated-dynamical-systems-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/d947686c24b50c278c1228ff766cda27/plot_iterated_dynamical_systems.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_iterated_dynamical_systems.py</span></code></a></p> diff --git a/auto_examples/algorithms/plot_krackhardt_centrality.html b/auto_examples/algorithms/plot_krackhardt_centrality.html index c925ce24..6b713218 100644 --- a/auto_examples/algorithms/plot_krackhardt_centrality.html +++ b/auto_examples/algorithms/plot_krackhardt_centrality.html @@ -569,7 +569,7 @@ Closeness centrality <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.083 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.062 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-algorithms-plot-krackhardt-centrality-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/e77acafa90a347f4353549d3bffbb72c/plot_krackhardt_centrality.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_krackhardt_centrality.py</span></code></a></p> diff --git a/auto_examples/algorithms/plot_parallel_betweenness.html b/auto_examples/algorithms/plot_parallel_betweenness.html index 67d469f3..7766f2d4 100644 --- a/auto_examples/algorithms/plot_parallel_betweenness.html +++ b/auto_examples/algorithms/plot_parallel_betweenness.html @@ -517,29 +517,29 @@ faster. This is a limitation of our CI/CD pipeline running on a single core.</p> <img src="../../_images/sphx_glr_plot_parallel_betweenness_001.png" srcset="../../_images/sphx_glr_plot_parallel_betweenness_001.png" alt="plot parallel betweenness" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Computing betweenness centrality for: Graph with 1000 nodes and 2991 edges Parallel version - Time: 2.3492 seconds - Betweenness centrality for node 0: 0.09607 + Time: 1.7818 seconds + Betweenness centrality for node 0: 0.31038 Non-Parallel version - Time: 3.8726 seconds - Betweenness centrality for node 0: 0.09607 + Time: 2.9739 seconds + Betweenness centrality for node 0: 0.31038 Computing betweenness centrality for: -Graph with 1000 nodes and 4959 edges +Graph with 1000 nodes and 5022 edges Parallel version - Time: 3.1350 seconds - Betweenness centrality for node 0: 0.00268 + Time: 2.3230 seconds + Betweenness centrality for node 0: 0.00215 Non-Parallel version - Time: 5.2007 seconds - Betweenness centrality for node 0: 0.00268 + Time: 3.9431 seconds + Betweenness centrality for node 0: 0.00215 Computing betweenness centrality for: Graph with 1000 nodes and 2000 edges Parallel version - Time: 2.0758 seconds - Betweenness centrality for node 0: 0.02492 + Time: 1.5404 seconds + Betweenness centrality for node 0: 0.00225 Non-Parallel version - Time: 3.5621 seconds - Betweenness centrality for node 0: 0.02492 + Time: 2.7722 seconds + Betweenness centrality for node 0: 0.00225 </pre></div> </div> <div class="line-block"> @@ -611,7 +611,7 @@ Graph with 1000 nodes and 2000 edges <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 29.022 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 21.067 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-algorithms-plot-parallel-betweenness-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/8a9ce246f32a6cf6abd470292c7ffa6a/plot_parallel_betweenness.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_parallel_betweenness.py</span></code></a></p> diff --git a/auto_examples/algorithms/plot_rcm.html b/auto_examples/algorithms/plot_rcm.html index d928e6c7..bfd2bcb6 100644 --- a/auto_examples/algorithms/plot_rcm.html +++ b/auto_examples/algorithms/plot_rcm.html @@ -615,7 +615,7 @@ bandwidth: 7 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 1.328 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 1.074 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-algorithms-plot-rcm-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/544d21367fbc1520a180d8891369bb49/plot_rcm.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_rcm.py</span></code></a></p> diff --git a/auto_examples/algorithms/plot_snap.html b/auto_examples/algorithms/plot_snap.html index b4fd7f66..b4142ac7 100644 --- a/auto_examples/algorithms/plot_snap.html +++ b/auto_examples/algorithms/plot_snap.html @@ -610,7 +610,7 @@ graph.</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.272 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.177 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-algorithms-plot-snap-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/0a756ab7ea4b899fa151e327a4dce8d2/plot_snap.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_snap.py</span></code></a></p> diff --git a/auto_examples/algorithms/plot_subgraphs.html b/auto_examples/algorithms/plot_subgraphs.html index 01470664..9a7a3620 100644 --- a/auto_examples/algorithms/plot_subgraphs.html +++ b/auto_examples/algorithms/plot_subgraphs.html @@ -678,7 +678,7 @@ of subgraphs that contain only entirely <code class="xref py py-obj docutils lit <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<img src="../../_images/sphx_glr_plot_subgraphs_007.png" srcset="../../_images/sphx_glr_plot_subgraphs_007.png" alt="The reconstructed graph." class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.966 seconds)</p> +<img src="../../_images/sphx_glr_plot_subgraphs_007.png" srcset="../../_images/sphx_glr_plot_subgraphs_007.png" alt="The reconstructed graph." class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.679 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-algorithms-plot-subgraphs-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/7c14530887a80b15e4b4f3d68b23d114/plot_subgraphs.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_subgraphs.py</span></code></a></p> diff --git a/auto_examples/algorithms/sg_execution_times.html b/auto_examples/algorithms/sg_execution_times.html index df4c7a09..431e2490 100644 --- a/auto_examples/algorithms/sg_execution_times.html +++ b/auto_examples/algorithms/sg_execution_times.html @@ -463,55 +463,55 @@ <section id="computation-times"> <span id="sphx-glr-auto-examples-algorithms-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this heading">#</a></h1> -<p><strong>00:38.947</strong> total execution time for <strong>auto_examples_algorithms</strong> files:</p> +<p><strong>00:27.851</strong> total execution time for <strong>auto_examples_algorithms</strong> files:</p> <table class="table"> <tbody> <tr class="row-odd"><td><p><a class="reference internal" href="plot_parallel_betweenness.html#sphx-glr-auto-examples-algorithms-plot-parallel-betweenness-py"><span class="std std-ref">Parallel Betweenness</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_parallel_betweenness.py</span></code>)</p></td> -<td><p>00:29.022</p></td> +<td><p>00:21.067</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_betweenness_centrality.html#sphx-glr-auto-examples-algorithms-plot-betweenness-centrality-py"><span class="std std-ref">Betweeness Centrality</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_betweenness_centrality.py</span></code>)</p></td> -<td><p>00:05.712</p></td> +<td><p>00:03.635</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_rcm.html#sphx-glr-auto-examples-algorithms-plot-rcm-py"><span class="std std-ref">Reverse Cuthill–McKee</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_rcm.py</span></code>)</p></td> -<td><p>00:01.328</p></td> +<td><p>00:01.074</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_subgraphs.html#sphx-glr-auto-examples-algorithms-plot-subgraphs-py"><span class="std std-ref">Subgraphs</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_subgraphs.py</span></code>)</p></td> -<td><p>00:00.966</p></td> +<td><p>00:00.679</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_blockmodel.html#sphx-glr-auto-examples-algorithms-plot-blockmodel-py"><span class="std std-ref">Blockmodel</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_blockmodel.py</span></code>)</p></td> -<td><p>00:00.494</p></td> +<td><p>00:00.392</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_dedensification.html#sphx-glr-auto-examples-algorithms-plot-dedensification-py"><span class="std std-ref">Dedensification</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_dedensification.py</span></code>)</p></td> -<td><p>00:00.381</p></td> +<td><p>00:00.255</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_beam_search.html#sphx-glr-auto-examples-algorithms-plot-beam-search-py"><span class="std std-ref">Beam Search</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_beam_search.py</span></code>)</p></td> -<td><p>00:00.297</p></td> +<td><p>00:00.230</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_snap.html#sphx-glr-auto-examples-algorithms-plot-snap-py"><span class="std std-ref">SNAP Graph Summary</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_snap.py</span></code>)</p></td> -<td><p>00:00.272</p></td> +<td><p>00:00.177</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_circuits.html#sphx-glr-auto-examples-algorithms-plot-circuits-py"><span class="std std-ref">Circuits</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_circuits.py</span></code>)</p></td> -<td><p>00:00.159</p></td> +<td><p>00:00.107</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_iterated_dynamical_systems.html#sphx-glr-auto-examples-algorithms-plot-iterated-dynamical-systems-py"><span class="std std-ref">Iterated Dynamical Systems</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_iterated_dynamical_systems.py</span></code>)</p></td> -<td><p>00:00.128</p></td> +<td><p>00:00.096</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_davis_club.html#sphx-glr-auto-examples-algorithms-plot-davis-club-py"><span class="std std-ref">Davis Club</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_davis_club.py</span></code>)</p></td> -<td><p>00:00.105</p></td> +<td><p>00:00.077</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_krackhardt_centrality.html#sphx-glr-auto-examples-algorithms-plot-krackhardt-centrality-py"><span class="std std-ref">Krackhardt Centrality</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_krackhardt_centrality.py</span></code>)</p></td> -<td><p>00:00.083</p></td> +<td><p>00:00.062</p></td> <td><p>0.0 MB</p></td> </tr> </tbody> diff --git a/auto_examples/basic/plot_properties.html b/auto_examples/basic/plot_properties.html index 132804be..61170c40 100644 --- a/auto_examples/basic/plot_properties.html +++ b/auto_examples/basic/plot_properties.html @@ -574,7 +574,7 @@ density: 0.26666666666666666 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.130 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.090 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-basic-plot-properties-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/40632926e1e0842cea9103529e4bea12/plot_properties.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_properties.py</span></code></a></p> diff --git a/auto_examples/basic/plot_read_write.html b/auto_examples/basic/plot_read_write.html index b8efe2b8..068ab5a5 100644 --- a/auto_examples/basic/plot_read_write.html +++ b/auto_examples/basic/plot_read_write.html @@ -545,7 +545,7 @@ to download the full example code</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.090 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.063 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-basic-plot-read-write-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/63b2264e53e5d28aeb43b6aa768515b9/plot_read_write.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_read_write.py</span></code></a></p> diff --git a/auto_examples/basic/plot_simple_graph.html b/auto_examples/basic/plot_simple_graph.html index 4cd67857..68b26016 100644 --- a/auto_examples/basic/plot_simple_graph.html +++ b/auto_examples/basic/plot_simple_graph.html @@ -550,7 +550,7 @@ to download the full example code</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<img src="../../_images/sphx_glr_plot_simple_graph_002.png" srcset="../../_images/sphx_glr_plot_simple_graph_002.png" alt="plot simple graph" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.549 seconds)</p> +<img src="../../_images/sphx_glr_plot_simple_graph_002.png" srcset="../../_images/sphx_glr_plot_simple_graph_002.png" alt="plot simple graph" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.322 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-basic-plot-simple-graph-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/0f222beedce48fe624efff9ff2fdc89f/plot_simple_graph.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_simple_graph.py</span></code></a></p> diff --git a/auto_examples/basic/sg_execution_times.html b/auto_examples/basic/sg_execution_times.html index 0c442590..bcab5579 100644 --- a/auto_examples/basic/sg_execution_times.html +++ b/auto_examples/basic/sg_execution_times.html @@ -463,19 +463,19 @@ <section id="computation-times"> <span id="sphx-glr-auto-examples-basic-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this heading">#</a></h1> -<p><strong>00:00.769</strong> total execution time for <strong>auto_examples_basic</strong> files:</p> +<p><strong>00:00.475</strong> total execution time for <strong>auto_examples_basic</strong> files:</p> <table class="table"> <tbody> <tr class="row-odd"><td><p><a class="reference internal" href="plot_simple_graph.html#sphx-glr-auto-examples-basic-plot-simple-graph-py"><span class="std std-ref">Simple graph</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_simple_graph.py</span></code>)</p></td> -<td><p>00:00.549</p></td> +<td><p>00:00.322</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_properties.html#sphx-glr-auto-examples-basic-plot-properties-py"><span class="std std-ref">Properties</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_properties.py</span></code>)</p></td> -<td><p>00:00.130</p></td> +<td><p>00:00.090</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_read_write.html#sphx-glr-auto-examples-basic-plot-read-write-py"><span class="std std-ref">Read and write graphs.</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_read_write.py</span></code>)</p></td> -<td><p>00:00.090</p></td> +<td><p>00:00.063</p></td> <td><p>0.0 MB</p></td> </tr> </tbody> diff --git a/auto_examples/drawing/plot_center_node.html b/auto_examples/drawing/plot_center_node.html index 9c68d416..52a5c01d 100644 --- a/auto_examples/drawing/plot_center_node.html +++ b/auto_examples/drawing/plot_center_node.html @@ -530,7 +530,7 @@ to download the full example code</p> <span class="n">nx</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pos</span></a><span class="p">,</span> <span class="n">with_labels</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.096 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.068 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-center-node-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/8561539ed0b99621dbdbe53646ac5075/plot_center_node.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_center_node.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_chess_masters.html b/auto_examples/drawing/plot_chess_masters.html index 77d452d0..1453dc51 100644 --- a/auto_examples/drawing/plot_chess_masters.html +++ b/auto_examples/drawing/plot_chess_masters.html @@ -536,7 +536,7 @@ to black and contains selected game info.</p> <img src="../../_images/sphx_glr_plot_chess_masters_001.png" srcset="../../_images/sphx_glr_plot_chess_masters_001.png" alt="World Chess Championship Games: 1886 - 1985" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Loaded 685 chess games between 25 players Note the disconnected component consisting of: -['Korchnoi, Viktor L', 'Kasparov, Gary', 'Karpov, Anatoly'] +['Karpov, Anatoly', 'Korchnoi, Viktor L', 'Kasparov, Gary'] From a total of 237 different openings, the following games used the Sicilian opening @@ -702,7 +702,7 @@ findfont: Font family 'Helvetica' not found. <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.539 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.387 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-chess-masters-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/388158421a67216f605c1bbf9aa310bf/plot_chess_masters.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_chess_masters.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_custom_node_icons.html b/auto_examples/drawing/plot_custom_node_icons.html index 90997060..c0c1abe3 100644 --- a/auto_examples/drawing/plot_custom_node_icons.html +++ b/auto_examples/drawing/plot_custom_node_icons.html @@ -585,7 +585,7 @@ to download the full example code</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.435 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.279 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-custom-node-icons-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/b580b9776494e714c1fb1880f03524a8/plot_custom_node_icons.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_custom_node_icons.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_degree.html b/auto_examples/drawing/plot_degree.html index 6f88dc6f..7c7fa28a 100644 --- a/auto_examples/drawing/plot_degree.html +++ b/auto_examples/drawing/plot_degree.html @@ -561,7 +561,7 @@ each node is determined, and a figure is generated showing three things: <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.410 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.266 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-degree-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/70eaef0d99343cf8d3d6e70c803ad5a8/plot_degree.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_degree.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_directed.html b/auto_examples/drawing/plot_directed.html index 8e10635e..b1f619ad 100644 --- a/auto_examples/drawing/plot_directed.html +++ b/auto_examples/drawing/plot_directed.html @@ -556,7 +556,7 @@ to download the full example code</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.348 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.219 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-directed-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/6c2f9c3544cb695b31867eecc0f7fb1e/plot_directed.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_directed.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_edge_colormap.html b/auto_examples/drawing/plot_edge_colormap.html index d0d1490e..76f698aa 100644 --- a/auto_examples/drawing/plot_edge_colormap.html +++ b/auto_examples/drawing/plot_edge_colormap.html @@ -534,7 +534,7 @@ to download the full example code</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.093 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.063 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-edge-colormap-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/7ea4dc8cf44604668540ed81d6abebda/plot_edge_colormap.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_edge_colormap.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_ego_graph.html b/auto_examples/drawing/plot_ego_graph.html index 6a31c014..373ab30b 100644 --- a/auto_examples/drawing/plot_ego_graph.html +++ b/auto_examples/drawing/plot_ego_graph.html @@ -546,7 +546,7 @@ the largest hub in a Barabási-Albert network.</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.142 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.097 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-ego-graph-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/773fa56bdb128b8bd2a4f4a0e4dd38aa/plot_ego_graph.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_ego_graph.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_eigenvalues.html b/auto_examples/drawing/plot_eigenvalues.html index 23fa05ac..2e41ae6d 100644 --- a/auto_examples/drawing/plot_eigenvalues.html +++ b/auto_examples/drawing/plot_eigenvalues.html @@ -517,8 +517,8 @@ to download the full example code</p> <section class="sphx-glr-example-title" id="eigenvalues"> <span id="sphx-glr-auto-examples-drawing-plot-eigenvalues-py"></span><h1>Eigenvalues<a class="headerlink" href="#eigenvalues" title="Permalink to this heading">#</a></h1> <p>Create an G{n,m} random graph and compute the eigenvalues.</p> -<img src="../../_images/sphx_glr_plot_eigenvalues_001.png" srcset="../../_images/sphx_glr_plot_eigenvalues_001.png" alt="plot eigenvalues" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Largest eigenvalue: 1.5924617911775805 -Smallest eigenvalue: 4.0699282104742547e-16 +<img src="../../_images/sphx_glr_plot_eigenvalues_001.png" srcset="../../_images/sphx_glr_plot_eigenvalues_001.png" alt="plot eigenvalues" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Largest eigenvalue: 1.592461791177574 +Smallest eigenvalue: -2.5363890312656235e-16 </pre></div> </div> <div class="line-block"> @@ -541,7 +541,7 @@ Smallest eigenvalue: 4.0699282104742547e-16 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.994 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.646 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-eigenvalues-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/a8660a7bb6b65b5a644025485c973cb9/plot_eigenvalues.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_eigenvalues.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_four_grids.html b/auto_examples/drawing/plot_four_grids.html index 71a2dea5..a57201a9 100644 --- a/auto_examples/drawing/plot_four_grids.html +++ b/auto_examples/drawing/plot_four_grids.html @@ -562,7 +562,7 @@ customize the visualization of a simple Graph comprising a 4x4 grid.</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.532 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.328 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-four-grids-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/4136c066ab1d073cf527e9dc02bfec77/plot_four_grids.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_four_grids.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_house_with_colors.html b/auto_examples/drawing/plot_house_with_colors.html index b79eebf0..f3fb9fc1 100644 --- a/auto_examples/drawing/plot_house_with_colors.html +++ b/auto_examples/drawing/plot_house_with_colors.html @@ -538,7 +538,7 @@ to download the full example code</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.111 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.076 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-house-with-colors-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/98363b3c011ceaffb10684a5ba5de25b/plot_house_with_colors.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_house_with_colors.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_knuth_miles.html b/auto_examples/drawing/plot_knuth_miles.html index cbd9ca0c..ba6d5dfc 100644 --- a/auto_examples/drawing/plot_knuth_miles.html +++ b/auto_examples/drawing/plot_knuth_miles.html @@ -660,7 +660,7 @@ Graph with 128 nodes and 8128 edges <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.133 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.101 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-knuth-miles-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/e921c603ea1764485dc9acff178a2f05/plot_knuth_miles.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_knuth_miles.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_labels_and_colors.html b/auto_examples/drawing/plot_labels_and_colors.html index 3d0c2ace..8295b840 100644 --- a/auto_examples/drawing/plot_labels_and_colors.html +++ b/auto_examples/drawing/plot_labels_and_colors.html @@ -566,7 +566,7 @@ components of a graph.</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.263 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.188 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-labels-and-colors-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/cff4f78bc18685caa50507ced57e7c6f/plot_labels_and_colors.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_labels_and_colors.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_multipartite_graph.html b/auto_examples/drawing/plot_multipartite_graph.html index e44c90f8..d87b0728 100644 --- a/auto_examples/drawing/plot_multipartite_graph.html +++ b/auto_examples/drawing/plot_multipartite_graph.html @@ -553,7 +553,7 @@ to download the full example code</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.153 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.077 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-multipartite-graph-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/6cb4bf689cf53c849bce13cbab13eaec/plot_multipartite_graph.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_multipartite_graph.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_node_colormap.html b/auto_examples/drawing/plot_node_colormap.html index 0d8f7561..ef46ae2b 100644 --- a/auto_examples/drawing/plot_node_colormap.html +++ b/auto_examples/drawing/plot_node_colormap.html @@ -526,7 +526,7 @@ to download the full example code</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.088 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.051 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-node-colormap-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/19db6fb1da12c9b9c0afca26691448c8/plot_node_colormap.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_node_colormap.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_rainbow_coloring.html b/auto_examples/drawing/plot_rainbow_coloring.html index e15723bf..308d7915 100644 --- a/auto_examples/drawing/plot_rainbow_coloring.html +++ b/auto_examples/drawing/plot_rainbow_coloring.html @@ -578,7 +578,7 @@ helpful in determining how to place the tree copies.</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.173 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.127 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-rainbow-coloring-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/b64fd85d6e5ba509e65b2cb30a8274ed/plot_rainbow_coloring.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_rainbow_coloring.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_random_geometric_graph.html b/auto_examples/drawing/plot_random_geometric_graph.html index 3fea9848..1421c55d 100644 --- a/auto_examples/drawing/plot_random_geometric_graph.html +++ b/auto_examples/drawing/plot_random_geometric_graph.html @@ -555,7 +555,7 @@ to download the full example code</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.134 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.108 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-random-geometric-graph-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/f8f8cacecc651443537b92fc341fba08/plot_random_geometric_graph.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_random_geometric_graph.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_sampson.html b/auto_examples/drawing/plot_sampson.html index 332fa86f..82d517f3 100644 --- a/auto_examples/drawing/plot_sampson.html +++ b/auto_examples/drawing/plot_sampson.html @@ -557,7 +557,7 @@ to download the full example code</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.371 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.245 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-sampson-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/838bbb120e1c43a61657821eddf29c25/plot_sampson.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_sampson.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_selfloops.html b/auto_examples/drawing/plot_selfloops.html index ab5826a8..78ec105d 100644 --- a/auto_examples/drawing/plot_selfloops.html +++ b/auto_examples/drawing/plot_selfloops.html @@ -540,7 +540,7 @@ This example shows how to draw self-loops with <code class="xref py py-obj docut <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.123 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.081 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-selfloops-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/b6f62567cb843f23abdd4b7268921c0b/plot_selfloops.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_selfloops.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_simple_path.html b/auto_examples/drawing/plot_simple_path.html index ebbd3d07..fb81c810 100644 --- a/auto_examples/drawing/plot_simple_path.html +++ b/auto_examples/drawing/plot_simple_path.html @@ -526,7 +526,7 @@ to download the full example code</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.082 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.058 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-simple-path-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/2c281c05b18d8d3cf43a312fc3d67a3b/plot_simple_path.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_simple_path.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_spectral_grid.html b/auto_examples/drawing/plot_spectral_grid.html index 9a32ae25..06127370 100644 --- a/auto_examples/drawing/plot_spectral_grid.html +++ b/auto_examples/drawing/plot_spectral_grid.html @@ -568,7 +568,7 @@ As you remove internal nodes, this effect increases.</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.345 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.230 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-spectral-grid-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/5479a9bd23bf1ace2ef03c13b4ac9d7f/plot_spectral_grid.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_spectral_grid.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_tsp.html b/auto_examples/drawing/plot_tsp.html index 1d9ec9c2..9f2fdef7 100644 --- a/auto_examples/drawing/plot_tsp.html +++ b/auto_examples/drawing/plot_tsp.html @@ -568,7 +568,7 @@ that the traveler has to follow in order to minimize the total cost.</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.125 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.087 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-tsp-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/cc9848c15dd2eeae1872b955a8f34d15/plot_tsp.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_tsp.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_unix_email.html b/auto_examples/drawing/plot_unix_email.html index 09231b0b..0e92a178 100644 --- a/auto_examples/drawing/plot_unix_email.html +++ b/auto_examples/drawing/plot_unix_email.html @@ -583,7 +583,7 @@ From: ted@com To: alice@edu Subject: get together for lunch to discuss Networks? <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.166 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.155 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-unix-email-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/213697eef7dec7ebca6ee2e064eb9c24/plot_unix_email.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_unix_email.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_weighted_graph.html b/auto_examples/drawing/plot_weighted_graph.html index 88853e64..de96eb03 100644 --- a/auto_examples/drawing/plot_weighted_graph.html +++ b/auto_examples/drawing/plot_weighted_graph.html @@ -556,7 +556,7 @@ to download the full example code</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.121 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.083 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-weighted-graph-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/32d3b6ab4dec83957a1981fa91e52e14/plot_weighted_graph.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_weighted_graph.py</span></code></a></p> diff --git a/auto_examples/drawing/sg_execution_times.html b/auto_examples/drawing/sg_execution_times.html index 73635fc2..0dc3c619 100644 --- a/auto_examples/drawing/sg_execution_times.html +++ b/auto_examples/drawing/sg_execution_times.html @@ -463,99 +463,99 @@ <section id="computation-times"> <span id="sphx-glr-auto-examples-drawing-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this heading">#</a></h1> -<p><strong>00:05.980</strong> total execution time for <strong>auto_examples_drawing</strong> files:</p> +<p><strong>00:04.021</strong> total execution time for <strong>auto_examples_drawing</strong> files:</p> <table class="table"> <tbody> <tr class="row-odd"><td><p><a class="reference internal" href="plot_eigenvalues.html#sphx-glr-auto-examples-drawing-plot-eigenvalues-py"><span class="std std-ref">Eigenvalues</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_eigenvalues.py</span></code>)</p></td> -<td><p>00:00.994</p></td> +<td><p>00:00.646</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_chess_masters.html#sphx-glr-auto-examples-drawing-plot-chess-masters-py"><span class="std std-ref">Chess Masters</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_chess_masters.py</span></code>)</p></td> -<td><p>00:00.539</p></td> +<td><p>00:00.387</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_four_grids.html#sphx-glr-auto-examples-drawing-plot-four-grids-py"><span class="std std-ref">Four Grids</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_four_grids.py</span></code>)</p></td> -<td><p>00:00.532</p></td> +<td><p>00:00.328</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_custom_node_icons.html#sphx-glr-auto-examples-drawing-plot-custom-node-icons-py"><span class="std std-ref">Custom node icons</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_custom_node_icons.py</span></code>)</p></td> -<td><p>00:00.435</p></td> +<td><p>00:00.279</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_degree.html#sphx-glr-auto-examples-drawing-plot-degree-py"><span class="std std-ref">Degree Analysis</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_degree.py</span></code>)</p></td> -<td><p>00:00.410</p></td> +<td><p>00:00.266</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_sampson.html#sphx-glr-auto-examples-drawing-plot-sampson-py"><span class="std std-ref">Sampson</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_sampson.py</span></code>)</p></td> -<td><p>00:00.371</p></td> +<td><p>00:00.245</p></td> <td><p>0.0 MB</p></td> </tr> -<tr class="row-odd"><td><p><a class="reference internal" href="plot_directed.html#sphx-glr-auto-examples-drawing-plot-directed-py"><span class="std std-ref">Directed Graph</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_directed.py</span></code>)</p></td> -<td><p>00:00.348</p></td> +<tr class="row-odd"><td><p><a class="reference internal" href="plot_spectral_grid.html#sphx-glr-auto-examples-drawing-plot-spectral-grid-py"><span class="std std-ref">Spectral Embedding</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_spectral_grid.py</span></code>)</p></td> +<td><p>00:00.230</p></td> <td><p>0.0 MB</p></td> </tr> -<tr class="row-even"><td><p><a class="reference internal" href="plot_spectral_grid.html#sphx-glr-auto-examples-drawing-plot-spectral-grid-py"><span class="std std-ref">Spectral Embedding</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_spectral_grid.py</span></code>)</p></td> -<td><p>00:00.345</p></td> +<tr class="row-even"><td><p><a class="reference internal" href="plot_directed.html#sphx-glr-auto-examples-drawing-plot-directed-py"><span class="std std-ref">Directed Graph</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_directed.py</span></code>)</p></td> +<td><p>00:00.219</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_labels_and_colors.html#sphx-glr-auto-examples-drawing-plot-labels-and-colors-py"><span class="std std-ref">Labels And Colors</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_labels_and_colors.py</span></code>)</p></td> -<td><p>00:00.263</p></td> +<td><p>00:00.188</p></td> <td><p>0.0 MB</p></td> </tr> -<tr class="row-even"><td><p><a class="reference internal" 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href="plot_delaunay.html#sphx-glr-auto-examples-geospatial-plot-delaunay-py"><span class="std std-ref">Delaunay graphs from geographic points</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_delaunay.py</span></code>)</p></td> -<td><p>00:05.344</p></td> +<td><p>00:03.808</p></td> <td><p>0.0 MB</p></td> </tr> -<tr class="row-odd"><td><p><a class="reference internal" href="plot_points.html#sphx-glr-auto-examples-geospatial-plot-points-py"><span class="std std-ref">Graphs from geographic points</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_points.py</span></code>)</p></td> -<td><p>00:05.292</p></td> +<tr class="row-odd"><td><p><a class="reference internal" href="plot_lines.html#sphx-glr-auto-examples-geospatial-plot-lines-py"><span class="std std-ref">Graphs from a set of lines</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_lines.py</span></code>)</p></td> +<td><p>00:03.270</p></td> <td><p>0.0 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-<td><p>00:00.595</p></td> +<td><p>00:00.430</p></td> <td><p>0.0 MB</p></td> </tr> </tbody> diff --git a/auto_examples/graph/plot_dag_layout.html b/auto_examples/graph/plot_dag_layout.html index 3d4c5f21..96b247e3 100644 --- a/auto_examples/graph/plot_dag_layout.html +++ b/auto_examples/graph/plot_dag_layout.html @@ -541,7 +541,7 @@ order.</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.188 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.118 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graph-plot-dag-layout-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/317508b452046ab7944bed07a87a11a5/plot_dag_layout.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_dag_layout.py</span></code></a></p> diff --git a/auto_examples/graph/plot_degree_sequence.html b/auto_examples/graph/plot_degree_sequence.html index cb55f342..9205188a 100644 --- a/auto_examples/graph/plot_degree_sequence.html +++ b/auto_examples/graph/plot_degree_sequence.html @@ -548,7 +548,7 @@ degree #nodes <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.084 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.058 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graph-plot-degree-sequence-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/27102b9986eea2f742603c5d8496d2f8/plot_degree_sequence.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_degree_sequence.py</span></code></a></p> diff --git a/auto_examples/graph/plot_erdos_renyi.html b/auto_examples/graph/plot_erdos_renyi.html index 22e6aeba..338423c4 100644 --- a/auto_examples/graph/plot_erdos_renyi.html +++ b/auto_examples/graph/plot_erdos_renyi.html @@ -562,7 +562,7 @@ the adjacency list <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.087 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.059 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graph-plot-erdos-renyi-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/1dae7040b667b61c3253579b3b21fe83/plot_erdos_renyi.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_erdos_renyi.py</span></code></a></p> diff --git a/auto_examples/graph/plot_expected_degree_sequence.html b/auto_examples/graph/plot_expected_degree_sequence.html index 6fe8d05d..1704fd44 100644 --- a/auto_examples/graph/plot_expected_degree_sequence.html +++ b/auto_examples/graph/plot_expected_degree_sequence.html @@ -540,46 +540,46 @@ degree (#nodes) **** 30 ( 0) 31 ( 0) 32 ( 1) * -33 ( 1) * -34 ( 2) ** -35 ( 3) *** -36 ( 7) ******* -37 ( 7) ******* -38 ( 6) ****** -39 (12) ************ -40 (15) *************** -41 (11) *********** -42 (10) ********** -43 (21) ********************* -44 (13) ************* -45 (14) ************** -46 (24) ************************ -47 (26) ************************** -48 (27) *************************** +33 ( 0) +34 ( 0) +35 ( 0) +36 ( 2) ** +37 ( 3) *** +38 ( 5) ***** +39 ( 8) ******** +40 ( 8) ******** +41 ( 9) ********* +42 (17) ***************** +43 (13) ************* +44 (26) ************************** +45 (18) ****************** +46 (21) ********************* +47 (28) **************************** +48 (43) ******************************************* 49 (32) ******************************** -50 (31) ******************************* -51 (26) ************************** -52 (31) ******************************* -53 (22) ********************** -54 (24) ************************ -55 (29) ***************************** -56 (21) ********************* -57 (17) ***************** +50 (25) ************************* +51 (35) *********************************** +52 (37) ************************************* +53 (19) ******************* +54 (29) ***************************** +55 (30) ****************************** +56 (16) **************** +57 (14) ************** 58 (10) ********** -59 (13) ************* -60 (12) ************ -61 (11) *********** -62 ( 6) ****** -63 ( 3) *** -64 ( 6) ****** +59 (12) ************ +60 ( 8) ******** +61 ( 3) *** +62 (14) ************** +63 ( 4) **** +64 ( 1) * 65 ( 3) *** -66 ( 0) -67 ( 1) * +66 ( 1) * +67 ( 2) ** 68 ( 0) 69 ( 0) -70 ( 0) -71 ( 1) * -72 ( 0) +70 ( 1) * +71 ( 0) +72 ( 1) * 73 ( 1) * </pre></div> </div> @@ -600,7 +600,7 @@ degree (#nodes) **** <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">i</span></a><span class="si">:</span><span class="s2">2</span><span class="si">}</span><span class="s2"> (</span><span class="si">{</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">d</span></a><span class="si">:</span><span class="s2">2</span><span class="si">}</span><span class="s2">) </span><span class="si">{</span><span class="s1">'*'</span><span class="o">*</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">d</span></a><span class="si">}</span><span class="s2">"</span><span class="p">)</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.041 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.030 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graph-plot-expected-degree-sequence-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/7378087382f40e96e66bce4a35ba0e52/plot_expected_degree_sequence.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_expected_degree_sequence.py</span></code></a></p> diff --git a/auto_examples/graph/plot_football.html b/auto_examples/graph/plot_football.html index 6dd7c282..25cb3c26 100644 --- a/auto_examples/graph/plot_football.html +++ b/auto_examples/graph/plot_football.html @@ -686,7 +686,7 @@ Hawaii 11 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.393 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.553 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graph-plot-football-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/ca0a30060f60faf520286faa348f4700/plot_football.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_football.py</span></code></a></p> diff --git a/auto_examples/graph/plot_karate_club.html b/auto_examples/graph/plot_karate_club.html index b4052214..6dd520a8 100644 --- a/auto_examples/graph/plot_karate_club.html +++ b/auto_examples/graph/plot_karate_club.html @@ -562,7 +562,7 @@ Journal of Anthropological Research, 33, 452-473.</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.132 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.089 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graph-plot-karate-club-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/373a1e407e4caee6fc7b7b46704a985c/plot_karate_club.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_karate_club.py</span></code></a></p> diff --git a/auto_examples/graph/plot_morse_trie.html b/auto_examples/graph/plot_morse_trie.html index 6a46f709..f9b5579d 100644 --- a/auto_examples/graph/plot_morse_trie.html +++ b/auto_examples/graph/plot_morse_trie.html @@ -602,7 +602,7 @@ the path.</p> <span class="nb">print</span><span class="p">(</span><span class="s2">" "</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="n">morse_encode</span><span class="p">(</span><span class="n">ltr</span><span class="p">)</span> <span class="k">for</span> <span class="n">ltr</span> <span class="ow">in</span> <span class="s2">"ilovenetworkx"</span><span class="p">]))</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.280 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.186 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graph-plot-morse-trie-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/60379a4283563d425090aaae07ab115a/plot_morse_trie.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_morse_trie.py</span></code></a></p> diff --git a/auto_examples/graph/plot_napoleon_russian_campaign.html b/auto_examples/graph/plot_napoleon_russian_campaign.html index b398f00c..99635977 100644 --- a/auto_examples/graph/plot_napoleon_russian_campaign.html +++ b/auto_examples/graph/plot_napoleon_russian_campaign.html @@ -632,7 +632,7 @@ to download the full example code</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.181 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.128 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graph-plot-napoleon-russian-campaign-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/87e75a2d09fb817a4616bb71aa44546f/plot_napoleon_russian_campaign.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_napoleon_russian_campaign.py</span></code></a></p> diff --git a/auto_examples/graph/plot_roget.html b/auto_examples/graph/plot_roget.html index 6f351461..986e6017 100644 --- a/auto_examples/graph/plot_roget.html +++ b/auto_examples/graph/plot_roget.html @@ -588,7 +588,7 @@ DiGraph with 1022 nodes and 5075 edges <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.302 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.228 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graph-plot-roget-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/118b3a0c87610e4910d74143c904d290/plot_roget.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_roget.py</span></code></a></p> diff --git a/auto_examples/graph/plot_triad_types.html b/auto_examples/graph/plot_triad_types.html index 0a4c2465..27c4a038 100644 --- a/auto_examples/graph/plot_triad_types.html +++ b/auto_examples/graph/plot_triad_types.html @@ -563,7 +563,7 @@ the Orientation as Up (U), Down (D) , Cyclical (C) or Transitive (T).</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 1.716 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 1.102 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graph-plot-triad-types-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/b7a826e19c8bd8bafecaae1ae69c7d1d/plot_triad_types.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_triad_types.py</span></code></a></p> diff --git a/auto_examples/graph/plot_words.html b/auto_examples/graph/plot_words.html index e826b74c..181807c0 100644 --- a/auto_examples/graph/plot_words.html +++ b/auto_examples/graph/plot_words.html @@ -624,7 +624,7 @@ None <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.539 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.387 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graph-plot-words-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/e6a489a8b2deb49ed237fac38a28f429/plot_words.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_words.py</span></code></a></p> diff --git a/auto_examples/graph/sg_execution_times.html b/auto_examples/graph/sg_execution_times.html index fc6df6c9..54abf17b 100644 --- a/auto_examples/graph/sg_execution_times.html +++ b/auto_examples/graph/sg_execution_times.html @@ -463,51 +463,51 @@ <section id="computation-times"> <span id="sphx-glr-auto-examples-graph-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this heading">#</a></h1> -<p><strong>00:03.943</strong> total execution time for <strong>auto_examples_graph</strong> files:</p> +<p><strong>00:02.939</strong> total execution time for <strong>auto_examples_graph</strong> files:</p> <table class="table"> <tbody> <tr class="row-odd"><td><p><a class="reference internal" href="plot_triad_types.html#sphx-glr-auto-examples-graph-plot-triad-types-py"><span class="std std-ref">Triads</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_triad_types.py</span></code>)</p></td> -<td><p>00:01.716</p></td> +<td><p>00:01.102</p></td> <td><p>0.0 MB</p></td> </tr> -<tr class="row-even"><td><p><a class="reference internal" href="plot_words.html#sphx-glr-auto-examples-graph-plot-words-py"><span class="std std-ref">Words/Ladder Graph</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_words.py</span></code>)</p></td> -<td><p>00:00.539</p></td> +<tr class="row-even"><td><p><a class="reference internal" href="plot_football.html#sphx-glr-auto-examples-graph-plot-football-py"><span class="std std-ref">Football</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_football.py</span></code>)</p></td> +<td><p>00:00.553</p></td> <td><p>0.0 MB</p></td> </tr> -<tr class="row-odd"><td><p><a class="reference internal" href="plot_football.html#sphx-glr-auto-examples-graph-plot-football-py"><span class="std std-ref">Football</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_football.py</span></code>)</p></td> -<td><p>00:00.393</p></td> +<tr class="row-odd"><td><p><a class="reference internal" href="plot_words.html#sphx-glr-auto-examples-graph-plot-words-py"><span class="std std-ref">Words/Ladder Graph</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_words.py</span></code>)</p></td> +<td><p>00:00.387</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_roget.html#sphx-glr-auto-examples-graph-plot-roget-py"><span class="std std-ref">Roget</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_roget.py</span></code>)</p></td> -<td><p>00:00.302</p></td> +<td><p>00:00.228</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_morse_trie.html#sphx-glr-auto-examples-graph-plot-morse-trie-py"><span class="std std-ref">Morse Trie</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_morse_trie.py</span></code>)</p></td> -<td><p>00:00.280</p></td> +<td><p>00:00.186</p></td> <td><p>0.0 MB</p></td> </tr> -<tr class="row-even"><td><p><a class="reference internal" href="plot_dag_layout.html#sphx-glr-auto-examples-graph-plot-dag-layout-py"><span class="std std-ref">DAG - Topological Layout</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_dag_layout.py</span></code>)</p></td> -<td><p>00:00.188</p></td> +<tr class="row-even"><td><p><a class="reference internal" href="plot_napoleon_russian_campaign.html#sphx-glr-auto-examples-graph-plot-napoleon-russian-campaign-py"><span class="std std-ref">Napoleon Russian Campaign</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_napoleon_russian_campaign.py</span></code>)</p></td> +<td><p>00:00.128</p></td> <td><p>0.0 MB</p></td> </tr> -<tr class="row-odd"><td><p><a class="reference internal" href="plot_napoleon_russian_campaign.html#sphx-glr-auto-examples-graph-plot-napoleon-russian-campaign-py"><span class="std std-ref">Napoleon Russian Campaign</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_napoleon_russian_campaign.py</span></code>)</p></td> -<td><p>00:00.181</p></td> +<tr class="row-odd"><td><p><a class="reference internal" href="plot_dag_layout.html#sphx-glr-auto-examples-graph-plot-dag-layout-py"><span class="std std-ref">DAG - Topological Layout</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_dag_layout.py</span></code>)</p></td> +<td><p>00:00.118</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_karate_club.html#sphx-glr-auto-examples-graph-plot-karate-club-py"><span class="std std-ref">Karate Club</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_karate_club.py</span></code>)</p></td> -<td><p>00:00.132</p></td> +<td><p>00:00.089</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_erdos_renyi.html#sphx-glr-auto-examples-graph-plot-erdos-renyi-py"><span class="std std-ref">Erdos Renyi</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_erdos_renyi.py</span></code>)</p></td> -<td><p>00:00.087</p></td> +<td><p>00:00.059</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_degree_sequence.html#sphx-glr-auto-examples-graph-plot-degree-sequence-py"><span class="std std-ref">Degree Sequence</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_degree_sequence.py</span></code>)</p></td> -<td><p>00:00.084</p></td> +<td><p>00:00.058</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_expected_degree_sequence.html#sphx-glr-auto-examples-graph-plot-expected-degree-sequence-py"><span class="std std-ref">Expected Degree Sequence</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_expected_degree_sequence.py</span></code>)</p></td> -<td><p>00:00.041</p></td> +<td><p>00:00.030</p></td> <td><p>0.0 MB</p></td> </tr> </tbody> diff --git a/auto_examples/graphviz_drawing/plot_attributes.html b/auto_examples/graphviz_drawing/plot_attributes.html index 0c5bb4c5..23e37c98 100644 --- a/auto_examples/graphviz_drawing/plot_attributes.html +++ b/auto_examples/graphviz_drawing/plot_attributes.html @@ -532,7 +532,7 @@ node node attributes <span class="nb">print</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">nodes</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="kc">True</span><span class="p">))</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.039 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.105 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graphviz-drawing-plot-attributes-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/52bb0ebd52824aa460a3ecb45c1cb5e5/plot_attributes.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_attributes.py</span></code></a></p> diff --git a/auto_examples/graphviz_drawing/plot_conversion.html b/auto_examples/graphviz_drawing/plot_conversion.html index e0373657..e2475dda 100644 --- a/auto_examples/graphviz_drawing/plot_conversion.html +++ b/auto_examples/graphviz_drawing/plot_conversion.html @@ -514,7 +514,7 @@ to download the full example code</p> <a href="https://pygraphviz.github.io/documentation/stable/reference/agraph.html#pygraphviz.AGraph.draw" title="pygraphviz.AGraph.draw" class="sphx-glr-backref-module-pygraphviz sphx-glr-backref-type-py-method"><span class="n">A</span><span class="o">.</span><span class="n">draw</span></a><span class="p">(</span><span class="s2">"k5.png"</span><span class="p">,</span> <span class="n">prog</span><span class="o">=</span><span class="s2">"neato"</span><span class="p">)</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.033 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.026 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graphviz-drawing-plot-conversion-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/27aa0c08bacf20ba3f5ce4f8d02ac226/plot_conversion.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_conversion.py</span></code></a></p> diff --git a/auto_examples/graphviz_drawing/plot_grid.html b/auto_examples/graphviz_drawing/plot_grid.html index ac822ba5..adbfdcf8 100644 --- a/auto_examples/graphviz_drawing/plot_grid.html +++ b/auto_examples/graphviz_drawing/plot_grid.html @@ -519,7 +519,7 @@ Graphviz command line interface to create visualizations.</p> <img src="../../_images/sphx_glr_plot_grid_001.png" srcset="../../_images/sphx_glr_plot_grid_001.png" alt="plot grid" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Now run: neato -Tps grid.dot >grid.ps </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.085 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.068 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graphviz-drawing-plot-grid-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/26e3cd745ae317a76a0df34cbf4999d8/plot_grid.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_grid.py</span></code></a></p> diff --git a/auto_examples/graphviz_drawing/plot_mini_atlas.html b/auto_examples/graphviz_drawing/plot_mini_atlas.html index f747ad20..c580e2a0 100644 --- a/auto_examples/graphviz_drawing/plot_mini_atlas.html +++ b/auto_examples/graphviz_drawing/plot_mini_atlas.html @@ -543,7 +543,7 @@ Graph named 'G19' with 5 nodes and 0 edges <a href="https://pygraphviz.github.io/documentation/stable/reference/agraph.html#pygraphviz.AGraph.draw" title="pygraphviz.AGraph.draw" class="sphx-glr-backref-module-pygraphviz sphx-glr-backref-type-py-method"><span class="n">A</span><span class="o">.</span><span class="n">draw</span></a><span class="p">(</span><span class="s2">"A20.png"</span><span class="p">,</span> <span class="n">prog</span><span class="o">=</span><span class="s2">"neato"</span><span class="p">)</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.105 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.081 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graphviz-drawing-plot-mini-atlas-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/cc271806f4fdfe8710206c593b90e506/plot_mini_atlas.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_mini_atlas.py</span></code></a></p> diff --git a/auto_examples/graphviz_drawing/sg_execution_times.html b/auto_examples/graphviz_drawing/sg_execution_times.html index 254e399e..aa50e45f 100644 --- a/auto_examples/graphviz_drawing/sg_execution_times.html +++ b/auto_examples/graphviz_drawing/sg_execution_times.html @@ -463,23 +463,23 @@ <section id="computation-times"> <span id="sphx-glr-auto-examples-graphviz-drawing-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this heading">#</a></h1> -<p><strong>00:00.262</strong> total execution time for <strong>auto_examples_graphviz_drawing</strong> files:</p> +<p><strong>00:00.280</strong> total execution time for <strong>auto_examples_graphviz_drawing</strong> files:</p> <table class="table"> <tbody> -<tr class="row-odd"><td><p><a class="reference internal" href="plot_mini_atlas.html#sphx-glr-auto-examples-graphviz-drawing-plot-mini-atlas-py"><span class="std std-ref">Atlas</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_mini_atlas.py</span></code>)</p></td> +<tr class="row-odd"><td><p><a class="reference internal" href="plot_attributes.html#sphx-glr-auto-examples-graphviz-drawing-plot-attributes-py"><span class="std std-ref">Attributes</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_attributes.py</span></code>)</p></td> <td><p>00:00.105</p></td> <td><p>0.0 MB</p></td> </tr> -<tr class="row-even"><td><p><a class="reference internal" href="plot_grid.html#sphx-glr-auto-examples-graphviz-drawing-plot-grid-py"><span class="std std-ref">2D Grid</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_grid.py</span></code>)</p></td> -<td><p>00:00.085</p></td> +<tr class="row-even"><td><p><a class="reference internal" href="plot_mini_atlas.html#sphx-glr-auto-examples-graphviz-drawing-plot-mini-atlas-py"><span class="std std-ref">Atlas</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_mini_atlas.py</span></code>)</p></td> +<td><p>00:00.081</p></td> <td><p>0.0 MB</p></td> </tr> -<tr class="row-odd"><td><p><a class="reference internal" href="plot_attributes.html#sphx-glr-auto-examples-graphviz-drawing-plot-attributes-py"><span class="std std-ref">Attributes</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_attributes.py</span></code>)</p></td> -<td><p>00:00.039</p></td> +<tr class="row-odd"><td><p><a class="reference internal" href="plot_grid.html#sphx-glr-auto-examples-graphviz-drawing-plot-grid-py"><span class="std std-ref">2D Grid</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_grid.py</span></code>)</p></td> +<td><p>00:00.068</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_conversion.html#sphx-glr-auto-examples-graphviz-drawing-plot-conversion-py"><span class="std std-ref">Conversion</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_conversion.py</span></code>)</p></td> -<td><p>00:00.033</p></td> +<td><p>00:00.026</p></td> <td><p>0.0 MB</p></td> </tr> </tbody> diff --git a/auto_examples/graphviz_layout/plot_atlas.html b/auto_examples/graphviz_layout/plot_atlas.html index 7bdac4fd..be86d74b 100644 --- a/auto_examples/graphviz_layout/plot_atlas.html +++ b/auto_examples/graphviz_layout/plot_atlas.html @@ -549,7 +549,7 @@ We don’t plot the empty graph nor the single node graph. <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 4.994 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 3.761 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graphviz-layout-plot-atlas-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/37c712582f2a7575f32a59a1389228a7/plot_atlas.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_atlas.py</span></code></a></p> diff --git a/auto_examples/graphviz_layout/plot_circular_tree.html b/auto_examples/graphviz_layout/plot_circular_tree.html index 9af4dfcf..c585e64f 100644 --- a/auto_examples/graphviz_layout/plot_circular_tree.html +++ b/auto_examples/graphviz_layout/plot_circular_tree.html @@ -510,7 +510,7 @@ to download the full example code</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.199 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.150 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graphviz-layout-plot-circular-tree-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/e854482dd498b1c5f7f158a5717b999d/plot_circular_tree.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_circular_tree.py</span></code></a></p> diff --git a/auto_examples/graphviz_layout/plot_decomposition.html b/auto_examples/graphviz_layout/plot_decomposition.html index 023bb247..54b05499 100644 --- a/auto_examples/graphviz_layout/plot_decomposition.html +++ b/auto_examples/graphviz_layout/plot_decomposition.html @@ -535,7 +535,7 @@ to download the full example code</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.454 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.301 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graphviz-layout-plot-decomposition-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/533257c084adfbb38066f806a87784c5/plot_decomposition.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_decomposition.py</span></code></a></p> diff --git a/auto_examples/graphviz_layout/plot_giant_component.html b/auto_examples/graphviz_layout/plot_giant_component.html index 6db50cba..4e5d8444 100644 --- a/auto_examples/graphviz_layout/plot_giant_component.html +++ b/auto_examples/graphviz_layout/plot_giant_component.html @@ -543,7 +543,7 @@ giant connected component in a binomial random graph.</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 1.102 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.818 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graphviz-layout-plot-giant-component-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/f5d29b33ff492f40e4749050b3f5e7dd/plot_giant_component.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_giant_component.py</span></code></a></p> diff --git a/auto_examples/graphviz_layout/plot_lanl_routes.html b/auto_examples/graphviz_layout/plot_lanl_routes.html index 25c79269..f1d0eb1c 100644 --- a/auto_examples/graphviz_layout/plot_lanl_routes.html +++ b/auto_examples/graphviz_layout/plot_lanl_routes.html @@ -561,7 +561,7 @@ to download the full example code</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.443 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.341 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graphviz-layout-plot-lanl-routes-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/30e04b92b8aefc7afe7f634d84ae925a/plot_lanl_routes.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_lanl_routes.py</span></code></a></p> diff --git a/auto_examples/graphviz_layout/sg_execution_times.html b/auto_examples/graphviz_layout/sg_execution_times.html index ca50727e..0fe5091c 100644 --- a/auto_examples/graphviz_layout/sg_execution_times.html +++ b/auto_examples/graphviz_layout/sg_execution_times.html @@ -463,27 +463,27 @@ <section id="computation-times"> <span id="sphx-glr-auto-examples-graphviz-layout-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this heading">#</a></h1> -<p><strong>00:07.191</strong> total execution time for <strong>auto_examples_graphviz_layout</strong> files:</p> +<p><strong>00:05.370</strong> total execution time for <strong>auto_examples_graphviz_layout</strong> files:</p> <table class="table"> <tbody> <tr class="row-odd"><td><p><a class="reference internal" href="plot_atlas.html#sphx-glr-auto-examples-graphviz-layout-plot-atlas-py"><span class="std std-ref">Atlas</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_atlas.py</span></code>)</p></td> -<td><p>00:04.994</p></td> +<td><p>00:03.761</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_giant_component.html#sphx-glr-auto-examples-graphviz-layout-plot-giant-component-py"><span class="std std-ref">Giant Component</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_giant_component.py</span></code>)</p></td> -<td><p>00:01.102</p></td> +<td><p>00:00.818</p></td> <td><p>0.0 MB</p></td> </tr> -<tr class="row-odd"><td><p><a class="reference internal" href="plot_decomposition.html#sphx-glr-auto-examples-graphviz-layout-plot-decomposition-py"><span class="std std-ref">Decomposition</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_decomposition.py</span></code>)</p></td> -<td><p>00:00.454</p></td> +<tr class="row-odd"><td><p><a class="reference internal" href="plot_lanl_routes.html#sphx-glr-auto-examples-graphviz-layout-plot-lanl-routes-py"><span class="std std-ref">Lanl Routes</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_lanl_routes.py</span></code>)</p></td> +<td><p>00:00.341</p></td> <td><p>0.0 MB</p></td> </tr> -<tr class="row-even"><td><p><a class="reference internal" href="plot_lanl_routes.html#sphx-glr-auto-examples-graphviz-layout-plot-lanl-routes-py"><span class="std std-ref">Lanl Routes</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_lanl_routes.py</span></code>)</p></td> -<td><p>00:00.443</p></td> +<tr class="row-even"><td><p><a class="reference internal" href="plot_decomposition.html#sphx-glr-auto-examples-graphviz-layout-plot-decomposition-py"><span class="std std-ref">Decomposition</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_decomposition.py</span></code>)</p></td> +<td><p>00:00.301</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_circular_tree.html#sphx-glr-auto-examples-graphviz-layout-plot-circular-tree-py"><span class="std std-ref">Circular Tree</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_circular_tree.py</span></code>)</p></td> -<td><p>00:00.199</p></td> +<td><p>00:00.150</p></td> <td><p>0.0 MB</p></td> </tr> </tbody> diff --git a/auto_examples/subclass/plot_antigraph.html b/auto_examples/subclass/plot_antigraph.html index 39bf62de..9b7a0907 100644 --- a/auto_examples/subclass/plot_antigraph.html +++ b/auto_examples/subclass/plot_antigraph.html @@ -680,7 +680,7 @@ algorithms.</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.125 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.093 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-subclass-plot-antigraph-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/652afbfc3c52c8cdd7689321df2e696a/plot_antigraph.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_antigraph.py</span></code></a></p> diff --git a/auto_examples/subclass/plot_printgraph.html b/auto_examples/subclass/plot_printgraph.html index 370121c7..eb790207 100644 --- a/auto_examples/subclass/plot_printgraph.html +++ b/auto_examples/subclass/plot_printgraph.html @@ -616,7 +616,7 @@ Add edge: 9-12 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.086 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.060 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-subclass-plot-printgraph-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/1b5e7bf8d2514d71280314171170de85/plot_printgraph.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_printgraph.py</span></code></a></p> diff --git a/auto_examples/subclass/sg_execution_times.html b/auto_examples/subclass/sg_execution_times.html index e2665b00..3664e17b 100644 --- a/auto_examples/subclass/sg_execution_times.html +++ b/auto_examples/subclass/sg_execution_times.html @@ -463,15 +463,15 @@ <section id="computation-times"> <span id="sphx-glr-auto-examples-subclass-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this heading">#</a></h1> -<p><strong>00:00.211</strong> total execution time for <strong>auto_examples_subclass</strong> files:</p> +<p><strong>00:00.154</strong> total execution time for <strong>auto_examples_subclass</strong> files:</p> <table class="table"> <tbody> <tr class="row-odd"><td><p><a class="reference internal" href="plot_antigraph.html#sphx-glr-auto-examples-subclass-plot-antigraph-py"><span class="std std-ref">Antigraph</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_antigraph.py</span></code>)</p></td> -<td><p>00:00.125</p></td> +<td><p>00:00.093</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_printgraph.html#sphx-glr-auto-examples-subclass-plot-printgraph-py"><span class="std std-ref">Print Graph</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_printgraph.py</span></code>)</p></td> -<td><p>00:00.086</p></td> +<td><p>00:00.060</p></td> <td><p>0.0 MB</p></td> </tr> </tbody> diff --git a/developer/index.html b/developer/index.html index cfb855df..8a66634c 100644 --- a/developer/index.html +++ b/developer/index.html @@ -493,7 +493,7 @@ <dd class="field-odd"><p>3.0rc2.dev0</p> </dd> <dt class="field-even">Date<span class="colon">:</span></dt> -<dd class="field-even"><p>Jan 03, 2023</p> +<dd class="field-even"><p>Jan 04, 2023</p> </dd> </dl> <div class="toctree-wrapper compound"> @@ -464,7 +464,7 @@ <dd class="field-odd"><p>3.0rc2.dev0</p> </dd> <dt class="field-even">Date<span class="colon">:</span></dt> -<dd class="field-even"><p>Jan 03, 2023</p> +<dd class="field-even"><p>Jan 04, 2023</p> </dd> </dl> <p>NetworkX is a Python package for the creation, manipulation, and study @@ -522,7 +522,7 @@ by Alex Martelli <a class="reference internal" href="#martelli03" id="id10"><spa <section id="license"> <h2>License<a class="headerlink" href="#license" title="Permalink to this heading">#</a></h2> <p>NetworkX is distributed with the 3-clause BSD license.</p> -<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Copyright</span> <span class="p">(</span><span class="n">C</span><span class="p">)</span> <span class="mi">2004</span><span class="o">-</span><span class="mi">2022</span><span class="p">,</span> <span class="n">NetworkX</span> <span class="n">Developers</span> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Copyright</span> <span class="p">(</span><span class="n">C</span><span class="p">)</span> <span class="mi">2004</span><span class="o">-</span><span class="mi">2023</span><span class="p">,</span> <span class="n">NetworkX</span> <span class="n">Developers</span> <span class="n">Aric</span> <span class="n">Hagberg</span> <span class="o"><</span><span class="n">hagberg</span><span class="nd">@lanl</span><span class="o">.</span><span class="n">gov</span><span class="o">></span> <span class="n">Dan</span> <span class="n">Schult</span> <span class="o"><</span><span class="n">dschult</span><span class="nd">@colgate</span><span class="o">.</span><span class="n">edu</span><span class="o">></span> <span class="n">Pieter</span> <span class="n">Swart</span> <span class="o"><</span><span class="n">swart</span><span class="nd">@lanl</span><span class="o">.</span><span class="n">gov</span><span class="o">></span> diff --git a/reference/generated/networkx.generators.geometric.geographical_threshold_graph.html b/reference/generated/networkx.generators.geometric.geographical_threshold_graph.html index 482c777b..f98cae0c 100644 --- a/reference/generated/networkx.generators.geometric.geographical_threshold_graph.html +++ b/reference/generated/networkx.generators.geometric.geographical_threshold_graph.html @@ -674,7 +674,7 @@ originally conceived as a probability density function giving the probability of connecting two nodes that are of metric distance <code class="xref py py-obj docutils literal notranslate"><span class="pre">r</span></code> apart. The implementation here allows for more arbitrary definitions of <code class="xref py py-obj docutils literal notranslate"><span class="pre">p_dist</span></code> that do not need to correspond to valid probability -density functions. The <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/stats.html#module-scipy.stats" title="(in SciPy v1.9.3)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">scipy.stats</span></code></a> package has many +density functions. The <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/stats.html#module-scipy.stats" title="(in SciPy v1.10.0)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">scipy.stats</span></code></a> package has many probability density functions implemented and tools for custom probability density definitions, and passing the <code class="docutils literal notranslate"><span class="pre">.pdf</span></code> method of scipy.stats distributions can be used here. If <code class="docutils literal notranslate"><span class="pre">p_dist=None</span></code> diff --git a/reference/generated/networkx.generators.geometric.geometric_edges.html b/reference/generated/networkx.generators.geometric.geometric_edges.html index efe79007..ccce60a6 100644 --- a/reference/generated/networkx.generators.geometric.geometric_edges.html +++ b/reference/generated/networkx.generators.geometric.geometric_edges.html @@ -653,7 +653,7 @@ distances. The default value is 2, i.e. Euclidean distance.</p> </dl> <p class="rubric">Notes</p> <p>Radius uses Minkowski distance metric <code class="xref py py-obj docutils literal notranslate"><span class="pre">p</span></code>. -If scipy is available, <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.cKDTree.html#scipy.spatial.cKDTree" title="(in SciPy v1.9.3)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scipy.spatial.cKDTree</span></code></a> is used to speed computation.</p> +If scipy is available, <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.cKDTree.html#scipy.spatial.cKDTree" title="(in SciPy v1.10.0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scipy.spatial.cKDTree</span></code></a> is used to speed computation.</p> <p class="rubric">Examples</p> <p>Create a graph with nodes that have a “pos” attribute representing 2D coordinates.</p> diff --git a/reference/generated/networkx.generators.random_graphs.random_kernel_graph.html b/reference/generated/networkx.generators.random_graphs.random_kernel_graph.html index 8b54258c..402f21a5 100644 --- a/reference/generated/networkx.generators.random_graphs.random_kernel_graph.html +++ b/reference/generated/networkx.generators.random_graphs.random_kernel_graph.html @@ -643,7 +643,7 @@ bounded function.</p> <span class="math notranslate nohighlight">\(F(y,a,b) := \int_a^b \kappa(x,y)dx\)</span></p> </dd> <dt><strong>kernel_root: function (optional)</strong></dt><dd><p>Function that returns the root <span class="math notranslate nohighlight">\(b\)</span> of the equation <span class="math notranslate nohighlight">\(F(y,a,b) = r\)</span>. -If None, the root is found using <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.brentq.html#scipy.optimize.brentq" title="(in SciPy v1.9.3)"><code class="xref py py-func docutils literal notranslate"><span class="pre">scipy.optimize.brentq()</span></code></a> +If None, the root is found using <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.brentq.html#scipy.optimize.brentq" title="(in SciPy v1.10.0)"><code class="xref py py-func docutils literal notranslate"><span class="pre">scipy.optimize.brentq()</span></code></a> (this requires SciPy).</p> </dd> <dt><strong>seed</strong><span class="classifier">integer, random_state, or None (default)</span></dt><dd><p>Indicator of random number generation state. diff --git a/reference/index.html b/reference/index.html index 001c4091..590e3f06 100644 --- a/reference/index.html +++ b/reference/index.html @@ -496,7 +496,7 @@ <dd class="field-odd"><p>3.0rc2.dev0</p> </dd> <dt class="field-even">Date<span class="colon">:</span></dt> -<dd class="field-even"><p>Jan 03, 2023</p> +<dd class="field-even"><p>Jan 04, 2023</p> </dd> </dl> </div></blockquote> diff --git a/reference/introduction-7.hires.png b/reference/introduction-7.hires.png Binary files differindex cc9a99ef..927ee58a 100644 --- a/reference/introduction-7.hires.png +++ b/reference/introduction-7.hires.png diff --git a/reference/introduction-7.pdf b/reference/introduction-7.pdf Binary files differindex f84a4ecc..4d44da11 100644 --- a/reference/introduction-7.pdf +++ b/reference/introduction-7.pdf diff --git a/reference/introduction-7.png b/reference/introduction-7.png Binary files differindex 1b022418..00060814 100644 --- a/reference/introduction-7.png +++ b/reference/introduction-7.png diff --git a/reference/introduction.ipynb b/reference/introduction.ipynb index cae82d4c..75f67012 100644 --- a/reference/introduction.ipynb +++ b/reference/introduction.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "032fb79d", + "id": "131a3536", "metadata": {}, "source": [ "## Introduction\n", @@ -34,7 +34,7 @@ { "cell_type": "code", "execution_count": null, - "id": "99f6bdf8", + "id": "9d9272b2", "metadata": {}, "outputs": [], "source": [ @@ -43,7 +43,7 @@ }, { "cell_type": "markdown", - "id": "8ed4bfef", + "id": "236b37be", "metadata": {}, "source": [ "To save repetition, in the documentation we assume that\n", @@ -82,7 +82,7 @@ { "cell_type": "code", "execution_count": null, - "id": "f145b972", + "id": "afa00d31", "metadata": {}, "outputs": [], "source": [ @@ -94,7 +94,7 @@ }, { "cell_type": "markdown", - "id": "3080a91f", + "id": "1e09a450", "metadata": {}, "source": [ "All graph classes allow any [hashable](https://docs.python.org/3/glossary.html#term-hashable) object as a node.\n", @@ -193,7 +193,7 @@ { "cell_type": "code", "execution_count": null, - "id": "8355bc13", + "id": "b639f000", "metadata": {}, "outputs": [], "source": [ @@ -205,7 +205,7 @@ }, { "cell_type": "markdown", - "id": "618c2ee3", + "id": "4dd7e969", "metadata": {}, "source": [ "Edge attributes can be anything:" @@ -214,7 +214,7 @@ { "cell_type": "code", "execution_count": null, - "id": "6c1f6eef", + "id": "428b3ca6", "metadata": {}, "outputs": [], "source": [ @@ -225,7 +225,7 @@ }, { "cell_type": "markdown", - "id": "eaebd399", + "id": "b41eb025", "metadata": {}, "source": [ "You can add many edges at one time:" @@ -234,7 +234,7 @@ { "cell_type": "code", "execution_count": null, - "id": "c831b73c", + "id": "3a09a8f0", "metadata": {}, "outputs": [], "source": [ @@ -246,7 +246,7 @@ }, { "cell_type": "markdown", - "id": "d70bbeff", + "id": "24b9b77d", "metadata": {}, "source": [ "See the Tutorial for more examples.\n", @@ -311,7 +311,7 @@ { "cell_type": "code", "execution_count": null, - "id": "11bef07a", + "id": "9ead068a", "metadata": {}, "outputs": [], "source": [ @@ -323,7 +323,7 @@ }, { "cell_type": "markdown", - "id": "1f73282d", + "id": "c7837ddd", "metadata": {}, "source": [ "# Drawing\n", @@ -344,7 +344,7 @@ { "cell_type": "code", "execution_count": null, - "id": "e37c43b3", + "id": "065c1191", "metadata": {}, "outputs": [], "source": [ @@ -358,7 +358,7 @@ }, { "cell_type": "markdown", - "id": "5af7e732", + "id": "02d93645", "metadata": {}, "source": [ "See the examples for more ideas.\n", @@ -398,7 +398,7 @@ { "cell_type": "code", "execution_count": null, - "id": "4aacda5f", + "id": "60f0bd11", "metadata": {}, "outputs": [], "source": [ @@ -410,7 +410,7 @@ }, { "cell_type": "markdown", - "id": "570035bb", + "id": "74d0822f", "metadata": {}, "source": [ "The data structure gets morphed slightly for each base graph class.\n", @@ -428,7 +428,7 @@ { "cell_type": "code", "execution_count": null, - "id": "c7e74c22", + "id": "497a016a", "metadata": {}, "outputs": [], "source": [ diff --git a/reference/introduction_full.ipynb b/reference/introduction_full.ipynb index fb2591c7..59f24b24 100644 --- a/reference/introduction_full.ipynb +++ b/reference/introduction_full.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "032fb79d", + "id": "131a3536", "metadata": {}, "source": [ "## Introduction\n", @@ -34,13 +34,13 @@ { "cell_type": "code", "execution_count": 1, - "id": "99f6bdf8", + "id": "9d9272b2", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T21:20:09.005963Z", - "iopub.status.busy": "2023-01-03T21:20:09.005709Z", - "iopub.status.idle": "2023-01-03T21:20:09.112055Z", - "shell.execute_reply": "2023-01-03T21:20:09.111154Z" + "iopub.execute_input": "2023-01-04T02:02:30.469665Z", + "iopub.status.busy": "2023-01-04T02:02:30.469148Z", + "iopub.status.idle": "2023-01-04T02:02:30.544512Z", + "shell.execute_reply": "2023-01-04T02:02:30.543782Z" } }, "outputs": [], @@ -50,7 +50,7 @@ }, { "cell_type": "markdown", - "id": "8ed4bfef", + "id": "236b37be", "metadata": {}, "source": [ "To save repetition, in the documentation we assume that\n", @@ -89,13 +89,13 @@ { "cell_type": "code", "execution_count": 2, - "id": "f145b972", + "id": "afa00d31", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T21:20:09.116588Z", - "iopub.status.busy": "2023-01-03T21:20:09.116303Z", - "iopub.status.idle": "2023-01-03T21:20:09.120925Z", - "shell.execute_reply": "2023-01-03T21:20:09.119959Z" + "iopub.execute_input": "2023-01-04T02:02:30.548417Z", + "iopub.status.busy": "2023-01-04T02:02:30.548157Z", + "iopub.status.idle": "2023-01-04T02:02:30.551908Z", + "shell.execute_reply": "2023-01-04T02:02:30.551155Z" } }, "outputs": [], @@ -108,7 +108,7 @@ }, { "cell_type": "markdown", - "id": "3080a91f", + "id": "1e09a450", "metadata": {}, "source": [ "All graph classes allow any [hashable](https://docs.python.org/3/glossary.html#term-hashable) object as a node.\n", @@ -207,13 +207,13 @@ { "cell_type": "code", "execution_count": 3, - "id": "8355bc13", + "id": "b639f000", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T21:20:09.125798Z", - "iopub.status.busy": "2023-01-03T21:20:09.125426Z", - "iopub.status.idle": "2023-01-03T21:20:09.129842Z", - "shell.execute_reply": "2023-01-03T21:20:09.128767Z" + "iopub.execute_input": "2023-01-04T02:02:30.555401Z", + "iopub.status.busy": "2023-01-04T02:02:30.555156Z", + "iopub.status.idle": "2023-01-04T02:02:30.558835Z", + "shell.execute_reply": "2023-01-04T02:02:30.558178Z" } }, "outputs": [], @@ -226,7 +226,7 @@ }, { "cell_type": "markdown", - "id": "618c2ee3", + "id": "4dd7e969", "metadata": {}, "source": [ "Edge attributes can be anything:" @@ -235,13 +235,13 @@ { "cell_type": "code", "execution_count": 4, - "id": "6c1f6eef", + "id": "428b3ca6", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T21:20:09.133724Z", - "iopub.status.busy": "2023-01-03T21:20:09.133474Z", - "iopub.status.idle": "2023-01-03T21:20:09.137388Z", - "shell.execute_reply": "2023-01-03T21:20:09.136596Z" + "iopub.execute_input": "2023-01-04T02:02:30.562162Z", + "iopub.status.busy": "2023-01-04T02:02:30.561935Z", + "iopub.status.idle": "2023-01-04T02:02:30.565364Z", + "shell.execute_reply": "2023-01-04T02:02:30.564706Z" } }, "outputs": [], @@ -253,7 +253,7 @@ }, { "cell_type": "markdown", - "id": "eaebd399", + "id": "b41eb025", "metadata": {}, "source": [ "You can add many edges at one time:" @@ -262,13 +262,13 @@ { "cell_type": "code", "execution_count": 5, - "id": "c831b73c", + "id": "3a09a8f0", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T21:20:09.142122Z", - "iopub.status.busy": "2023-01-03T21:20:09.141793Z", - "iopub.status.idle": "2023-01-03T21:20:09.146367Z", - "shell.execute_reply": "2023-01-03T21:20:09.145606Z" + "iopub.execute_input": "2023-01-04T02:02:30.568691Z", + "iopub.status.busy": "2023-01-04T02:02:30.568465Z", + "iopub.status.idle": "2023-01-04T02:02:30.572554Z", + "shell.execute_reply": "2023-01-04T02:02:30.571900Z" } }, "outputs": [], @@ -281,7 +281,7 @@ }, { "cell_type": "markdown", - "id": "d70bbeff", + "id": "24b9b77d", "metadata": {}, "source": [ "See the Tutorial for more examples.\n", @@ -346,13 +346,13 @@ { "cell_type": "code", "execution_count": 6, - "id": "11bef07a", + "id": "9ead068a", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T21:20:09.150056Z", - "iopub.status.busy": "2023-01-03T21:20:09.149675Z", - "iopub.status.idle": "2023-01-03T21:20:09.156548Z", - "shell.execute_reply": "2023-01-03T21:20:09.155416Z" + "iopub.execute_input": "2023-01-04T02:02:30.575923Z", + "iopub.status.busy": "2023-01-04T02:02:30.575699Z", + "iopub.status.idle": "2023-01-04T02:02:30.580257Z", + "shell.execute_reply": "2023-01-04T02:02:30.579582Z" } }, "outputs": [ @@ -373,7 +373,7 @@ }, { "cell_type": "markdown", - "id": "1f73282d", + "id": "c7837ddd", "metadata": {}, "source": [ "# Drawing\n", @@ -394,19 +394,19 @@ { "cell_type": "code", "execution_count": 7, - "id": "e37c43b3", + "id": "065c1191", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T21:20:09.162652Z", - "iopub.status.busy": "2023-01-03T21:20:09.162394Z", - "iopub.status.idle": "2023-01-03T21:20:09.921522Z", - "shell.execute_reply": "2023-01-03T21:20:09.920601Z" + "iopub.execute_input": "2023-01-04T02:02:30.585378Z", + "iopub.status.busy": "2023-01-04T02:02:30.585143Z", + "iopub.status.idle": "2023-01-04T02:02:31.192916Z", + "shell.execute_reply": "2023-01-04T02:02:31.192308Z" } }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "<Figure size 640x480 with 2 Axes>" ] @@ -426,7 +426,7 @@ }, { "cell_type": "markdown", - "id": "5af7e732", + "id": "02d93645", "metadata": {}, "source": [ "See the examples for more ideas.\n", @@ -466,13 +466,13 @@ { "cell_type": "code", "execution_count": 8, - "id": "4aacda5f", + "id": "60f0bd11", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T21:20:09.925805Z", - "iopub.status.busy": "2023-01-03T21:20:09.925376Z", - "iopub.status.idle": "2023-01-03T21:20:09.930536Z", - "shell.execute_reply": "2023-01-03T21:20:09.929703Z" + "iopub.execute_input": "2023-01-04T02:02:31.196994Z", + "iopub.status.busy": "2023-01-04T02:02:31.196382Z", + "iopub.status.idle": "2023-01-04T02:02:31.200579Z", + "shell.execute_reply": "2023-01-04T02:02:31.200053Z" } }, "outputs": [ @@ -493,7 +493,7 @@ }, { "cell_type": "markdown", - "id": "570035bb", + "id": "74d0822f", "metadata": {}, "source": [ "The data structure gets morphed slightly for each base graph class.\n", @@ -511,13 +511,13 @@ { "cell_type": "code", "execution_count": 9, - "id": "c7e74c22", + "id": "497a016a", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T21:20:09.936229Z", - "iopub.status.busy": "2023-01-03T21:20:09.935979Z", - "iopub.status.idle": "2023-01-03T21:20:09.941219Z", - "shell.execute_reply": "2023-01-03T21:20:09.940350Z" + "iopub.execute_input": "2023-01-04T02:02:31.203648Z", + "iopub.status.busy": "2023-01-04T02:02:31.203215Z", + "iopub.status.idle": "2023-01-04T02:02:31.207393Z", + "shell.execute_reply": "2023-01-04T02:02:31.206889Z" } }, "outputs": [ diff --git a/reference/readwrite/matrix_market.html b/reference/readwrite/matrix_market.html index 89da69c7..9ed710c5 100644 --- a/reference/readwrite/matrix_market.html +++ b/reference/readwrite/matrix_market.html @@ -507,9 +507,9 @@ NIST. Matrix Market supports both a <strong>coordinate format</strong> for sparse matrices and an <strong>array format</strong> for dense matrices. -The <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/io.html#module-scipy.io" title="(in SciPy v1.9.3)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">scipy.io</span></code></a> module provides the <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.mmread.html#scipy.io.mmread" title="(in SciPy v1.9.3)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scipy.io.mmread</span></code></a> and <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.mmwrite.html#scipy.io.mmwrite" title="(in SciPy v1.9.3)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scipy.io.mmwrite</span></code></a> +The <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/io.html#module-scipy.io" title="(in SciPy v1.10.0)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">scipy.io</span></code></a> module provides the <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.mmread.html#scipy.io.mmread" title="(in SciPy v1.10.0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scipy.io.mmread</span></code></a> and <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.mmwrite.html#scipy.io.mmwrite" title="(in SciPy v1.10.0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scipy.io.mmwrite</span></code></a> functions to read and write data in Matrix Market format, respectively. -These functions work with either <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a> or <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.coo_matrix.html#scipy.sparse.coo_matrix" title="(in SciPy v1.9.3)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scipy.sparse.coo_matrix</span></code></a> +These functions work with either <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a> or <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.coo_matrix.html#scipy.sparse.coo_matrix" title="(in SciPy v1.10.0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scipy.sparse.coo_matrix</span></code></a> objects depending on whether the data is in <strong>array</strong> or <strong>coordinate</strong> format. These functions can be combined with those of NetworkX’s <a class="reference internal" href="../convert.html#module-networkx.convert_matrix" title="networkx.convert_matrix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">convert_matrix</span></code></a> module to read and write Graphs in Matrix Market format.</p> diff --git a/release/release_2.7.html b/release/release_2.7.html index 395b7acd..6bef9fdb 100644 --- a/release/release_2.7.html +++ b/release/release_2.7.html @@ -543,11 +543,11 @@ problem: <a class="reference internal" href="../reference/algorithms/generated/n <a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">FutureWarning</span></code></a> to all functions that return a <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.matrix.html#numpy.matrix" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.matrix</span></code></a> instance. The <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.matrix.html#numpy.matrix" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.matrix</span></code></a> class will be replaced with 2D <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a> instances in NetworkX 3.0.</p></li> -<li><p>Added support for the <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/sparse.html#module-scipy.sparse" title="(in SciPy v1.9.3)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scipy.sparse</span></code></a> array interface. This includes +<li><p>Added support for the <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/sparse.html#module-scipy.sparse" title="(in SciPy v1.10.0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scipy.sparse</span></code></a> array interface. This includes <a class="reference internal" href="../reference/generated/networkx.convert_matrix.to_scipy_sparse_array.html#networkx.convert_matrix.to_scipy_sparse_array" title="networkx.convert_matrix.to_scipy_sparse_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_scipy_sparse_array</span></code></a> and <a class="reference internal" href="../reference/generated/networkx.convert_matrix.from_scipy_sparse_array.html#networkx.convert_matrix.from_scipy_sparse_array" title="networkx.convert_matrix.from_scipy_sparse_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_scipy_sparse_array</span></code></a>. In NetworkX 3.0, sparse arrays will replace sparse matrices as the primary interface to -<a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/sparse.html#module-scipy.sparse" title="(in SciPy v1.9.3)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scipy.sparse</span></code></a>. New code should use <code class="docutils literal notranslate"><span class="pre">to_scipy_sparse_array</span></code> and +<a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/sparse.html#module-scipy.sparse" title="(in SciPy v1.10.0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scipy.sparse</span></code></a>. New code should use <code class="docutils literal notranslate"><span class="pre">to_scipy_sparse_array</span></code> and <code class="docutils literal notranslate"><span class="pre">from_scipy_sparse_array</span></code> instead of their matrix counterparts. 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class="xref py py-obj docutils literal notranslate"><span class="pre">scipy.sparse</span></code></a> array interface.</p></li> +Add support for the <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/sparse.html#module-scipy.sparse" title="(in SciPy v1.10.0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scipy.sparse</span></code></a> array interface.</p></li> <li><p>[<a class="reference external" href="https://github.com/networkx/networkx/pull/5144">#5144</a>] Improve readibility of <code class="docutils literal notranslate"><span class="pre">node_classification</span></code> functions.</p></li> <li><p>[<a class="reference external" href="https://github.com/networkx/networkx/pull/5145">#5145</a>] diff --git a/searchindex.js b/searchindex.js index 76bd4c2f..d5bacbf8 100644 --- a/searchindex.js +++ b/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["auto_examples/3d_drawing/index", 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691, 720, 722, 723, 724, 725, 726, 729, 730, 731, 748, 749, 751, 762, 767, 770, 775, 786, 791, 796, 850, 853, 854, 855, 856, 857, 862, 865, 867, 870, 871, 873, 874, 878, 879, 882, 887, 888, 892, 895, 898, 899, 900, 901, 902, 907, 910, 912, 914, 916, 917, 921, 925, 928, 931, 934, 935, 936, 937, 938, 943, 946, 947, 948, 951, 952, 955, 956, 960, 963, 968, 973, 976, 979, 980, 981, 982, 983, 988, 991, 992, 993, 995, 998, 999, 1003, 1007, 1010, 1036, 1037, 1038, 1039, 1040, 1042, 1045, 1047, 1059, 1060, 1061, 1063, 1066, 1068, 1082, 1085, 1088, 1102, 1103, 1105, 1129, 1133, 1135, 1137, 1148, 1151, 1154, 1164, 1165, 1166, 1167, 1174, 1175, 1177, 1193, 1196, 1197, 1198, 1206, 1207, 1217, 1218, 1219, 1222, 1235, 1246, 1248, 1250, 1258, 1263, 1264, 1269, 1272, 1275, 1276, 1278, 1279, 1281, 1282, 1283, 1284, 1295, 1296, 1297, 1299, 1301, 1302, 1303, 1320, 1321, 1323, 1324, 1326, 1328, 1329, 1330, 1333, 1334, 1347, 1349, 1352, 1354, 1356, 1357, 1362, 1363, 1371, 1372, 1378, 1380, 1382, 1385, 1387, 1388, 1392, 1393, 1394, 1395, 1396, 1399, 1402, 1404, 1405, 1406, 1408, 1409, 1412, 1425, 1426], "more": [8, 43, 53, 67, 86, 92, 93, 94, 97, 99, 100, 101, 102, 103, 107, 109, 110, 111, 114, 115, 121, 127, 128, 143, 165, 172, 198, 199, 202, 204, 215, 216, 218, 219, 220, 221, 230, 231, 235, 256, 267, 277, 278, 281, 289, 299, 310, 314, 324, 325, 335, 338, 361, 378, 383, 385, 387, 389, 390, 392, 399, 405, 406, 407, 422, 427, 428, 432, 433, 437, 460, 464, 480, 520, 521, 559, 560, 581, 582, 583, 590, 593, 614, 619, 626, 631, 635, 653, 656, 660, 661, 662, 676, 679, 683, 691, 698, 699, 703, 711, 717, 718, 735, 737, 748, 760, 782, 786, 796, 862, 868, 886, 887, 890, 891, 907, 913, 924, 925, 926, 927, 943, 949, 967, 968, 971, 972, 988, 994, 1006, 1007, 1008, 1009, 1037, 1039, 1040, 1042, 1043, 1071, 1094, 1100, 1116, 1119, 1120, 1123, 1130, 1131, 1132, 1133, 1135, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1185, 1192, 1193, 1206, 1214, 1217, 1218, 1219, 1272, 1287, 1288, 1295, 1296, 1297, 1323, 1326, 1328, 1337, 1345, 1348, 1349, 1350, 1390, 1394, 1395, 1397, 1398, 1399, 1401, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426], "express": [8, 92, 110, 184, 315, 329, 330, 383, 384, 618, 619, 873, 916, 955, 998, 1199, 1287, 1326], "than": [8, 11, 34, 43, 55, 97, 99, 101, 102, 103, 115, 128, 142, 143, 144, 161, 199, 214, 215, 216, 218, 219, 221, 227, 231, 235, 241, 256, 277, 278, 281, 288, 289, 297, 298, 299, 304, 306, 307, 310, 311, 315, 316, 321, 324, 325, 326, 328, 329, 330, 341, 352, 358, 361, 374, 380, 381, 383, 384, 385, 387, 389, 390, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 425, 426, 429, 435, 464, 468, 469, 500, 527, 537, 559, 560, 581, 582, 583, 590, 625, 626, 635, 636, 652, 653, 656, 658, 659, 673, 676, 678, 679, 681, 683, 686, 690, 692, 693, 694, 698, 699, 711, 731, 735, 737, 748, 752, 761, 786, 887, 925, 947, 968, 992, 1007, 1038, 1042, 1043, 1060, 1102, 1135, 1146, 1154, 1162, 1165, 1167, 1172, 1174, 1185, 1187, 1194, 1198, 1226, 1230, 1231, 1236, 1237, 1238, 1239, 1275, 1276, 1296, 1297, 1326, 1328, 1345, 1348, 1349, 1350, 1353, 1354, 1358, 1365, 1366, 1379, 1382, 1395, 1402, 1404, 1405, 1408, 1413, 1423, 1425], "worst": [8, 210, 211, 212, 221, 228, 235, 264, 293, 294, 338, 345, 346, 347, 440, 513, 515, 516, 517, 518], "reus": [8, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 1131, 1132, 1138, 1139, 1140, 1141, 1142, 1328, 1402], "subcircuit": 8, "multipl": [8, 11, 25, 40, 45, 77, 93, 94, 99, 103, 107, 109, 143, 157, 158, 166, 175, 188, 195, 207, 287, 311, 357, 385, 386, 423, 443, 447, 458, 460, 464, 485, 486, 487, 594, 595, 597, 615, 616, 641, 643, 678, 690, 691, 697, 705, 738, 762, 786, 796, 856, 857, 863, 869, 877, 884, 892, 901, 902, 908, 923, 928, 937, 938, 944, 946, 950, 959, 960, 962, 963, 965, 973, 982, 983, 989, 991, 1002, 1003, 1005, 1010, 1037, 1039, 1040, 1045, 1046, 1102, 1103, 1105, 1127, 1135, 1137, 1216, 1217, 1219, 1285, 1291, 1296, 1298, 1326, 1352, 1378, 1393, 1405, 1406, 1412, 1413, 1417, 1425, 1426], "wherea": [8, 103, 682, 762, 786, 791, 1165, 1417], "cannot": [8, 101, 103, 127, 132, 199, 232, 300, 362, 394, 476, 581, 582, 583, 584, 632, 722, 887, 925, 934, 968, 979, 1007, 1043, 1165, 1208, 1209, 1296, 1298, 1302, 1303, 1326, 1345, 1347, 1348, 1349, 1350], "subformula": 8, "onc": [8, 38, 54, 55, 88, 93, 94, 99, 100, 112, 127, 199, 227, 230, 231, 232, 246, 247, 360, 374, 380, 388, 422, 423, 428, 488, 491, 492, 581, 582, 583, 652, 678, 679, 717, 718, 887, 925, 968, 1007, 1046, 1066, 1087, 1217, 1311, 1326, 1403, 1407], "thu": [8, 88, 101, 103, 115, 215, 216, 220, 256, 258, 331, 418, 419, 427, 428, 462, 477, 500, 512, 583, 679, 698, 699, 760, 762, 796, 1037, 1039, 1040, 1043, 1087, 1112, 1148, 1215, 1217, 1234, 1278, 1279, 1296, 1328, 1402, 1405, 1407], "wai": [8, 27, 52, 53, 55, 75, 86, 88, 93, 97, 99, 100, 101, 102, 103, 104, 107, 110, 115, 132, 152, 157, 158, 165, 184, 226, 281, 297, 298, 315, 330, 337, 356, 588, 598, 615, 618, 678, 691, 730, 760, 791, 796, 854, 856, 857, 862, 873, 899, 901, 902, 907, 915, 916, 935, 937, 938, 943, 955, 980, 982, 983, 988, 996, 998, 1037, 1039, 1040, 1041, 1097, 1165, 1213, 1215, 1217, 1239, 1262, 1269, 1272, 1326, 1328, 1330, 1393, 1394, 1404, 1406, 1411, 1426], "infeas": [8, 422], "circuit_to_formula": 8, "dag_to_branch": [8, 758, 1408], "transfer": [8, 202, 204, 230, 231, 469, 890, 891, 926, 927, 971, 972, 1008, 1009, 1420], "oper": [8, 30, 52, 95, 101, 112, 115, 168, 184, 189, 227, 374, 423, 460, 546, 547, 548, 552, 553, 554, 577, 595, 598, 601, 671, 672, 673, 674, 679, 680, 758, 786, 865, 873, 878, 910, 916, 946, 955, 960, 991, 998, 1036, 1068, 1088, 1103, 1164, 1218, 1219, 1295, 1302, 1319, 1323, 1325, 1326, 1393, 1394, 1400, 1404, 1405, 1406, 1407, 1408, 1411, 1412, 1413, 1414, 1417], "variabl": [8, 94, 132, 373, 530, 540, 618, 619, 732, 796, 1037, 1038, 1039, 1040, 1154, 1165, 1326, 1408, 1412, 1413, 1414, 1420], "formula_to_str": 8, "_to_str": 8, "root": [8, 67, 84, 293, 294, 338, 387, 389, 390, 394, 449, 460, 559, 577, 609, 671, 673, 678, 704, 728, 730, 739, 760, 791, 1119, 1120, 1125, 1126, 1145, 1147, 1235, 1271, 1272, 1323, 1365, 1366, 1393, 1406, 1407, 1408, 1412, 1413, 1423, 1425], "children": [8, 460, 577, 1145, 1155, 1272, 1365, 1366], "otherwis": [8, 92, 110, 146, 149, 171, 178, 184, 185, 198, 217, 230, 249, 250, 284, 297, 298, 303, 306, 307, 311, 315, 316, 322, 323, 324, 325, 326, 329, 330, 343, 353, 358, 393, 394, 395, 396, 397, 398, 410, 411, 412, 418, 419, 422, 425, 426, 462, 463, 464, 470, 479, 488, 490, 494, 495, 496, 498, 499, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 521, 555, 562, 563, 568, 572, 574, 584, 586, 588, 597, 601, 616, 618, 619, 633, 663, 673, 687, 688, 689, 696, 698, 699, 734, 735, 736, 737, 751, 848, 867, 873, 874, 886, 893, 912, 916, 917, 924, 929, 934, 948, 955, 956, 967, 974, 979, 993, 998, 999, 1006, 1068, 1091, 1135, 1137, 1165, 1185, 1197, 1217, 1270, 1282, 1283, 1284, 1307, 1309, 1312, 1342, 1356, 1357, 1376, 1409, 1413, 1426], "child": [8, 1147, 1272], "must": [8, 11, 93, 94, 95, 99, 100, 103, 110, 151, 152, 158, 161, 171, 204, 206, 207, 214, 215, 216, 219, 230, 231, 232, 252, 253, 257, 258, 259, 260, 261, 262, 264, 267, 268, 269, 271, 273, 276, 281, 285, 297, 298, 306, 307, 315, 316, 317, 318, 319, 324, 325, 327, 329, 330, 342, 361, 362, 363, 378, 382, 385, 391, 410, 411, 412, 413, 425, 429, 440, 471, 472, 473, 474, 475, 545, 546, 547, 548, 549, 550, 551, 553, 555, 556, 557, 558, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 577, 578, 579, 580, 584, 585, 586, 587, 588, 589, 593, 597, 599, 601, 602, 603, 604, 615, 626, 627, 632, 633, 635, 636, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 671, 672, 673, 674, 680, 690, 692, 698, 699, 707, 721, 734, 735, 736, 737, 789, 796, 853, 854, 857, 867, 891, 892, 898, 899, 902, 912, 928, 934, 938, 972, 973, 979, 983, 1010, 1037, 1038, 1039, 1040, 1063, 1071, 1085, 1102, 1133, 1137, 1146, 1162, 1165, 1173, 1176, 1186, 1188, 1190, 1193, 1197, 1199, 1209, 1213, 1217, 1219, 1235, 1239, 1240, 1270, 1275, 1276, 1277, 1278, 1279, 1295, 1296, 1298, 1307, 1309, 1310, 1311, 1312, 1315, 1333, 1337, 1338, 1339, 1340, 1359, 1361, 1362, 1363, 1364, 1365, 1366, 1376, 1393, 1394, 1395, 1407, 1426], "NOT": [8, 110, 199, 549, 550, 551, 748, 887, 925, 968, 1007], "util": [8, 14, 36, 44, 45, 93, 97, 102, 103, 229, 230, 231, 316, 374, 423, 425, 426, 429, 460, 496, 678, 679, 758, 1044, 1242, 1299, 1301, 1303, 1310, 1319, 1320, 1321, 1325, 1402, 1406, 1407, 1411, 1413, 1416, 1419], "arbitrary_el": [8, 1392, 1413], "nb": [8, 1331, 1334], "left": [8, 71, 115, 183, 311, 312, 322, 324, 325, 385, 559, 560, 584, 616, 688, 689, 739, 1106, 1134, 1136, 1146, 1179, 1206, 1280, 1355, 1358, 1404], "right": [8, 71, 110, 111, 115, 152, 206, 322, 385, 427, 428, 500, 559, 560, 584, 585, 587, 588, 615, 616, 688, 689, 739, 854, 935, 980, 1134, 1136, 1146, 1155, 1157, 1179, 1206, 1213, 1215, 1270, 1280], "littl": [8, 94, 298, 307], "mislead": 8, "That": [8, 97, 132, 165, 212, 221, 227, 295, 385, 436, 465, 525, 535, 555, 588, 657, 671, 672, 673, 674, 691, 704, 717, 791, 862, 907, 943, 988, 1046, 1162, 1210, 1296, 1388, 1404, 1409], "okai": 8, "becaus": [8, 11, 54, 69, 94, 99, 101, 102, 103, 112, 132, 161, 215, 216, 220, 255, 311, 378, 387, 389, 390, 394, 411, 412, 427, 494, 498, 499, 500, 510, 569, 585, 587, 615, 616, 632, 652, 934, 979, 1038, 1236, 1273, 1296, 1303, 1326, 1345, 1350, 1404, 1407, 1416], "AND": [8, 110, 598, 748, 762], "OR": [8, 110, 157, 175, 188, 856, 869, 877, 901, 937, 947, 950, 959, 982, 992], "symmetr": [8, 145, 148, 237, 545, 586, 593, 761, 1173, 1192, 1235, 1246, 1250, 1251, 1256, 1258, 1269, 1320, 1321, 1387], "It": [8, 52, 56, 58, 92, 93, 94, 97, 99, 101, 102, 104, 107, 110, 112, 115, 132, 172, 184, 207, 214, 215, 216, 229, 230, 231, 249, 260, 261, 262, 264, 278, 310, 316, 324, 325, 326, 343, 346, 347, 351, 353, 412, 414, 415, 416, 417, 418, 419, 429, 438, 440, 452, 457, 464, 480, 496, 500, 508, 530, 540, 545, 559, 560, 565, 566, 567, 582, 588, 594, 595, 598, 600, 601, 615, 619, 628, 629, 630, 652, 658, 659, 663, 671, 674, 692, 717, 718, 719, 760, 761, 762, 791, 796, 868, 873, 892, 913, 916, 928, 949, 955, 973, 994, 998, 1010, 1012, 1013, 1018, 1037, 1038, 1039, 1040, 1054, 1117, 1170, 1174, 1200, 1201, 1206, 1207, 1210, 1217, 1223, 1227, 1234, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1253, 1254, 1258, 1261, 1263, 1264, 1269, 1275, 1276, 1277, 1280, 1296, 1297, 1323, 1324, 1326, 1328, 1343, 1382, 1383, 1393, 1395, 1398, 1402, 1404, 1407, 1408, 1409, 1411, 1412, 1413, 1426], "just": [8, 99, 102, 103, 104, 184, 199, 338, 374, 439, 464, 559, 560, 577, 660, 661, 662, 692, 791, 873, 887, 916, 925, 946, 955, 960, 968, 991, 998, 1007, 1120, 1126, 1229, 1278, 1279, 1296, 1328, 1393, 1404, 1406], "operand": 8, "predict": [8, 567, 568, 569, 570, 571, 572, 573, 574, 591, 592, 758, 1325, 1402, 1406, 1412], "henc": [8, 168, 189, 521, 865, 878, 910, 946, 960, 991, 1059, 1202, 1383], "doe": [8, 77, 93, 94, 99, 101, 102, 103, 104, 114, 115, 132, 147, 153, 154, 165, 168, 189, 207, 208, 227, 228, 229, 230, 231, 232, 293, 308, 339, 340, 342, 343, 352, 357, 373, 382, 385, 410, 414, 426, 450, 469, 494, 495, 496, 497, 498, 499, 500, 502, 503, 506, 507, 509, 510, 511, 512, 534, 544, 549, 550, 551, 564, 566, 583, 584, 586, 589, 601, 612, 626, 627, 678, 691, 693, 694, 698, 699, 717, 718, 721, 722, 723, 724, 725, 726, 762, 862, 865, 878, 892, 907, 910, 928, 943, 946, 960, 973, 988, 991, 1010, 1038, 1043, 1066, 1070, 1072, 1081, 1102, 1103, 1105, 1106, 1107, 1109, 1114, 1173, 1175, 1177, 1192, 1207, 1222, 1223, 1227, 1229, 1234, 1241, 1296, 1300, 1303, 1326, 1333, 1334, 1341, 1342, 1344, 1351, 1353, 1354, 1355, 1356, 1357, 1358, 1371, 1379, 1380, 1381, 1383, 1393, 1404, 1405, 1406, 1410, 1417, 1426], "necessarili": [8, 99, 341, 451, 483, 559, 560, 641, 643, 1038, 1219], "behav": [8, 88, 103, 159, 190, 200, 220, 351, 858, 879, 888, 903, 939, 969, 984, 1229, 1296, 1395, 1404], "everi": [8, 11, 57, 88, 93, 109, 112, 120, 144, 157, 161, 177, 211, 212, 220, 221, 229, 230, 231, 235, 243, 264, 287, 295, 300, 324, 325, 343, 352, 380, 397, 437, 439, 440, 450, 462, 471, 472, 473, 474, 475, 477, 483, 484, 491, 512, 516, 565, 606, 614, 615, 619, 632, 633, 635, 636, 663, 685, 687, 688, 717, 718, 791, 856, 901, 937, 982, 1052, 1053, 1054, 1070, 1071, 1072, 1085, 1086, 1102, 1103, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1114, 1115, 1116, 1117, 1148, 1162, 1195, 1216, 1217, 1257, 1264, 1278, 1279, 1296, 1407], "left_subformula": 8, "right_subformula": 8, "in_degre": [8, 166, 188, 491, 678, 863, 877, 944, 959, 1177, 1207, 1208, 1404, 1406, 1407, 1426], "ha": [8, 11, 16, 44, 67, 88, 91, 93, 94, 95, 97, 99, 100, 101, 102, 103, 105, 107, 110, 112, 116, 120, 127, 152, 161, 165, 166, 173, 174, 175, 184, 188, 198, 207, 212, 214, 215, 219, 220, 226, 227, 229, 230, 231, 232, 235, 238, 239, 240, 241, 242, 243, 244, 247, 249, 252, 269, 271, 272, 273, 274, 275, 276, 282, 289, 291, 293, 294, 295, 300, 305, 310, 324, 331, 343, 352, 355, 356, 363, 364, 365, 373, 378, 380, 381, 383, 384, 385, 386, 391, 393, 394, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 424, 427, 428, 429, 439, 450, 458, 460, 466, 467, 468, 471, 472, 473, 474, 475, 476, 477, 480, 491, 492, 493, 494, 495, 496, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 522, 564, 566, 577, 578, 581, 590, 593, 605, 607, 610, 611, 622, 623, 624, 628, 629, 630, 632, 633, 634, 635, 636, 638, 646, 647, 649, 652, 657, 658, 682, 688, 690, 692, 697, 711, 717, 718, 729, 730, 731, 739, 749, 786, 791, 854, 862, 863, 869, 873, 877, 886, 892, 899, 907, 908, 916, 924, 928, 935, 943, 944, 948, 950, 955, 959, 967, 973, 980, 988, 989, 993, 998, 1006, 1010, 1040, 1043, 1045, 1066, 1068, 1070, 1072, 1075, 1080, 1084, 1098, 1099, 1101, 1102, 1103, 1105, 1122, 1130, 1145, 1154, 1160, 1162, 1165, 1176, 1180, 1185, 1193, 1195, 1196, 1197, 1198, 1199, 1207, 1210, 1211, 1215, 1217, 1222, 1234, 1239, 1243, 1244, 1248, 1249, 1254, 1259, 1261, 1264, 1267, 1269, 1270, 1272, 1275, 1276, 1277, 1278, 1279, 1281, 1282, 1283, 1284, 1285, 1286, 1289, 1291, 1293, 1296, 1300, 1326, 1328, 1330, 1333, 1334, 1353, 1354, 1371, 1372, 1379, 1382, 1393, 1394, 1395, 1398, 1403, 1404, 1405, 1406, 1407, 1409, 1413, 1414, 1416, 1423, 1425], "output": [8, 13, 16, 89, 93, 101, 102, 103, 109, 197, 287, 288, 345, 374, 380, 494, 498, 499, 509, 510, 575, 588, 677, 678, 691, 722, 1045, 1193, 1197, 1199, 1269, 1296, 1326, 1334, 1341, 1344, 1355, 1358, 1399, 1402, 1404, 1406, 1411, 1413, 1414, 1426], "two": [8, 11, 16, 27, 34, 38, 43, 54, 55, 57, 58, 65, 67, 71, 88, 93, 95, 99, 100, 102, 109, 112, 114, 115, 120, 132, 151, 171, 175, 184, 185, 188, 202, 207, 211, 212, 213, 214, 215, 216, 217, 220, 221, 226, 227, 230, 231, 232, 245, 249, 251, 252, 253, 257, 258, 260, 261, 262, 265, 269, 270, 271, 272, 273, 274, 275, 276, 282, 285, 286, 287, 289, 305, 311, 315, 316, 322, 326, 329, 330, 337, 341, 343, 345, 351, 352, 358, 359, 377, 380, 381, 383, 391, 411, 412, 419, 423, 428, 429, 430, 431, 442, 443, 444, 445, 447, 452, 453, 454, 457, 462, 471, 472, 473, 474, 475, 476, 480, 491, 494, 498, 499, 500, 502, 503, 506, 508, 509, 510, 511, 521, 545, 549, 550, 551, 555, 559, 560, 561, 562, 563, 564, 565, 566, 568, 569, 572, 574, 578, 584, 585, 586, 587, 588, 593, 598, 605, 607, 608, 610, 611, 615, 619, 626, 627, 629, 632, 633, 635, 636, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 671, 672, 673, 674, 675, 676, 680, 692, 694, 731, 732, 738, 739, 760, 761, 762, 780, 786, 791, 796, 853, 867, 869, 873, 874, 877, 890, 892, 898, 912, 916, 917, 926, 928, 934, 946, 948, 950, 955, 956, 959, 960, 971, 973, 979, 991, 993, 998, 999, 1008, 1010, 1019, 1020, 1021, 1022, 1036, 1037, 1039, 1040, 1056, 1084, 1088, 1098, 1100, 1101, 1106, 1107, 1108, 1109, 1114, 1116, 1134, 1146, 1147, 1149, 1151, 1152, 1156, 1174, 1185, 1186, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1204, 1207, 1210, 1211, 1215, 1217, 1218, 1243, 1244, 1253, 1271, 1272, 1275, 1276, 1294, 1295, 1296, 1323, 1324, 1326, 1328, 1359, 1360, 1363, 1393, 1394, 1395, 1397, 1402, 1404, 1405, 1406, 1407, 1410, 1411, 1413, 1425], "layer": [8, 36, 55, 61, 67, 103, 438, 705, 1038, 1109, 1420], "third": [8, 102, 114, 249, 422, 467, 585, 587, 734, 736, 1217, 1226, 1262, 1263, 1326, 1407], "appear": [8, 83, 93, 95, 99, 100, 102, 179, 204, 230, 231, 238, 243, 246, 247, 277, 363, 364, 365, 378, 451, 452, 453, 455, 466, 470, 584, 585, 587, 588, 675, 679, 707, 730, 734, 736, 891, 972, 1036, 1088, 1102, 1136, 1150, 1152, 1154, 1157, 1159, 1187, 1188, 1277, 1282, 1323, 1324, 1345, 1348, 1349, 1350, 1382, 1407, 1413, 1414], "both": [8, 52, 55, 92, 93, 94, 100, 101, 102, 103, 115, 161, 164, 204, 214, 215, 216, 217, 240, 257, 258, 259, 264, 282, 286, 287, 289, 337, 358, 379, 383, 415, 417, 418, 419, 423, 427, 440, 470, 502, 506, 545, 575, 581, 598, 600, 601, 602, 603, 604, 605, 606, 607, 610, 611, 615, 621, 635, 636, 653, 654, 655, 676, 711, 720, 760, 761, 762, 782, 891, 972, 1020, 1036, 1066, 1075, 1080, 1084, 1088, 1097, 1120, 1126, 1144, 1165, 1189, 1192, 1199, 1207, 1210, 1211, 1213, 1215, 1282, 1296, 1326, 1328, 1358, 1363, 1364, 1387, 1393, 1395, 1402, 1413, 1416, 1417, 1425, 1426], "negat": 8, "sole": [8, 786, 1278, 1279, 1326], "fourth": [8, 230, 231, 1326, 1404], "digraph": [8, 10, 11, 16, 21, 25, 41, 45, 56, 61, 67, 69, 70, 82, 88, 101, 102, 115, 132, 151, 152, 156, 157, 158, 160, 162, 163, 165, 166, 168, 170, 171, 172, 175, 176, 185, 186, 187, 188, 189, 192, 193, 194, 195, 196, 198, 199, 202, 204, 207, 208, 216, 227, 229, 230, 231, 240, 246, 247, 299, 308, 314, 318, 319, 321, 327, 328, 334, 335, 336, 337, 339, 340, 342, 343, 388, 391, 393, 396, 397, 398, 399, 401, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 430, 431, 437, 450, 452, 453, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 481, 482, 492, 494, 495, 496, 497, 498, 499, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 513, 514, 518, 519, 523, 555, 566, 575, 576, 577, 588, 590, 613, 615, 623, 630, 636, 643, 644, 652, 656, 657, 658, 659, 663, 678, 688, 690, 693, 696, 697, 698, 699, 700, 701, 702, 706, 707, 708, 709, 711, 716, 717, 718, 719, 721, 722, 723, 724, 725, 726, 740, 741, 744, 745, 746, 747, 748, 749, 750, 752, 760, 789, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 904, 905, 906, 907, 908, 911, 912, 913, 915, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 935, 936, 937, 938, 940, 941, 942, 943, 949, 957, 958, 963, 964, 965, 966, 967, 968, 972, 973, 974, 975, 977, 978, 980, 981, 982, 983, 985, 986, 987, 988, 989, 994, 996, 1000, 1001, 1003, 1004, 1005, 1006, 1007, 1010, 1035, 1037, 1038, 1039, 1040, 1041, 1052, 1062, 1066, 1070, 1072, 1075, 1080, 1083, 1084, 1098, 1099, 1101, 1118, 1135, 1150, 1154, 1168, 1169, 1170, 1173, 1177, 1178, 1180, 1182, 1183, 1184, 1185, 1189, 1217, 1270, 1272, 1273, 1274, 1283, 1284, 1287, 1290, 1292, 1298, 1323, 1326, 1333, 1337, 1342, 1356, 1357, 1362, 1365, 1366, 1371, 1393, 1399, 1401, 1402, 1404, 1405, 1406, 1407, 1408, 1409, 1411, 1412, 1413, 1414, 1416, 1417, 1424, 1425, 1426], "add_nod": [8, 11, 26, 34, 69, 74, 89, 102, 157, 184, 246, 339, 340, 398, 422, 491, 492, 496, 504, 505, 508, 522, 523, 605, 607, 610, 611, 691, 796, 856, 873, 901, 916, 937, 955, 982, 998, 1037, 1039, 1040, 1086, 1275, 1326, 1345, 1407, 1408, 1417, 1426], "get_node_attribut": [8, 39, 44, 71, 1213, 1404], "600": [8, 10, 12], "font_siz": [8, 16, 21, 25, 32, 35, 38, 45, 46, 1133, 1134, 1136], "22": [8, 35, 64, 66, 383, 384, 1271, 1323, 1403, 1408, 1412, 1422], "multipartite_layout": [8, 36, 61, 67, 1412, 1414, 1420], "subset_kei": [8, 36, 61, 67, 1109], "equal": [8, 36, 81, 144, 214, 215, 216, 230, 231, 238, 269, 271, 273, 276, 288, 297, 298, 300, 303, 306, 307, 310, 311, 312, 315, 316, 320, 323, 324, 325, 329, 330, 331, 373, 410, 411, 412, 413, 418, 419, 428, 471, 474, 476, 491, 494, 495, 496, 498, 499, 502, 503, 504, 505, 506, 507, 508, 509, 510, 525, 535, 545, 552, 553, 554, 555, 568, 572, 605, 623, 657, 671, 672, 673, 674, 687, 688, 689, 690, 721, 722, 740, 741, 753, 761, 791, 1112, 1116, 1162, 1165, 1198, 1204, 1230, 1239, 1271, 1280, 1291, 1307, 1309, 1312, 1398, 1399], "159": [8, 17, 555, 762], "plot_circuit": [8, 17], "southern": [9, 1265], "women": [9, 1265, 1398, 1406], "unipartit": [9, 115, 258, 259, 358], "properti": [9, 11, 18, 22, 33, 63, 86, 101, 102, 103, 112, 134, 159, 161, 166, 168, 175, 176, 179, 184, 188, 189, 190, 200, 284, 285, 286, 287, 288, 363, 364, 365, 388, 476, 500, 545, 569, 619, 685, 858, 863, 865, 869, 870, 873, 877, 878, 879, 888, 903, 908, 910, 916, 939, 944, 946, 950, 951, 955, 959, 960, 969, 984, 989, 991, 998, 1085, 1086, 1122, 1134, 1136, 1193, 1202, 1217, 1219, 1269, 1283, 1284, 1326, 1328, 1383, 1398, 1405, 1406, 1407, 1408, 1413, 1417, 1426], "These": [9, 52, 58, 73, 79, 86, 93, 94, 105, 112, 336, 385, 494, 512, 559, 671, 673, 732, 748, 779, 786, 1038, 1045, 1047, 1323, 1326, 1385, 1387, 1392, 1394, 1395, 1397, 1399, 1404, 1405, 1411, 1426], "were": [9, 65, 88, 99, 101, 104, 215, 216, 220, 289, 305, 410, 437, 460, 588, 962, 1002, 1199, 1393, 1395, 1399, 1402, 1405, 1406, 1407, 1413, 1416], "et": [9, 210, 226, 227, 315, 316, 322, 330, 334, 337, 345, 352, 358, 373, 380, 381, 423, 425, 426, 451, 569, 591, 592, 681, 682, 684, 693, 1202], "al": [9, 210, 226, 227, 315, 316, 322, 330, 334, 337, 345, 352, 358, 373, 380, 381, 423, 425, 426, 451, 569, 591, 592, 681, 682, 684, 693, 1202, 1407, 1413], "1930": [9, 1396], "thei": [9, 54, 58, 65, 71, 92, 93, 94, 97, 99, 100, 101, 102, 103, 104, 105, 107, 112, 132, 151, 165, 207, 213, 220, 249, 285, 287, 288, 296, 297, 298, 301, 302, 306, 307, 308, 309, 351, 362, 374, 391, 396, 427, 451, 452, 453, 454, 464, 465, 471, 472, 473, 474, 475, 496, 504, 505, 508, 512, 546, 547, 548, 559, 560, 576, 583, 586, 588, 600, 604, 675, 676, 704, 717, 750, 760, 786, 853, 862, 892, 898, 907, 928, 934, 943, 962, 973, 979, 988, 1002, 1010, 1036, 1038, 1066, 1085, 1088, 1109, 1120, 1126, 1133, 1135, 1137, 1151, 1159, 1165, 1193, 1197, 1198, 1217, 1271, 1272, 1323, 1328, 1353, 1354, 1356, 1357, 1359, 1363, 1394, 1396, 1402, 1404, 1406, 1409, 1414, 1426], "repres": [9, 11, 26, 43, 52, 54, 57, 67, 92, 99, 107, 115, 230, 231, 265, 281, 283, 286, 287, 288, 291, 292, 338, 350, 361, 362, 363, 377, 378, 380, 381, 382, 385, 386, 391, 448, 452, 453, 455, 457, 460, 465, 466, 494, 495, 498, 499, 500, 502, 503, 506, 507, 509, 510, 521, 565, 577, 578, 579, 580, 586, 588, 609, 615, 618, 619, 656, 660, 664, 667, 676, 679, 691, 692, 695, 697, 698, 699, 700, 702, 728, 730, 731, 734, 736, 739, 752, 786, 791, 796, 1019, 1020, 1021, 1022, 1037, 1038, 1039, 1040, 1045, 1081, 1102, 1140, 1151, 1185, 1193, 1194, 1196, 1197, 1198, 1199, 1209, 1217, 1240, 1243, 1246, 1250, 1258, 1267, 1269, 1272, 1273, 1278, 1279, 1323, 1324, 1326, 1329, 1330, 1346, 1347, 1388, 1393, 1406], "observ": [9, 13, 132, 223, 1414, 1426], "attend": 9, "14": [9, 11, 16, 19, 25, 38, 44, 64, 66, 71, 229, 230, 231, 383, 384, 405, 406, 501, 619, 690, 1150, 1242, 1250, 1262, 1406, 1408, 1426], "event": [9, 25, 99, 100, 110, 1165, 1229, 1300], "18": [9, 44, 64, 66, 93, 324, 325, 345, 383, 384, 618, 1169, 1249, 1255, 1258, 1260, 1263, 1269, 1393, 1406, 1416, 1417, 1421, 1426], "bipartit": [9, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 350, 351, 358, 377, 439, 440, 443, 581, 588, 758, 1043, 1106, 1151, 1203, 1204, 1205, 1265, 1325, 1395, 1398, 1399, 1400, 1401, 1406, 1407, 1411, 1413, 1417, 1421, 1425], "biadjac": [9, 282, 283, 1400, 1406], "7": [9, 12, 14, 19, 25, 35, 44, 46, 63, 64, 65, 66, 68, 89, 99, 101, 102, 115, 125, 151, 158, 170, 171, 192, 207, 232, 268, 297, 299, 314, 322, 327, 332, 333, 339, 340, 342, 362, 374, 380, 391, 403, 410, 413, 414, 415, 423, 424, 425, 426, 441, 445, 446, 483, 496, 501, 508, 511, 512, 555, 581, 586, 618, 619, 630, 652, 658, 663, 671, 674, 680, 695, 703, 706, 707, 708, 730, 747, 750, 761, 796, 853, 857, 866, 867, 881, 892, 898, 902, 911, 912, 915, 920, 928, 934, 938, 947, 973, 979, 983, 992, 996, 1010, 1037, 1039, 1040, 1052, 1053, 1085, 1100, 1104, 1148, 1212, 1242, 1248, 1250, 1251, 1255, 1258, 1260, 1273, 1323, 1326, 1330, 1339, 1340, 1345, 1348, 1349, 1350, 1382, 1392, 1394, 1402, 1403, 1405, 1408, 1409, 1410, 1411, 1412, 1413, 1426], "12": [9, 11, 19, 25, 44, 50, 55, 58, 64, 65, 66, 89, 91, 93, 229, 230, 231, 265, 345, 380, 381, 392, 399, 405, 406, 407, 449, 486, 501, 516, 568, 572, 574, 606, 616, 1052, 1053, 1054, 1133, 1136, 1150, 1244, 1245, 1249, 1254, 1257, 1263, 1335, 1406, 1408, 1412, 1426], "9": [9, 11, 12, 19, 25, 35, 44, 46, 63, 64, 65, 66, 68, 82, 89, 101, 102, 111, 115, 125, 232, 293, 295, 339, 340, 342, 346, 347, 356, 374, 380, 405, 406, 424, 438, 449, 494, 496, 501, 504, 505, 508, 545, 566, 581, 586, 676, 706, 707, 708, 761, 1100, 1104, 1148, 1150, 1194, 1199, 1212, 1217, 1235, 1246, 1255, 1267, 1273, 1283, 1284, 1323, 1326, 1328, 1396, 1403, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426], "11": [9, 25, 33, 44, 64, 65, 66, 68, 89, 102, 110, 115, 157, 210, 239, 240, 297, 298, 303, 306, 307, 323, 392, 399, 405, 406, 407, 413, 415, 417, 422, 501, 514, 517, 606, 618, 680, 721, 738, 856, 901, 937, 982, 1052, 1053, 1054, 1100, 1150, 1287, 1403, 1410, 1413, 1414, 1419, 1424, 1425, 1426], "13": [9, 11, 38, 44, 64, 66, 89, 91, 156, 229, 230, 231, 343, 501, 703, 855, 900, 936, 981, 1150, 1192, 1406, 1420, 1426], "16": [9, 19, 31, 44, 45, 64, 66, 70, 229, 230, 231, 346, 347, 387, 389, 390, 394, 453, 508, 511, 512, 519, 571, 592, 606, 748, 749, 750, 1109, 1205, 1256, 1271, 1286, 1323, 1406, 1411, 1426], "17": [9, 21, 44, 64, 66, 103, 229, 230, 231, 297, 508, 680, 693, 1405, 1406, 1426], "friend": [9, 545, 1407, 1412], "member": [9, 92, 93, 94, 100, 112, 315, 317, 318, 319, 330, 391, 483, 484, 586, 691, 1222, 1267, 1403], "evelyn": 9, "jefferson": 9, "laura": 9, "mandevil": 9, "theresa": 9, "anderson": 9, "brenda": 9, "roger": 9, "charlott": 9, "mcdowd": 9, "franc": 9, "eleanor": 9, "nye": 9, "pearl": [9, 132], "oglethorp": 9, "ruth": 9, "desand": 9, "vern": 9, "sanderson": 9, "myra": 9, "liddel": 9, "katherina": 9, "sylvia": 9, "avondal": 9, "nora": 9, "fayett": 9, "helen": 9, "lloyd": 9, "dorothi": 9, "murchison": 9, "olivia": 9, "carleton": 9, "flora": 9, "price": 9, "meet": [9, 94, 1165, 1196, 1197, 1198], "50": [9, 25, 30, 34, 40, 50, 54, 55, 56, 57, 64, 65, 272, 312, 1117, 1193, 1197, 1198, 1251, 1297, 1302], "45": [9, 58, 64, 110, 226, 300, 409, 1175], "57": [9, 64], "46": [9, 64, 235, 564, 619, 1264], "24": [9, 19, 37, 64, 66, 68, 103, 383, 384, 496, 505, 508, 703, 1212, 1229, 1244, 1262, 1271, 1403], "32": [9, 64, 66, 68, 209, 211, 212, 383, 384, 564, 703, 1403, 1411], "36": [9, 21, 64, 68, 752, 1150, 1262, 1271, 1353, 1354, 1379, 1403], "31": [9, 64, 66, 229, 230, 231, 260, 261, 262, 289, 383, 384, 409, 703, 1226, 1235, 1403], "40": [9, 50, 64, 80, 101, 297, 300, 555, 672, 1173, 1240, 1271], "38": [9, 17, 64, 688, 1271], "33": [9, 58, 64, 66, 68, 93, 383, 384, 500, 514, 703, 1267, 1271, 1403, 1414], "37": [9, 56, 64, 68, 303, 311, 312, 323, 324, 325, 496, 508, 1039, 1040, 1271, 1393, 1403, 1408, 1425], "43": [9, 64, 324, 325, 606, 1244, 1271], "34": [9, 64, 68, 331, 508, 762, 1271, 1403], "algorithm": [9, 14, 15, 44, 52, 54, 88, 93, 94, 95, 96, 102, 103, 107, 109, 110, 111, 112, 114, 115, 117, 120, 121, 122, 125, 127, 128, 132, 133, 136, 141, 151, 210, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 226, 227, 228, 229, 230, 231, 232, 235, 249, 251, 252, 253, 254, 255, 256, 258, 260, 261, 262, 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683, 686, 690, 691, 692, 693, 695, 696, 697, 698, 699, 700, 701, 702, 711, 717, 721, 722, 729, 731, 732, 734, 735, 736, 737, 738, 749, 764, 765, 768, 770, 775, 776, 780, 786, 789, 790, 791, 853, 898, 934, 979, 1011, 1038, 1042, 1043, 1105, 1106, 1107, 1109, 1114, 1116, 1117, 1125, 1126, 1155, 1165, 1168, 1169, 1177, 1178, 1179, 1180, 1181, 1185, 1186, 1187, 1188, 1193, 1195, 1200, 1201, 1202, 1205, 1207, 1209, 1210, 1216, 1223, 1224, 1226, 1227, 1228, 1230, 1231, 1232, 1234, 1235, 1239, 1260, 1269, 1275, 1276, 1277, 1298, 1302, 1319, 1320, 1321, 1323, 1325, 1328, 1367, 1368, 1386, 1393, 1394, 1395, 1400, 1401, 1402, 1403, 1406, 1407, 1408, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1419, 1422, 1424, 1425, 1426], "davis_southern_women_graph": [9, 88, 263], "top": [9, 34, 52, 67, 106, 111, 112, 115, 125, 260, 272, 284, 350, 381, 670, 675, 770, 1106, 1134, 1136, 1252, 1396, 1399, 1407, 1412, 1413, 1416], "bottom": [9, 91, 115, 260, 272, 274, 284, 285, 286, 287, 288, 350, 381, 1134, 1136, 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518, 1166, 1167], "plot_davis_club": [9, 17], "retain": [10, 102, 110, 230, 284, 285, 286, 287, 288, 1100, 1187, 1295], "pattern": [10, 54, 93, 103, 236, 241, 244, 248, 385, 494, 519, 555, 671, 672, 673, 674, 690, 691, 693, 762, 786, 1036, 1088, 1388, 1413], "add": [10, 11, 26, 34, 41, 45, 49, 52, 61, 71, 88, 89, 91, 93, 94, 101, 102, 105, 106, 115, 151, 152, 153, 154, 156, 157, 158, 164, 207, 222, 223, 229, 282, 285, 341, 374, 411, 412, 423, 428, 430, 431, 450, 460, 581, 582, 583, 589, 614, 615, 618, 619, 654, 690, 701, 717, 718, 796, 850, 853, 854, 855, 856, 857, 892, 895, 898, 899, 900, 901, 902, 928, 931, 934, 935, 936, 937, 938, 973, 976, 979, 980, 981, 982, 983, 984, 1010, 1037, 1038, 1039, 1040, 1042, 1049, 1052, 1053, 1054, 1100, 1154, 1165, 1172, 1185, 1207, 1210, 1217, 1219, 1233, 1234, 1236, 1302, 1326, 1353, 1354, 1356, 1357, 1379, 1380, 1383, 1393, 1394, 1395, 1398, 1404, 1406, 1407, 1408, 1409, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426], "compressor": [10, 690, 786], "do": [10, 55, 75, 88, 92, 93, 94, 96, 99, 101, 102, 106, 107, 109, 111, 115, 133, 165, 184, 199, 202, 204, 230, 231, 238, 243, 277, 278, 280, 362, 380, 410, 411, 412, 418, 419, 458, 459, 467, 470, 589, 598, 632, 690, 692, 734, 735, 736, 737, 791, 796, 862, 873, 887, 890, 891, 907, 916, 925, 926, 927, 943, 954, 955, 968, 971, 972, 988, 997, 998, 1007, 1008, 1009, 1037, 1038, 1039, 1040, 1061, 1082, 1102, 1165, 1177, 1189, 1193, 1207, 1210, 1216, 1217, 1227, 1272, 1328, 1393, 1401, 1402, 1407, 1411, 1426], "would": [10, 92, 93, 95, 96, 100, 101, 102, 103, 104, 105, 107, 289, 305, 414, 415, 416, 417, 422, 428, 579, 583, 588, 632, 679, 690, 693, 717, 718, 751, 1217, 1236, 1295, 1296, 1300, 1303, 1326, 1416, 1417], "result": [10, 11, 25, 71, 92, 95, 101, 103, 109, 110, 112, 142, 165, 209, 218, 220, 230, 231, 255, 269, 271, 273, 276, 283, 284, 285, 286, 287, 288, 289, 299, 300, 305, 324, 325, 330, 374, 380, 381, 382, 385, 386, 391, 411, 412, 416, 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1425, 1426], "edgecolor": [10, 15, 21, 32, 34, 35, 38, 54, 58, 82, 83, 1137], "black": [10, 15, 21, 25, 65, 69, 93, 598, 1133, 1134, 1136, 1412, 1413, 1414, 1416, 1426], "ax1": [10, 15, 27, 50, 82], "number_of_edg": [10, 15, 25, 28, 198, 690, 886, 924, 967, 1006, 1059, 1154, 1271, 1406, 1407, 1426], "nonexp_graph": 10, "compression_nod": 10, "summar": [10, 15, 100, 101, 690, 691, 758, 791, 1325, 1328, 1413], "dedensifi": [10, 758], "threshold": [10, 57, 83, 112, 220, 229, 231, 380, 381, 690, 692, 695, 696, 758, 786, 1117, 1193, 1194, 1196, 1197, 1198, 1325, 1398, 1406, 1407, 1408, 1412, 1414], "copi": [10, 16, 38, 44, 93, 95, 106, 167, 196, 199, 202, 203, 204, 205, 284, 285, 286, 287, 288, 341, 388, 390, 392, 406, 433, 434, 435, 436, 437, 453, 460, 469, 521, 584, 585, 587, 596, 599, 602, 603, 605, 606, 607, 610, 611, 613, 614, 633, 636, 690, 864, 885, 887, 890, 891, 909, 925, 926, 927, 945, 963, 966, 968, 971, 972, 990, 1003, 1007, 1008, 1009, 1035, 1038, 1057, 1061, 1063, 1066, 1082, 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672, 673, 674, 675, 676, 678, 679, 680, 681, 682, 683, 684, 686, 690, 691, 692, 694, 695, 696, 697, 701, 703, 704, 705, 706, 707, 708, 716, 717, 719, 721, 722, 723, 724, 725, 726, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 743, 748, 752, 760, 761, 762, 767, 775, 782, 791, 796, 801, 806, 810, 814, 818, 822, 827, 832, 837, 842, 847, 849, 850, 851, 853, 854, 856, 857, 859, 862, 863, 864, 865, 866, 869, 871, 872, 873, 877, 878, 880, 881, 882, 883, 884, 886, 887, 889, 891, 892, 894, 895, 896, 898, 899, 901, 902, 904, 907, 908, 909, 910, 911, 914, 915, 916, 920, 921, 922, 923, 924, 925, 927, 928, 930, 931, 932, 934, 935, 937, 938, 940, 943, 944, 945, 946, 947, 948, 950, 952, 953, 954, 955, 959, 960, 961, 962, 963, 964, 965, 967, 968, 970, 972, 973, 975, 976, 977, 979, 980, 982, 983, 985, 988, 989, 990, 991, 992, 993, 995, 996, 997, 998, 1002, 1003, 1004, 1005, 1006, 1007, 1009, 1010, 1012, 1013, 1018, 1020, 1036, 1037, 1038, 1039, 1040, 1042, 1043, 1045, 1046, 1049, 1050, 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52, 76, 92, 93, 94, 96, 97, 98, 102, 104, 180, 222, 226, 232, 274, 331, 338, 373, 383, 405, 406, 440, 455, 464, 465, 466, 592, 624, 691, 760, 786, 871, 914, 952, 995, 1045, 1100, 1175, 1177, 1180, 1216, 1219, 1222, 1225, 1245, 1280, 1290, 1295, 1296, 1299, 1301, 1383, 1395, 1407, 1408, 1412, 1413, 1414, 1419, 1426], "follow": [11, 25, 44, 49, 52, 53, 65, 67, 83, 86, 91, 92, 93, 94, 95, 97, 99, 100, 101, 102, 103, 108, 110, 111, 128, 132, 151, 161, 171, 183, 207, 213, 227, 229, 230, 231, 243, 280, 305, 338, 343, 351, 362, 373, 378, 380, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 440, 452, 453, 465, 466, 496, 502, 503, 504, 505, 506, 507, 508, 588, 598, 599, 602, 615, 636, 679, 748, 750, 760, 762, 791, 853, 867, 892, 898, 912, 928, 934, 948, 973, 979, 993, 1010, 1102, 1103, 1105, 1144, 1165, 1175, 1179, 1185, 1188, 1200, 1201, 1209, 1219, 1225, 1233, 1234, 1241, 1251, 1260, 1274, 1275, 1276, 1277, 1281, 1296, 1315, 1323, 1326, 1328, 1329, 1388, 1393, 1395, 1399, 1404, 1406, 1407, 1409, 1411, 1412, 1413, 1425, 1426], "given": [11, 38, 44, 62, 64, 67, 91, 99, 101, 103, 112, 116, 141, 142, 144, 152, 158, 193, 197, 208, 211, 212, 227, 229, 235, 236, 248, 249, 260, 264, 266, 269, 271, 273, 274, 276, 279, 281, 283, 284, 285, 286, 287, 288, 320, 329, 331, 338, 344, 351, 353, 357, 362, 363, 364, 365, 373, 378, 380, 381, 385, 439, 454, 455, 460, 462, 470, 477, 478, 480, 497, 511, 512, 513, 559, 560, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 576, 578, 579, 580, 588, 589, 590, 614, 615, 616, 622, 623, 659, 660, 661, 662, 676, 677, 678, 679, 681, 683, 684, 686, 690, 691, 693, 697, 698, 699, 700, 702, 703, 704, 706, 707, 708, 709, 728, 729, 730, 731, 732, 739, 748, 753, 761, 782, 786, 854, 857, 882, 899, 902, 921, 935, 938, 963, 980, 983, 1003, 1046, 1085, 1086, 1094, 1101, 1102, 1135, 1144, 1151, 1162, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1189, 1199, 1200, 1201, 1206, 1207, 1208, 1209, 1210, 1221, 1222, 1240, 1269, 1273, 1274, 1276, 1295, 1300, 1302, 1315, 1323, 1353, 1354, 1379, 1380, 1394, 1395, 1406], "digit": [11, 70, 99], "base": [11, 15, 38, 43, 55, 58, 69, 93, 94, 100, 101, 102, 103, 107, 128, 132, 199, 203, 205, 212, 216, 220, 229, 296, 297, 301, 302, 303, 308, 309, 310, 311, 312, 322, 323, 324, 325, 329, 330, 337, 343, 346, 347, 362, 371, 373, 374, 380, 381, 382, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 423, 425, 426, 427, 428, 430, 431, 449, 464, 466, 494, 498, 499, 500, 509, 510, 545, 555, 564, 566, 569, 574, 581, 614, 616, 660, 667, 680, 688, 691, 704, 706, 707, 708, 710, 711, 712, 713, 714, 715, 717, 732, 738, 758, 761, 762, 786, 791, 796, 887, 925, 934, 935, 968, 979, 980, 1007, 1036, 1037, 1038, 1041, 1043, 1082, 1088, 1182, 1229, 1235, 1253, 1267, 1296, 1320, 1321, 1323, 1326, 1383, 1387, 1392, 1395, 1402, 1403, 1404, 1406, 1407, 1408, 1409, 1411, 1412, 1421, 1425], "obtain": [11, 91, 165, 207, 282, 345, 346, 347, 380, 383, 387, 388, 389, 390, 394, 465, 511, 606, 618, 619, 656, 722, 742, 743, 760, 796, 862, 892, 907, 928, 943, 973, 988, 1010, 1037, 1039, 1040, 1164, 1253, 1272, 1278, 1279, 1323, 1326, 1356, 1357, 1402, 1426], "seri": [11, 444, 616, 680, 1215, 1286], "finit": [11, 462, 494, 495, 498, 499, 502, 503, 506, 507, 509, 510, 514, 518, 1177, 1179, 1192, 1222], "end": [11, 25, 36, 52, 95, 101, 106, 153, 154, 206, 215, 227, 267, 268, 300, 332, 333, 342, 371, 372, 427, 614, 618, 619, 626, 627, 631, 632, 634, 635, 636, 639, 640, 650, 651, 652, 653, 654, 655, 660, 664, 667, 677, 678, 680, 734, 736, 1038, 1061, 1066, 1075, 1080, 1082, 1084, 1117, 1133, 1135, 1152, 1165, 1206, 1229, 1326, 1333, 1334, 1337, 1338, 1339, 1340, 1342, 1344, 1350, 1353, 1357, 1358, 1368, 1371, 1372, 1375, 1376, 1379, 1404, 1413], "In": [11, 16, 27, 43, 54, 57, 58, 88, 92, 93, 94, 95, 97, 99, 100, 101, 103, 110, 115, 127, 132, 133, 175, 184, 199, 217, 229, 230, 231, 235, 240, 257, 258, 259, 278, 283, 286, 288, 289, 299, 311, 312, 324, 325, 329, 350, 357, 378, 379, 380, 410, 413, 414, 415, 422, 429, 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1413, 1414, 1417, 1425, 1426], "produc": [15, 44, 49, 103, 115, 226, 246, 247, 272, 280, 297, 298, 306, 307, 315, 316, 329, 330, 422, 460, 565, 601, 612, 629, 632, 633, 635, 636, 677, 678, 680, 691, 786, 1097, 1102, 1103, 1105, 1165, 1179, 1181, 1189, 1212, 1236, 1280, 1281, 1282, 1283, 1284, 1285, 1286, 1287, 1288, 1392, 1399, 1406, 1408, 1416, 1417], "infer": [15, 695, 1104, 1118, 1358, 1412], "differ": [15, 25, 27, 28, 33, 41, 53, 54, 57, 63, 71, 86, 92, 93, 94, 95, 99, 103, 112, 161, 164, 165, 204, 207, 215, 216, 223, 280, 282, 297, 298, 314, 315, 326, 330, 334, 335, 337, 341, 358, 361, 371, 372, 373, 374, 378, 410, 413, 414, 415, 435, 437, 509, 511, 512, 593, 602, 615, 704, 717, 718, 738, 750, 758, 772, 786, 862, 891, 892, 907, 928, 943, 972, 973, 988, 1010, 1102, 1105, 1133, 1165, 1169, 1170, 1171, 1193, 1198, 1207, 1255, 1269, 1287, 1296, 1326, 1365, 1366, 1382, 1394, 1404, 1405, 1406, 1413, 1414, 1425, 1426], "relat": [15, 34, 67, 92, 93, 95, 99, 100, 115, 129, 132, 220, 230, 297, 366, 370, 586, 588, 619, 688, 762, 767, 795, 1202, 1205, 1269, 1323, 1395, 1402, 1406, 1413, 1416, 1425], "strong": [15, 397, 511, 512, 517, 610, 619, 691, 699, 758, 1408], "weak": [15, 398, 691, 758, 1425], "number_of_nod": [15, 25, 80, 156, 187, 311, 324, 337, 383, 564, 581, 852, 855, 876, 897, 900, 919, 933, 936, 958, 978, 981, 1001, 1154, 1271, 1426], "7482934": 15, "_": [15, 16, 26, 38, 93, 105, 300, 333, 356, 372, 405, 406, 425, 426, 502, 503, 506, 507, 569, 588, 630, 1352, 1354, 1378, 1380, 1411], "edge_type_visual_weight_lookup": 15, "edge_weight": [15, 382, 583], "node_attribut": [15, 691], "edge_attribut": [15, 283, 691, 1101], "summary_graph": [15, 691], "snap_aggreg": [15, 758, 1413], "prefix": [15, 67, 512, 690, 691, 1272, 1326, 1347, 1413, 1421], "aggreg": [15, 511, 512, 691, 786], "summary_po": 15, "8375428": 15, "edge_typ": 15, "get_edge_data": [15, 25, 1411], "272": [15, 17], "plot_snap": [15, 17], "support": [16, 52, 77, 92, 93, 96, 100, 101, 102, 103, 226, 308, 322, 339, 340, 342, 343, 356, 373, 410, 411, 412, 418, 419, 464, 494, 496, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 597, 626, 627, 632, 633, 635, 636, 690, 738, 762, 775, 786, 796, 1037, 1038, 1039, 1040, 1114, 1116, 1146, 1302, 1326, 1341, 1342, 1344, 1353, 1354, 1355, 1356, 1357, 1358, 1379, 1380, 1381, 1383, 1387, 1394, 1395, 1396, 1398, 1402, 1404, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426], "unsupport": 16, "contain": [16, 25, 34, 45, 65, 69, 71, 88, 99, 102, 104, 114, 115, 151, 152, 157, 158, 165, 166, 167, 168, 172, 175, 176, 177, 180, 188, 189, 193, 195, 199, 207, 212, 214, 220, 226, 236, 237, 238, 240, 241, 243, 245, 248, 249, 252, 253, 255, 256, 257, 258, 259, 260, 264, 266, 267, 270, 277, 278, 280, 281, 290, 293, 294, 299, 315, 320, 322, 338, 344, 346, 347, 350, 352, 353, 355, 356, 357, 358, 360, 373, 377, 379, 380, 381, 388, 400, 408, 414, 415, 427, 432, 433, 437, 440, 457, 481, 482, 494, 495, 498, 499, 500, 502, 503, 506, 507, 509, 510, 512, 513, 514, 516, 549, 550, 564, 568, 572, 574, 589, 593, 596, 599, 602, 621, 624, 631, 632, 652, 656, 658, 660, 661, 662, 687, 688, 689, 695, 723, 724, 725, 726, 749, 786, 796, 853, 854, 856, 857, 862, 863, 864, 865, 868, 869, 870, 871, 877, 878, 882, 884, 887, 892, 898, 899, 901, 902, 907, 908, 909, 910, 913, 914, 921, 923, 925, 928, 934, 935, 937, 938, 943, 944, 945, 946, 949, 950, 951, 952, 959, 960, 963, 965, 968, 973, 979, 980, 982, 983, 988, 989, 990, 991, 994, 995, 1003, 1005, 1007, 1010, 1037, 1038, 1039, 1040, 1041, 1052, 1053, 1054, 1061, 1066, 1085, 1086, 1087, 1094, 1097, 1100, 1102, 1103, 1105, 1106, 1118, 1127, 1140, 1150, 1151, 1152, 1154, 1157, 1164, 1173, 1200, 1201, 1206, 1207, 1208, 1211, 1251, 1286, 1296, 1297, 1298, 1302, 1322, 1323, 1324, 1326, 1331, 1334, 1352, 1356, 1359, 1360, 1363, 1364, 1371, 1378, 1390, 1395, 1403, 1404, 1406, 1407, 1409, 1411, 1412, 1414, 1423, 1425, 1426], "entir": [16, 95, 101, 165, 179, 184, 260, 360, 375, 577, 862, 873, 907, 916, 943, 955, 988, 998, 1038, 1085, 1100, 1225, 1406, 1409], "adopt": [16, 96, 98, 101, 102, 107, 1405, 1414], "lobpcg": [16, 91, 1275, 1276, 1277], "python_exampl": 16, "graph_partit": 16, "categor": [16, 546, 547, 548, 611], "node_typ": [16, 1342, 1356, 1357], "supported_nod": 16, "unsupported_nod": 16, "remove_edges_from": [16, 89, 192, 453, 602, 881, 920, 962, 1002, 1175, 1177, 1222, 1393, 1394, 1412, 1420, 1426], "nbr": [16, 88, 159, 190, 199, 200, 207, 229, 230, 231, 285, 500, 506, 796, 858, 879, 887, 888, 892, 903, 925, 928, 939, 968, 969, 973, 984, 1007, 1010, 1037, 1039, 1040, 1094, 1326, 1404, 1426], "adj": [16, 88, 199, 200, 207, 324, 325, 796, 849, 887, 888, 892, 894, 915, 925, 928, 930, 968, 969, 973, 975, 996, 1007, 1010, 1037, 1039, 1040, 1094, 1326, 1404, 1411, 1417, 1425, 1426], "g_minus_h": 16, "strip": [16, 25, 69, 1215], "_node_color": 16, "_po": 16, "draw_networkx_edg": [16, 25, 26, 27, 28, 33, 35, 38, 39, 40, 41, 44, 46, 68, 83, 1130, 1133, 1134, 1136, 1137, 1411, 1413], "draw_networkx_label": [16, 25, 35, 38, 46, 71, 1130, 1133, 1134, 1135, 1137], "ncl": 16, "undirect": [16, 25, 34, 71, 93, 112, 177, 185, 204, 205, 209, 211, 212, 214, 215, 216, 217, 218, 219, 220, 221, 224, 227, 228, 229, 230, 231, 232, 237, 239, 240, 246, 247, 264, 267, 275, 277, 278, 280, 281, 293, 294, 295, 297, 298, 300, 313, 315, 318, 319, 321, 322, 328, 330, 331, 332, 333, 337, 338, 341, 345, 346, 347, 348, 349, 350, 352, 353, 371, 372, 379, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 428, 430, 431, 437, 439, 440, 450, 463, 464, 465, 466, 467, 478, 479, 480, 481, 482, 485, 486, 487, 488, 490, 491, 492, 500, 559, 560, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 581, 582, 583, 590, 594, 595, 598, 600, 601, 605, 606, 607, 610, 611, 613, 615, 618, 619, 624, 625, 652, 658, 681, 682, 683, 684, 686, 687, 688, 689, 692, 694, 717, 718, 727, 730, 731, 732, 734, 735, 736, 737, 738, 742, 743, 753, 760, 761, 762, 767, 779, 791, 874, 891, 917, 927, 956, 972, 999, 1009, 1036, 1038, 1056, 1060, 1088, 1090, 1098, 1101, 1115, 1133, 1135, 1146, 1166, 1167, 1173, 1175, 1182, 1184, 1187, 1189, 1190, 1191, 1193, 1196, 1197, 1198, 1199, 1202, 1206, 1207, 1217, 1219, 1230, 1243, 1244, 1247, 1250, 1251, 1252, 1254, 1259, 1273, 1275, 1276, 1278, 1279, 1282, 1298, 1323, 1326, 1327, 1333, 1341, 1342, 1344, 1351, 1352, 1353, 1354, 1371, 1377, 1378, 1379, 1380, 1381, 1383, 1389, 1390, 1395, 1401, 1402, 1404, 1406, 1408, 1411, 1414, 1417, 1426], "And": [16, 23, 47, 86, 93, 101, 107, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 467, 502, 503, 506, 507, 688, 1296, 1297, 1328, 1408, 1409, 1411, 1416, 1425], "specifi": [16, 24, 25, 62, 102, 151, 152, 157, 158, 167, 184, 185, 193, 207, 222, 223, 226, 232, 236, 238, 240, 241, 243, 244, 246, 247, 248, 260, 264, 266, 267, 268, 269, 271, 273, 276, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 299, 305, 310, 311, 320, 324, 326, 329, 338, 348, 349, 353, 356, 357, 374, 377, 410, 411, 412, 413, 414, 415, 418, 419, 433, 435, 436, 440, 442, 443, 444, 445, 447, 448, 449, 458, 473, 491, 494, 495, 498, 499, 510, 518, 552, 553, 554, 555, 564, 565, 566, 575, 577, 584, 588, 597, 601, 604, 608, 609, 635, 636, 660, 671, 672, 673, 674, 676, 686, 691, 692, 704, 705, 706, 707, 708, 710, 711, 712, 713, 714, 715, 716, 721, 722, 751, 760, 853, 854, 856, 857, 864, 873, 874, 882, 892, 898, 899, 901, 902, 909, 916, 917, 921, 928, 934, 935, 937, 938, 945, 947, 948, 955, 956, 962, 963, 973, 979, 980, 982, 983, 990, 992, 993, 998, 999, 1002, 1003, 1010, 1043, 1061, 1070, 1071, 1072, 1081, 1094, 1095, 1096, 1098, 1099, 1104, 1117, 1130, 1133, 1134, 1135, 1136, 1137, 1151, 1154, 1165, 1175, 1177, 1178, 1181, 1182, 1189, 1193, 1196, 1197, 1198, 1199, 1202, 1207, 1210, 1211, 1212, 1219, 1222, 1235, 1242, 1275, 1276, 1277, 1278, 1279, 1294, 1295, 1296, 1297, 1300, 1315, 1323, 1324, 1326, 1328, 1331, 1334, 1336, 1337, 1338, 1339, 1340, 1341, 1344, 1345, 1348, 1349, 1350, 1356, 1357, 1360, 1363, 1364, 1382, 1393, 1397, 1398, 1399, 1402, 1403, 1404, 1406, 1407, 1412, 1416, 1426], "to_undirect": [16, 25, 69, 796, 1037, 1039, 1040, 1182, 1184, 1404, 1413, 1426], "magenta": 16, "six": 16, "classifi": [16, 512, 684, 750], "four": [16, 23, 47, 86, 99, 102, 165, 263, 585, 587, 692, 862, 907, 943, 988, 1039, 1040, 1164, 1193, 1199, 1211, 1323, 1407, 1408, 1414, 1426], "green": [16, 32, 38, 70, 93, 115, 464, 598, 760, 1302, 1330, 1394, 1412, 1426], "goal": [16, 88, 92, 99, 105, 107, 127, 383, 626, 627, 717, 718, 1042], "g_ex": 16, "m": [16, 25, 28, 30, 31, 63, 65, 67, 91, 93, 96, 102, 106, 110, 112, 128, 181, 191, 201, 209, 211, 212, 219, 227, 231, 235, 236, 238, 239, 240, 241, 243, 244, 248, 257, 258, 259, 263, 272, 274, 275, 278, 280, 282, 284, 293, 294, 296, 300, 301, 302, 308, 309, 315, 316, 317, 330, 338, 341, 343, 345, 352, 355, 356, 361, 362, 370, 380, 383, 385, 412, 429, 431, 432, 433, 451, 462, 479, 494, 498, 499, 509, 510, 511, 512, 519, 545, 555, 569, 582, 584, 585, 587, 588, 606, 614, 619, 625, 652, 658, 659, 684, 686, 691, 692, 706, 748, 749, 761, 762, 775, 872, 880, 889, 953, 961, 970, 1060, 1151, 1155, 1157, 1169, 1175, 1177, 1179, 1181, 1199, 1201, 1202, 1203, 1204, 1205, 1207, 1208, 1209, 1210, 1211, 1213, 1215, 1216, 1218, 1219, 1220, 1222, 1223, 1226, 1229, 1230, 1231, 1233, 1234, 1235, 1240, 1256, 1265, 1269, 1271, 1278, 1279, 1280, 1287, 1288, 1292, 1323, 1387, 1406, 1409, 1426], "node_color_list": 16, "nc": [16, 56], "spectral_layout": [16, 43, 1141, 1399, 1406], "subgraphs_of_g_ex": 16, "removed_edg": 16, "node_color_list_c": 16, "One": [16, 52, 55, 101, 102, 103, 115, 545, 559, 560, 679, 684, 761, 1177, 1186, 1272, 1315, 1326, 1404, 1426], "g_ex_r": 16, "compos": [16, 269, 270, 271, 272, 273, 274, 275, 276, 600, 604, 758, 1400, 1406, 1407, 1417, 1423, 1425], "previous": [16, 91, 108, 112, 322, 614, 1182, 1183, 1184, 1395, 1407, 1417], "store": [16, 25, 39, 53, 54, 55, 57, 67, 86, 93, 97, 101, 102, 110, 158, 219, 220, 283, 290, 345, 346, 347, 431, 470, 471, 472, 473, 474, 475, 494, 495, 498, 499, 502, 503, 506, 507, 509, 510, 585, 587, 615, 660, 664, 667, 719, 733, 739, 762, 786, 796, 857, 902, 938, 983, 1037, 1038, 1039, 1040, 1046, 1085, 1086, 1101, 1102, 1104, 1165, 1170, 1193, 1196, 1197, 1198, 1199, 1213, 1215, 1278, 1294, 1296, 1330, 1333, 1334, 1345, 1348, 1349, 1350, 1359, 1360, 1363, 1364, 1365, 1366, 1371, 1382, 1388, 1390, 1394, 1404, 1414], "assert": [16, 67, 88, 102, 1411, 1414, 1424, 1425, 1426], "is_isomorph": [16, 584, 585, 587, 588, 608, 671, 690, 739, 758, 761, 762, 1399, 1406], "966": [16, 17], "plot_subgraph": [16, 17, 1414], "947": 17, "auto_examples_algorithm": 17, "read": [18, 22, 25, 40, 52, 54, 55, 57, 58, 65, 75, 86, 93, 94, 100, 115, 159, 165, 167, 190, 200, 267, 583, 618, 796, 858, 862, 864, 879, 888, 903, 907, 909, 939, 943, 945, 947, 969, 984, 988, 990, 992, 1012, 1013, 1018, 1019, 1020, 1021, 1022, 1035, 1036, 1037, 1038, 1039, 1040, 1042, 1043, 1061, 1066, 1082, 1083, 1088, 1121, 1143, 1144, 1270, 1296, 1325, 1326, 1329, 1330, 1333, 1337, 1338, 1342, 1343, 1345, 1348, 1349, 1350, 1351, 1352, 1354, 1356, 1357, 1367, 1368, 1371, 1375, 1377, 1378, 1380, 1381, 1382, 1383, 1386, 1387, 1388, 1389, 1390, 1394, 1395, 1397, 1398, 1401, 1402, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 1413, 1414, 1418, 1424, 1425], "write": [18, 22, 49, 52, 75, 76, 77, 86, 89, 93, 99, 105, 110, 115, 267, 268, 470, 1012, 1013, 1018, 1019, 1020, 1021, 1022, 1047, 1123, 1129, 1300, 1325, 1326, 1329, 1330, 1334, 1337, 1339, 1340, 1344, 1345, 1348, 1349, 1350, 1352, 1354, 1357, 1358, 1372, 1375, 1376, 1378, 1380, 1381, 1382, 1383, 1387, 1388, 1390, 1395, 1397, 1398, 1399, 1401, 1402, 1405, 1406, 1411, 1412, 1414, 1425, 1426], "simpl": [18, 22, 23, 32, 47, 86, 93, 94, 97, 100, 103, 109, 110, 132, 184, 220, 229, 230, 231, 249, 287, 293, 300, 304, 313, 321, 328, 332, 333, 338, 343, 371, 372, 373, 380, 381, 423, 425, 438, 452, 453, 468, 479, 481, 482, 490, 496, 500, 504, 505, 508, 514, 517, 518, 594, 608, 624, 632, 677, 678, 679, 680, 686, 693, 758, 775, 780, 796, 873, 916, 955, 998, 1037, 1038, 1039, 1040, 1098, 1099, 1100, 1130, 1133, 1175, 1177, 1180, 1181, 1207, 1208, 1209, 1210, 1217, 1219, 1222, 1252, 1269, 1296, 1323, 1325, 1326, 1328, 1330, 1351, 1352, 1353, 1354, 1382, 1388, 1395, 1401, 1404, 1406, 1407, 1412, 1413, 1421, 1426], "lollipop": [19, 1157, 1426], "vertex": [19, 115, 211, 235, 249, 281, 289, 315, 322, 330, 338, 359, 360, 373, 387, 394, 397, 427, 428, 432, 438, 477, 491, 580, 606, 615, 616, 619, 622, 623, 624, 688, 689, 758, 1164, 1185, 1190, 1206, 1218, 1219, 1222, 1251, 1323, 1326, 1400, 1406, 1407], "length": [19, 39, 52, 67, 102, 120, 151, 232, 288, 295, 297, 298, 299, 306, 307, 310, 314, 315, 316, 320, 322, 326, 327, 329, 330, 332, 333, 341, 343, 345, 346, 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1412], "abc": [144, 545, 1154, 1206, 1303, 1412, 1413], "interchang": [144, 362], "bool": [145, 146, 148, 149, 165, 168, 171, 176, 184, 189, 196, 204, 208, 232, 237, 238, 242, 243, 245, 249, 250, 258, 265, 266, 267, 268, 272, 275, 286, 287, 288, 291, 294, 295, 296, 297, 298, 299, 301, 302, 305, 306, 307, 308, 309, 310, 314, 315, 322, 324, 325, 326, 327, 330, 343, 350, 355, 362, 393, 394, 395, 396, 397, 398, 439, 454, 462, 463, 467, 479, 480, 488, 489, 491, 494, 498, 499, 509, 510, 513, 514, 515, 516, 517, 518, 520, 521, 522, 545, 562, 564, 578, 579, 580, 581, 588, 613, 614, 616, 617, 622, 623, 625, 640, 652, 663, 673, 679, 685, 690, 696, 698, 699, 700, 704, 708, 719, 723, 724, 725, 726, 728, 730, 733, 734, 735, 736, 737, 738, 740, 741, 742, 743, 862, 865, 867, 870, 873, 878, 885, 891, 907, 910, 912, 916, 927, 931, 943, 946, 948, 951, 955, 960, 966, 972, 976, 988, 991, 993, 998, 1039, 1040, 1045, 1057, 1068, 1070, 1071, 1072, 1084, 1091, 1097, 1116, 1133, 1134, 1135, 1136, 1169, 1179, 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"connect_compon": [153, 154, 155, 615], "add_half_edge_first": [153, 154, 164, 615], "add_half_edge_ccw": [154, 155, 164, 615], "node_for_ad": [156, 855, 900, 936, 981], "mutabl": [156, 855, 900, 936, 981, 1061, 1066, 1082, 1085, 1086], "hash": [156, 511, 512, 758, 855, 900, 936, 981, 1324, 1325, 1414, 1426], "hello": [156, 157, 855, 856, 900, 901, 936, 937, 981, 982, 1303], "k3": [156, 157, 855, 856, 900, 901, 936, 937, 981, 982, 1217], "utm": [156, 855, 900, 936, 981], "382871": [156, 855, 900, 936, 981], "3972649": [156, 855, 900, 936, 981], "nodes_for_ad": [157, 856, 901, 937, 982], "iterator_of_nod": [157, 195, 856, 884, 901, 923, 937, 965, 982, 1005], "datadict": [159, 190, 200, 207, 734, 736, 858, 879, 888, 892, 903, 928, 939, 969, 973, 1010, 1084, 1312, 1326], "foovalu": [159, 190, 200, 858, 879, 888, 903, 939, 969], "nbrdict": [160, 859, 904, 940, 985, 1019, 1094], "fulfil": [161, 615], "cw": [161, 615], "ccw": [161, 615], "planar": [161, 614, 616, 617, 758, 1110, 1138, 1243, 1246, 1247, 1249, 1325, 1409, 1410], "first_nbr": [161, 615], "invalid": [161, 615, 1413], "alter": [163, 861, 906, 942, 987], "afterward": 164, "as_view": [165, 202, 204, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1089, 1090], "shallow": [165, 202, 204, 284, 285, 286, 287, 288, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1394], "deepcopi": [165, 202, 204, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1409], "__class__": [165, 199, 862, 887, 907, 925, 943, 968, 988, 1007, 1404, 1407, 1409, 1410, 1411], "fresh": [165, 862, 907, 943, 988, 1404], "inspir": [165, 230, 231, 342, 681, 862, 907, 943, 988, 1226, 1323, 1404], "deep": [165, 202, 204, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1265, 1394], "degreeview": [166, 863, 908, 944, 950, 989, 1404, 1426], "didegreeview": [166, 863], "outedgeview": [168, 189, 467, 468, 613, 747, 750, 865, 878, 1035, 1083, 1404, 1418], "ddict": [168, 176, 184, 189, 865, 870, 873, 878, 910, 916, 946, 951, 955, 960, 991, 998], "in_edg": [168, 189, 865, 878, 946, 960, 1404, 1406, 1407], "out_edg": [168, 865, 946, 1062, 1404, 1406, 1407, 1426], "quietli": [168, 189, 865, 878, 910, 946, 960, 991, 1087, 1426], "outedgedataview": [168, 189, 865, 878, 1404, 1411], "set_data": 169, "edge_dict": [170, 866, 911, 947, 992], "safe": [170, 866, 911, 1404, 1412], "edge_ind": [171, 867, 912, 948, 993], "data_dictionari": [171, 867, 912], "simpler": [172, 184, 868, 873, 913, 916, 949, 955, 994, 998, 1406, 1407, 1417], "indegreeview": [175, 869, 1404], "deg": [175, 188, 243, 259, 356, 361, 685, 869, 877, 950, 959, 1165, 1179, 1222, 1404], "inedgeview": [176, 870, 1404], "inedgedataview": [176, 870], "silent": [180, 193, 195, 320, 871, 882, 884, 914, 921, 923, 952, 963, 965, 995, 1003, 1005, 1085, 1086, 1127, 1353, 1354, 1359, 1363, 1406, 1413], "niter": [180, 681, 682, 683, 684, 851, 871, 896, 914, 932, 952, 977, 995, 1414], "__iter__": [180, 871, 914, 952, 995, 1303], "nodedata": [184, 873, 916, 955, 998], "5pm": [184, 796, 873, 916, 955, 998, 1037, 1039, 1040, 1394, 1426], "Not": [184, 379, 432, 433, 434, 435, 436, 437, 438, 476, 873, 916, 955, 998, 1117, 1216], "nedg": [185, 588, 874, 917, 956, 999], "__len__": [186, 187, 875, 876, 918, 919, 957, 958, 1000, 1001], "outdegreeview": [188, 877], "Will": [193, 362, 605, 607, 610, 882, 921, 963, 1003, 1404, 1414], "get_data": [197, 616], "inplac": [199, 690, 887, 925, 968, 1007, 1066, 1393], "reduct": [199, 469, 618, 786, 887, 925, 968, 1007, 1066, 1320, 1321, 1413, 1414], "sg": [199, 887, 925, 968, 1007], "largest_wcc": [199, 887, 925, 968, 1007], "is_multigraph": [199, 758, 887, 925, 968, 1007, 1154, 1412], "keydict": [199, 207, 887, 892, 925, 928, 968, 973, 1007, 1010, 1039, 1040], "contrast": [202, 204, 301, 302, 308, 309, 890, 891, 926, 927, 971, 972, 1008, 1009, 1066, 1233, 1241, 1426], "reciproc": [204, 299, 320, 322, 356, 411, 430, 447, 476, 620, 758, 891, 972, 1325, 1416, 1425], "mark_half_edg": 206, "li": [206, 619, 670, 675, 685, 775, 1207, 1210, 1425], "straightforward": [207, 892, 928, 973, 1010], "slightli": [207, 326, 437, 520, 521, 581, 892, 928, 973, 1010, 1165, 1326, 1404, 1407, 1412, 1414, 1425], "singleton": [207, 588, 892, 928, 973, 1010, 1218, 1251, 1407], "preserve_attr": [208, 723, 724, 725, 726], "optimum": [208, 231, 583, 720, 722, 791, 1395, 1406], "arboresc": [208, 460, 719, 720, 722, 724, 726, 740, 743, 758, 1272, 1395, 1406], "span": [208, 226, 227, 228, 295, 508, 618, 619, 624, 719, 720, 722, 724, 726, 732, 733, 734, 735, 736, 737, 738, 758, 1394, 1397, 1406, 1407, 1420], "max_ind_cliqu": 209, "networkxnotimpl": [209, 210, 211, 212, 220, 224, 227, 293, 294, 295, 318, 319, 321, 328, 343, 379, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 403, 404, 405, 406, 407, 422, 424, 425, 426, 427, 429, 455, 457, 458, 459, 460, 468, 481, 482, 500, 589, 590, 608, 680, 732, 1043, 1216, 1275, 1276, 1298, 1325, 1353, 1354, 1379, 1407, 1408], "boppana": [209, 211, 212], "halld\u00f3rsson": [209, 211, 212], "1992": [209, 211, 212, 517, 518, 1407], "exclud": [209, 211, 212, 215, 216, 261, 262, 453, 688, 719, 723, 724, 725, 726, 733, 751, 1036, 1038, 1088, 1217, 1412], "180": [209, 211, 212, 238], "196": [209, 211, 212], "heurist": [210, 220, 228, 233, 234, 377, 380, 381, 427, 494, 509, 626, 627, 652, 663, 703, 758, 1173, 1320, 1321, 1325, 1395, 1408, 1412, 1413], "max_cliqu": 210, "rigor": 210, "pattabiraman": 210, "bharath": 210, "massiv": [210, 217], "421": 210, "448": 210, "1080": [210, 297, 298, 306, 307, 329], "15427951": 210, "986778": 210, "apx": [211, 212], "subseteq": [211, 280, 289, 618, 675], "omega": [211, 758, 782, 1414], "maximum_cliqu": 211, "1007": [211, 226, 296, 301, 302, 303, 308, 309, 323, 324, 325, 341, 431, 451, 498, 574, 1144, 1181], "bf01994876": 211, "iset": 212, "trial": [213, 230, 231, 1195, 1237, 1238], "estim": [213, 224, 297, 306, 313, 564, 625, 626, 627, 782, 1280, 1407], "coeffici": [213, 248, 260, 261, 262, 263, 289, 355, 356, 358, 570, 618, 619, 625, 682, 684, 778, 782, 1397, 1398, 1399, 1406, 1413], "fraction": [213, 257, 259, 286, 289, 297, 299, 304, 306, 315, 317, 318, 319, 321, 322, 326, 328, 330, 356, 358, 359, 519, 1165, 1234], "schank": 213, "thoma": [213, 751, 1407, 1409, 1413], "dorothea": [213, 1168], "wagner": [213, 429, 758, 1168, 1402, 1406], "universit\u00e4t": 213, "karlsruh": 213, "fakult\u00e4t": 213, "f\u00fcr": 213, "informatik": [213, 412], "5445": 213, "ir": [213, 606], "1000001239": 213, "erdos_renyi_graph": [213, 1224, 1232, 1326, 1406, 1426], "214": 213, "cutoff": [214, 215, 310, 326, 383, 410, 411, 412, 418, 419, 494, 495, 498, 499, 510, 637, 638, 640, 641, 642, 643, 644, 647, 648, 649, 656, 660, 661, 662, 667, 668, 669, 677, 678, 1234, 1398, 1402, 1406, 1413, 1416, 1424, 1425], "distinct": [214, 215, 255, 281, 288, 352, 391, 452, 453, 460, 578, 595, 608, 618, 700, 701, 734, 735, 736, 737, 789, 1150, 1244, 1271, 1323, 1326, 1328, 1395, 1417], "nonadjac": [214, 215, 480, 584, 585, 587], "cutset": [214, 215, 414, 415, 416, 417, 427, 428, 500, 506, 758], "menger": [214, 215, 216], "theorem": [214, 215, 216, 220, 235, 281, 311, 312, 322, 411, 506, 507, 514, 517, 518, 618, 1190, 1205], "local_node_connect": [214, 216, 408, 409, 410, 411, 413], "node_connect": [214, 215, 409, 410, 411, 412, 414, 415, 416, 417, 419, 427, 428, 1402], "dougla": [214, 215, 216, 220, 1413, 1425], "035": [214, 215, 216, 220], "eclect": [214, 215, 216], "ss": [214, 215, 216], "uci": [214, 215, 216, 467, 704, 706, 707, 708, 710, 734, 736], "drwhite": [214, 215, 216], "pprint": [214, 577, 711], "all_pairs_node_connect": [215, 216, 1402, 1424, 1425], "bf": [215, 216, 217, 363, 588, 704, 706, 707, 708, 717, 1397, 1401, 1406, 1409, 1412, 1413], "lose": [215, 796, 1037, 1039, 1040], "accuraci": [215, 312, 786], "platon": [215, 216, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 1245, 1248, 1254, 1257, 1261, 1263], "octahedr": [215, 216, 1257], "approx": [215, 216, 227, 229, 230, 231, 1413], "octahedral_graph": [215, 216], "vari": [217, 238, 243, 373, 378, 569, 695], "sweep": [217, 1412], "dsweep": 217, "a_1": [217, 477], "a_2": 217, "magnien": [217, 260, 261, 262, 289], "cl\u00e9menc": [217, 260, 261, 262, 289], "matthieu": [217, 260, 261, 262, 274, 289], "latapi": [217, 260, 261, 262, 274, 289], "michel": 217, "habib": 217, "empir": 217, "tight": 217, "jea": 217, "0904": 217, "2728": 217, "crescenzi": 217, "pierluigi": 217, "roberto": 217, "grossi": 217, "leonardo": 217, "lanzi": 217, "andrea": [217, 1165, 1413], "marino": 217, "symposium": [217, 619, 1186, 1195, 1239], "berlin": [217, 520, 521, 1413], "heidelberg": [217, 520, 521], "ut": 217, "ee": [217, 313], "mtat": 217, "238": 217, "2014_fall": 217, "domin": [218, 219, 311, 410, 414, 481, 482, 483, 484, 758, 1325, 1395, 1400, 1406, 1407], "opt": [218, 221, 1425], "min_weight_dominating_set": 219, "vazirani": [219, 221], "vijai": [219, 221, 517], "min_dens": 220, "95": [220, 590, 1283, 1284, 1382], "nest": [220, 427, 728, 730, 791, 1038, 1045, 1061, 1094, 1296, 1308, 1348, 1355, 1356, 1357, 1358, 1383, 1406], "forth": [220, 427], "relax": [220, 227, 1171, 1413], "narrow": [220, 1165], "whitnei": 220, "bicompon": [220, 387, 389, 390, 394], "ferraro": [220, 427], "cohes": [220, 427, 437], "1503": [220, 427], "04476v1": [220, 427], "santaf": 220, "ind": 220, "embedded": [220, 305, 427], "sociolog": [220, 427, 748], "103": [220, 427, 1222, 1288, 1292], "2307": [220, 297, 1255], "3088904": 220, "petersen": [220, 427, 761, 1251, 1256, 1259], "triconnect": [220, 427], "apxa": 220, "petersen_graph": [220, 380, 427, 492, 761, 1119, 1120, 1426], "fo": 221, "initial_cut": 222, "highest": [222, 269, 273, 276, 337, 357, 374, 387, 389, 390, 394, 428, 509, 688, 703, 1180], "suppli": [222, 256, 277, 278, 280, 281, 594, 1197, 1320, 1321, 1326, 1345, 1348, 1349, 1350, 1382, 1408, 1413], "cut_valu": [222, 429, 500, 506, 507, 1402], "probabl": [223, 227, 230, 231, 236, 237, 238, 241, 242, 243, 245, 274, 275, 296, 358, 452, 468, 593, 675, 738, 758, 796, 1037, 1039, 1040, 1168, 1169, 1170, 1171, 1173, 1175, 1179, 1182, 1184, 1185, 1186, 1187, 1188, 1193, 1195, 1196, 1197, 1198, 1199, 1203, 1205, 1224, 1225, 1227, 1228, 1229, 1230, 1232, 1233, 1234, 1235, 1236, 1239, 1241, 1278, 1279, 1283, 1284, 1319, 1403, 1404, 1406, 1414, 1417, 1426], "cut_siz": [223, 442, 447, 448, 758], "ramsei": [224, 758], "max_pair": 224, "closur": [225, 226, 467, 468, 1036, 1088, 1395, 1406, 1408, 1411], "terminal_nod": 226, "steiner": [226, 758, 1408, 1425], "leaf": [226, 355, 460, 465, 678, 1155, 1236, 1272], "across": [226, 248, 625, 1038, 1100, 1326, 1405], "kou": 226, "mehlhorn": [226, 511, 512, 1425], "proce": [226, 231, 232, 373, 378, 518, 1165], "steiner_tree_problem": 226, "markowski": 226, "berman": 226, "1981": [226, 1164, 1323], "acta": [226, 508], "informatica": [226, 508], "bf00288961": 226, "kurt": [226, 511, 512], "1988": [226, 1199, 1407], "0020": [226, 455], "0190": [226, 455], "88": [226, 513, 1178, 1180], "90066": 226, "held": [227, 1105], "karp": [227, 277, 278, 280, 499, 758, 1169, 1395, 1402, 1406], "entropi": 227, "scheme": [227, 337, 719, 733, 1393], "lceil": 227, "rceil": 227, "augment": [227, 422, 496, 510, 581, 758, 1408], "tour": [227, 488, 490], "pari": 227, "inequ": [227, 1283, 1284], "trip": [227, 229, 230, 231], "goeman": 227, "madri": 227, "gharan": 227, "saberi": [227, 1181], "1043": 227, "1061": 227, "set_edge_attribut": [227, 374, 500, 598, 626, 1402, 1404, 1407], "minimum_spanning_tre": [228, 1406, 1407], "hamiltonian": [228, 232, 697, 1242, 1244, 1249, 1250, 1254, 1258, 1264], "nico": 228, "rr": 228, "388": [228, 300], "carnegi": 228, "mellon": 228, "univ": 228, "pa": 228, "1976": [228, 453, 516, 1407], "essenc": 229, "feasibl": [229, 422, 494, 496, 498, 499, 502, 503, 504, 505, 508, 509, 510, 531, 534, 541, 544, 762, 1043], "init_cycl": [230, 231, 1413], "temp": [230, 232, 1098], "max_iter": [230, 231, 676], "n_inner": [230, 231], "suboptim": [230, 231, 581], "perturb": [230, 231], "wors": [230, 231, 301, 302, 308, 309, 494], "escap": [230, 231, 1407, 1413], "decreas": [230, 231, 332, 333, 337, 367, 383, 608, 673, 692, 703, 719, 733, 1116, 1175, 1177, 1222, 1234, 1294], "temperatur": [230, 1117], "steel": 230, "harden": 230, "cool": 230, "goe": 230, "greedy_tsp": [230, 231, 232, 1413], "threshold_accepting_tsp": [230, 232, 1413], "transpos": [230, 231, 282], "swap_two_nod": [230, 231], "transposit": [230, 231], "move_one_nod": [230, 231], "enact": [230, 231], "declar": [230, 231], "outer": [230, 231, 380, 436, 606, 615, 796, 1012, 1013, 1018, 1019, 1020, 1021, 1022, 1037, 1039, 1040, 1086, 1160, 1326], "percentag": [230, 231, 1269], "metaheurist": [230, 231], "characterist": [230, 231, 682, 775], "thoughtfulli": [230, 231], "exp": [230, 1197, 1199], "n_i": 230, "n_o": 230, "simulated_ann": 230, "incycl": [230, 231], "amount": [231, 496, 504, 505, 508, 676, 786, 1042, 1296, 1424, 1425], "minima": 231, 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"module-networkx.algorithms.community.label_propagation"]], "Louvain Community Detection": [[125, "module-networkx.algorithms.community.louvain"]], "Fluid Communities": [[125, "module-networkx.algorithms.community.asyn_fluid"]], "Measuring partitions": [[125, "module-networkx.algorithms.community.quality"]], "Partitions via centrality measures": [[125, "module-networkx.algorithms.community.centrality"]], "Validating partitions": [[125, "module-networkx.algorithms.community.community_utils"]], "Components": [[126, "module-networkx.algorithms.components"]], "Strong connectivity": [[126, "strong-connectivity"]], "Weak connectivity": [[126, "weak-connectivity"]], "Attracting components": [[126, "attracting-components"]], "Biconnected components": [[126, "biconnected-components"]], "Semiconnectedness": [[126, "semiconnectedness"]], "Edge-augmentation": [[127, "module-networkx.algorithms.connectivity.edge_augmentation"]], "See Also": [[127, "see-also"], [762, "see-also"], [1041, "see-also"], [1041, "id2"], [1042, "see-also"], [1042, "id3"], [1042, "id5"]], "K-edge-components": [[127, "module-networkx.algorithms.connectivity.edge_kcomponents"]], "K-node-components": [[127, "module-networkx.algorithms.connectivity.kcomponents"]], "K-node-cutsets": [[127, "module-networkx.algorithms.connectivity.kcutsets"]], "Flow-based disjoint paths": [[127, "module-networkx.algorithms.connectivity.disjoint_paths"]], "Flow-based Connectivity": [[127, "module-networkx.algorithms.connectivity.connectivity"]], "Flow-based Minimum Cuts": [[127, "module-networkx.algorithms.connectivity.cuts"]], "Stoer-Wagner minimum cut": [[127, "module-networkx.algorithms.connectivity.stoerwagner"]], "Utils for flow-based connectivity": [[127, "module-networkx.algorithms.connectivity.utils"]], "Cores": [[128, "module-networkx.algorithms.core"]], "Cuts": [[130, "module-networkx.algorithms.cuts"]], "Cycles": [[131, "module-networkx.algorithms.cycles"]], "D-Separation": [[132, "module-networkx.algorithms.d_separation"]], "Blocking paths": [[132, "blocking-paths"]], "Illustration of D-separation with examples": [[132, "illustration-of-d-separation-with-examples"]], "D-separation and its applications in probability": [[132, "d-separation-and-its-applications-in-probability"]], "Examples": [[132, "examples"], [760, "examples"], [762, "examples"], [1041, "examples"], [1041, "id1"], [1042, "examples"], [1042, "id2"], [1042, "id4"], [1387, "examples"], [1393, "examples"], [1394, "examples"], [1402, "examples"], [1406, "examples"], [1406, "id29"], [1406, "id32"], [1406, "id35"], [1406, "id44"], [1406, "id47"], [1406, "id50"], [1406, "id53"], [1406, "id57"], [1406, "id60"], [1406, "id63"], [1406, "id66"], [1406, "id70"], [1406, "id74"]], "Directed Acyclic Graphs": [[133, "module-networkx.algorithms.dag"]], "Distance-Regular Graphs": [[135, "module-networkx.algorithms.distance_regular"]], "Dominance": [[136, "module-networkx.algorithms.dominance"]], "Dominating Sets": [[137, "module-networkx.algorithms.dominating"]], "Efficiency": [[138, "module-networkx.algorithms.efficiency_measures"]], "Eulerian": [[139, "module-networkx.algorithms.euler"]], "Flows": [[140, "module-networkx.algorithms.flow"]], "Maximum Flow": [[140, "maximum-flow"]], "Edmonds-Karp": [[140, "edmonds-karp"]], "Shortest Augmenting Path": [[140, "shortest-augmenting-path"]], "Preflow-Push": [[140, "preflow-push"]], "Dinitz": [[140, "dinitz"]], "Boykov-Kolmogorov": [[140, "boykov-kolmogorov"]], "Gomory-Hu Tree": [[140, "gomory-hu-tree"]], "Utils": [[140, "utils"]], "Network Simplex": [[140, "network-simplex"]], "Capacity Scaling Minimum Cost Flow": [[140, "capacity-scaling-minimum-cost-flow"]], "EdgeComponentAuxGraph.construct": [[141, "edgecomponentauxgraph-construct"]], "EdgeComponentAuxGraph.k_edge_components": [[142, "edgecomponentauxgraph-k-edge-components"]], "EdgeComponentAuxGraph.k_edge_subgraphs": [[143, "edgecomponentauxgraph-k-edge-subgraphs"]], "ISMAGS.analyze_symmetry": [[144, "ismags-analyze-symmetry"]], "ISMAGS.find_isomorphisms": [[145, "ismags-find-isomorphisms"]], "ISMAGS.is_isomorphic": [[146, "ismags-is-isomorphic"]], "ISMAGS.isomorphisms_iter": [[147, "ismags-isomorphisms-iter"]], "ISMAGS.largest_common_subgraph": [[148, "ismags-largest-common-subgraph"]], "ISMAGS.subgraph_is_isomorphic": [[149, "ismags-subgraph-is-isomorphic"]], "ISMAGS.subgraph_isomorphisms_iter": [[150, "ismags-subgraph-isomorphisms-iter"]], "PlanarEmbedding.add_edge": [[151, "planarembedding-add-edge"]], "PlanarEmbedding.add_edges_from": [[152, "planarembedding-add-edges-from"]], "PlanarEmbedding.add_half_edge_ccw": [[153, "planarembedding-add-half-edge-ccw"]], "PlanarEmbedding.add_half_edge_cw": [[154, "planarembedding-add-half-edge-cw"]], "PlanarEmbedding.add_half_edge_first": [[155, "planarembedding-add-half-edge-first"]], "PlanarEmbedding.add_node": [[156, "planarembedding-add-node"]], "PlanarEmbedding.add_nodes_from": [[157, "planarembedding-add-nodes-from"]], "PlanarEmbedding.add_weighted_edges_from": [[158, "planarembedding-add-weighted-edges-from"]], "PlanarEmbedding.adj": [[159, "planarembedding-adj"]], "PlanarEmbedding.adjacency": [[160, "planarembedding-adjacency"]], "PlanarEmbedding.check_structure": [[161, "planarembedding-check-structure"]], "PlanarEmbedding.clear": [[162, "planarembedding-clear"]], "PlanarEmbedding.clear_edges": [[163, "planarembedding-clear-edges"]], "PlanarEmbedding.connect_components": [[164, "planarembedding-connect-components"]], "PlanarEmbedding.copy": [[165, "planarembedding-copy"]], "PlanarEmbedding.degree": [[166, "planarembedding-degree"]], "PlanarEmbedding.edge_subgraph": [[167, "planarembedding-edge-subgraph"]], "PlanarEmbedding.edges": [[168, "planarembedding-edges"]], "PlanarEmbedding.get_data": [[169, "planarembedding-get-data"]], "PlanarEmbedding.get_edge_data": [[170, "planarembedding-get-edge-data"]], "PlanarEmbedding.has_edge": [[171, "planarembedding-has-edge"]], "PlanarEmbedding.has_node": [[172, "planarembedding-has-node"]], "PlanarEmbedding.has_predecessor": [[173, "planarembedding-has-predecessor"]], "PlanarEmbedding.has_successor": [[174, "planarembedding-has-successor"]], "PlanarEmbedding.in_degree": [[175, "planarembedding-in-degree"]], "PlanarEmbedding.in_edges": [[176, "planarembedding-in-edges"]], "PlanarEmbedding.is_directed": [[177, "planarembedding-is-directed"]], "PlanarEmbedding.is_multigraph": [[178, "planarembedding-is-multigraph"]], "PlanarEmbedding.name": [[179, "planarembedding-name"]], "PlanarEmbedding.nbunch_iter": [[180, "planarembedding-nbunch-iter"]], "PlanarEmbedding.neighbors": [[181, "planarembedding-neighbors"]], "PlanarEmbedding.neighbors_cw_order": [[182, "planarembedding-neighbors-cw-order"]], "PlanarEmbedding.next_face_half_edge": [[183, "planarembedding-next-face-half-edge"]], "PlanarEmbedding.nodes": [[184, "planarembedding-nodes"]], "PlanarEmbedding.number_of_edges": [[185, "planarembedding-number-of-edges"]], "PlanarEmbedding.number_of_nodes": [[186, "planarembedding-number-of-nodes"]], "PlanarEmbedding.order": [[187, "planarembedding-order"]], "PlanarEmbedding.out_degree": [[188, "planarembedding-out-degree"]], "PlanarEmbedding.out_edges": [[189, "planarembedding-out-edges"]], "PlanarEmbedding.pred": [[190, "planarembedding-pred"]], "PlanarEmbedding.predecessors": [[191, "planarembedding-predecessors"]], "PlanarEmbedding.remove_edge": [[192, "planarembedding-remove-edge"]], "PlanarEmbedding.remove_edges_from": [[193, "planarembedding-remove-edges-from"]], "PlanarEmbedding.remove_node": [[194, "planarembedding-remove-node"]], "PlanarEmbedding.remove_nodes_from": [[195, "planarembedding-remove-nodes-from"]], "PlanarEmbedding.reverse": [[196, "planarembedding-reverse"]], "PlanarEmbedding.set_data": [[197, "planarembedding-set-data"]], "PlanarEmbedding.size": [[198, "planarembedding-size"]], "PlanarEmbedding.subgraph": [[199, "planarembedding-subgraph"]], "PlanarEmbedding.succ": [[200, "planarembedding-succ"]], "PlanarEmbedding.successors": [[201, "planarembedding-successors"]], "PlanarEmbedding.to_directed": [[202, "planarembedding-to-directed"]], "PlanarEmbedding.to_directed_class": [[203, "planarembedding-to-directed-class"]], "PlanarEmbedding.to_undirected": [[204, "planarembedding-to-undirected"]], "PlanarEmbedding.to_undirected_class": [[205, "planarembedding-to-undirected-class"]], "PlanarEmbedding.traverse_face": [[206, "planarembedding-traverse-face"]], "PlanarEmbedding.update": [[207, "planarembedding-update"]], "Edmonds.find_optimum": [[208, "edmonds-find-optimum"]], "clique_removal": [[209, "clique-removal"]], "large_clique_size": [[210, "large-clique-size"]], "max_clique": [[211, "max-clique"]], "maximum_independent_set": [[212, "maximum-independent-set"]], "average_clustering": [[213, "average-clustering"], [260, "average-clustering"], [355, "average-clustering"]], "all_pairs_node_connectivity": [[214, "all-pairs-node-connectivity"], [408, "all-pairs-node-connectivity"]], "local_node_connectivity": [[215, "local-node-connectivity"], [412, "local-node-connectivity"]], "node_connectivity": [[216, "node-connectivity"], [413, "node-connectivity"]], "diameter": [[217, "diameter"], [472, "diameter"]], "min_edge_dominating_set": [[218, "min-edge-dominating-set"]], "min_weighted_dominating_set": [[219, "min-weighted-dominating-set"]], "k_components": [[220, "k-components"], [427, "k-components"]], "min_maximal_matching": [[221, "min-maximal-matching"]], "one_exchange": [[222, "one-exchange"]], "randomized_partitioning": [[223, "randomized-partitioning"]], "ramsey_R2": [[224, "ramsey-r2"]], "metric_closure": [[225, "metric-closure"]], "steiner_tree": [[226, "steiner-tree"]], "asadpour_atsp": [[227, "asadpour-atsp"]], "christofides": [[228, "christofides"]], "greedy_tsp": [[229, "greedy-tsp"]], "simulated_annealing_tsp": [[230, "simulated-annealing-tsp"]], "threshold_accepting_tsp": [[231, "threshold-accepting-tsp"]], "traveling_salesman_problem": [[232, "traveling-salesman-problem"]], "treewidth_min_degree": [[233, "treewidth-min-degree"]], "treewidth_min_fill_in": [[234, "treewidth-min-fill-in"]], "min_weighted_vertex_cover": [[235, "min-weighted-vertex-cover"]], "attribute_assortativity_coefficient": [[236, "attribute-assortativity-coefficient"]], "attribute_mixing_dict": [[237, "attribute-mixing-dict"]], "attribute_mixing_matrix": [[238, "attribute-mixing-matrix"]], "average_degree_connectivity": [[239, "average-degree-connectivity"]], "average_neighbor_degree": [[240, "average-neighbor-degree"]], "degree_assortativity_coefficient": [[241, "degree-assortativity-coefficient"]], "degree_mixing_dict": [[242, "degree-mixing-dict"]], "degree_mixing_matrix": [[243, "degree-mixing-matrix"]], "degree_pearson_correlation_coefficient": [[244, "degree-pearson-correlation-coefficient"]], "mixing_dict": [[245, "mixing-dict"]], "node_attribute_xy": [[246, "node-attribute-xy"]], "node_degree_xy": [[247, "node-degree-xy"]], "numeric_assortativity_coefficient": [[248, "numeric-assortativity-coefficient"]], "find_asteroidal_triple": [[249, "find-asteroidal-triple"]], "is_at_free": [[250, "is-at-free"]], "color": [[251, "color"]], "degrees": [[252, "degrees"]], "density": [[253, "density"], [1060, "density"]], "is_bipartite": [[254, "is-bipartite"]], "is_bipartite_node_set": [[255, "is-bipartite-node-set"]], "sets": [[256, "sets"]], "betweenness_centrality": [[257, "betweenness-centrality"], [297, "betweenness-centrality"]], "closeness_centrality": [[258, "closeness-centrality"], [299, "closeness-centrality"]], "degree_centrality": [[259, "degree-centrality"], [304, "degree-centrality"]], "clustering": [[261, "clustering"], [356, "clustering"]], "latapy_clustering": [[262, "latapy-clustering"]], "robins_alexander_clustering": [[263, "robins-alexander-clustering"]], "min_edge_cover": [[264, "min-edge-cover"], [440, "min-edge-cover"]], "generate_edgelist": [[265, "generate-edgelist"], [1335, "generate-edgelist"]], "parse_edgelist": [[266, "parse-edgelist"], [1336, "parse-edgelist"]], "read_edgelist": [[267, "read-edgelist"], [1337, "read-edgelist"]], "write_edgelist": [[268, "write-edgelist"], [1339, "write-edgelist"]], "alternating_havel_hakimi_graph": [[269, "alternating-havel-hakimi-graph"]], "complete_bipartite_graph": [[270, "complete-bipartite-graph"]], "configuration_model": [[271, "configuration-model"], [1175, "configuration-model"]], "gnmk_random_graph": [[272, "gnmk-random-graph"]], "havel_hakimi_graph": [[273, "havel-hakimi-graph"], [1180, "havel-hakimi-graph"]], "preferential_attachment_graph": [[274, "preferential-attachment-graph"]], "random_graph": [[275, "random-graph"]], "reverse_havel_hakimi_graph": [[276, "reverse-havel-hakimi-graph"]], "eppstein_matching": [[277, "eppstein-matching"]], "hopcroft_karp_matching": [[278, "hopcroft-karp-matching"]], "maximum_matching": [[279, "maximum-matching"]], "minimum_weight_full_matching": [[280, "minimum-weight-full-matching"]], "to_vertex_cover": [[281, "to-vertex-cover"]], "biadjacency_matrix": [[282, "biadjacency-matrix"]], "from_biadjacency_matrix": [[283, "from-biadjacency-matrix"]], "collaboration_weighted_projected_graph": [[284, "collaboration-weighted-projected-graph"]], "generic_weighted_projected_graph": [[285, "generic-weighted-projected-graph"]], "overlap_weighted_projected_graph": [[286, "overlap-weighted-projected-graph"]], "projected_graph": [[287, "projected-graph"]], "weighted_projected_graph": [[288, "weighted-projected-graph"]], "node_redundancy": [[289, "node-redundancy"]], "spectral_bipartivity": [[290, "spectral-bipartivity"]], "edge_boundary": [[291, "edge-boundary"]], "node_boundary": [[292, "node-boundary"]], "bridges": [[293, "bridges"]], "has_bridges": [[294, "has-bridges"]], "local_bridges": [[295, "local-bridges"]], "approximate_current_flow_betweenness_centrality": [[296, "approximate-current-flow-betweenness-centrality"]], "betweenness_centrality_subset": [[298, "betweenness-centrality-subset"]], "communicability_betweenness_centrality": [[300, "communicability-betweenness-centrality"]], "current_flow_betweenness_centrality": [[301, "current-flow-betweenness-centrality"]], "current_flow_betweenness_centrality_subset": [[302, "current-flow-betweenness-centrality-subset"]], "current_flow_closeness_centrality": [[303, "current-flow-closeness-centrality"]], "dispersion": [[305, "dispersion"]], "edge_betweenness_centrality": [[306, "edge-betweenness-centrality"]], "edge_betweenness_centrality_subset": [[307, "edge-betweenness-centrality-subset"]], "edge_current_flow_betweenness_centrality": [[308, "edge-current-flow-betweenness-centrality"]], "edge_current_flow_betweenness_centrality_subset": [[309, "edge-current-flow-betweenness-centrality-subset"]], "edge_load_centrality": [[310, "edge-load-centrality"]], "eigenvector_centrality": [[311, "eigenvector-centrality"]], "eigenvector_centrality_numpy": [[312, "eigenvector-centrality-numpy"]], "estrada_index": [[313, "estrada-index"]], "global_reaching_centrality": [[314, "global-reaching-centrality"]], "group_betweenness_centrality": [[315, "group-betweenness-centrality"]], "group_closeness_centrality": [[316, "group-closeness-centrality"]], "group_degree_centrality": [[317, "group-degree-centrality"]], "group_in_degree_centrality": [[318, "group-in-degree-centrality"]], "group_out_degree_centrality": [[319, "group-out-degree-centrality"]], "harmonic_centrality": [[320, "harmonic-centrality"]], "in_degree_centrality": [[321, "in-degree-centrality"]], "incremental_closeness_centrality": [[322, "incremental-closeness-centrality"]], "information_centrality": [[323, "information-centrality"]], "katz_centrality": [[324, "katz-centrality"]], "katz_centrality_numpy": [[325, "katz-centrality-numpy"]], "load_centrality": [[326, "load-centrality"]], "local_reaching_centrality": [[327, "local-reaching-centrality"]], "out_degree_centrality": [[328, "out-degree-centrality"]], "percolation_centrality": [[329, "percolation-centrality"]], "prominent_group": [[330, "prominent-group"]], "second_order_centrality": [[331, "second-order-centrality"]], "subgraph_centrality": [[332, "subgraph-centrality"]], "subgraph_centrality_exp": [[333, "subgraph-centrality-exp"]], "trophic_differences": [[334, "trophic-differences"]], "trophic_incoherence_parameter": [[335, "trophic-incoherence-parameter"]], "trophic_levels": [[336, "trophic-levels"]], "voterank": [[337, "voterank"]], "chain_decomposition": [[338, "chain-decomposition"]], "chordal_graph_cliques": [[339, "chordal-graph-cliques"]], "chordal_graph_treewidth": [[340, "chordal-graph-treewidth"]], "complete_to_chordal_graph": [[341, "complete-to-chordal-graph"]], "find_induced_nodes": [[342, "find-induced-nodes"]], "is_chordal": [[343, "is-chordal"]], "cliques_containing_node": [[344, "cliques-containing-node"]], "enumerate_all_cliques": [[345, "enumerate-all-cliques"]], "find_cliques": [[346, "find-cliques"]], "find_cliques_recursive": [[347, "find-cliques-recursive"]], "graph_clique_number": [[348, "graph-clique-number"]], "graph_number_of_cliques": [[349, "graph-number-of-cliques"]], "make_clique_bipartite": [[350, "make-clique-bipartite"]], "make_max_clique_graph": [[351, "make-max-clique-graph"]], "max_weight_clique": [[352, "max-weight-clique"]], "node_clique_number": [[353, "node-clique-number"]], "number_of_cliques": [[354, "number-of-cliques"]], "generalized_degree": [[357, "generalized-degree"]], "square_clustering": [[358, "square-clustering"]], "transitivity": [[359, "transitivity"]], "triangles": [[360, "triangles"]], "equitable_color": [[361, "equitable-color"]], "greedy_color": [[362, "greedy-color"]], "strategy_connected_sequential": [[363, "strategy-connected-sequential"]], "strategy_connected_sequential_bfs": [[364, "strategy-connected-sequential-bfs"]], "strategy_connected_sequential_dfs": [[365, "strategy-connected-sequential-dfs"]], "strategy_independent_set": [[366, "strategy-independent-set"]], "strategy_largest_first": [[367, "strategy-largest-first"]], "strategy_random_sequential": [[368, "strategy-random-sequential"]], "strategy_saturation_largest_first": [[369, "strategy-saturation-largest-first"]], "strategy_smallest_last": [[370, "strategy-smallest-last"]], "communicability": [[371, "communicability"]], "communicability_exp": [[372, "communicability-exp"]], "asyn_fluidc": [[373, "asyn-fluidc"]], "girvan_newman": [[374, "girvan-newman"]], "is_partition": [[375, "is-partition"]], "k_clique_communities": [[376, "k-clique-communities"]], "kernighan_lin_bisection": [[377, "kernighan-lin-bisection"]], "asyn_lpa_communities": [[378, "asyn-lpa-communities"]], "label_propagation_communities": [[379, "label-propagation-communities"]], "louvain_communities": [[380, "louvain-communities"]], "louvain_partitions": [[381, 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[[457, "antichains"]], "dag_longest_path": [[458, "dag-longest-path"]], "dag_longest_path_length": [[459, "dag-longest-path-length"]], "dag_to_branching": [[460, "dag-to-branching"]], "descendants": [[461, "descendants"]], "is_aperiodic": [[462, "is-aperiodic"]], "is_directed_acyclic_graph": [[463, "is-directed-acyclic-graph"]], "lexicographical_topological_sort": [[464, "lexicographical-topological-sort"]], "topological_generations": [[465, "topological-generations"]], "topological_sort": [[466, "topological-sort"]], "transitive_closure": [[467, "transitive-closure"]], "transitive_closure_dag": [[468, "transitive-closure-dag"]], "transitive_reduction": [[469, "transitive-reduction"]], "barycenter": [[470, "barycenter"]], "center": [[471, "center"]], "eccentricity": [[473, "eccentricity"]], "periphery": [[474, "periphery"]], "radius": [[475, "radius"]], "resistance_distance": [[476, "resistance-distance"]], "global_parameters": [[477, "global-parameters"]], "intersection_array": [[478, "intersection-array"]], "is_distance_regular": [[479, "is-distance-regular"]], "is_strongly_regular": [[480, "is-strongly-regular"]], "dominance_frontiers": [[481, "dominance-frontiers"]], "immediate_dominators": [[482, "immediate-dominators"]], "dominating_set": [[483, "dominating-set"]], "is_dominating_set": [[484, "is-dominating-set"]], "efficiency": [[485, "efficiency"]], "global_efficiency": [[486, "global-efficiency"]], "local_efficiency": [[487, "local-efficiency"]], "eulerian_circuit": [[488, "eulerian-circuit"]], "eulerian_path": [[489, "eulerian-path"]], "eulerize": [[490, "eulerize"]], "has_eulerian_path": [[491, "has-eulerian-path"]], "is_eulerian": [[492, "is-eulerian"]], "is_semieulerian": [[493, "is-semieulerian"]], "boykov_kolmogorov": [[494, "boykov-kolmogorov"]], "build_residual_network": [[495, "build-residual-network"]], "capacity_scaling": [[496, "capacity-scaling"]], "cost_of_flow": [[497, "cost-of-flow"]], "dinitz": [[498, "dinitz"]], "edmonds_karp": [[499, 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"is_valid_degree_sequence_havel_hakimi": [[518, "is-valid-degree-sequence-havel-hakimi"]], "flow_hierarchy": [[519, "flow-hierarchy"]], "is_kl_connected": [[520, "is-kl-connected"]], "kl_connected_subgraph": [[521, "kl-connected-subgraph"]], "is_isolate": [[522, "is-isolate"]], "isolates": [[523, "isolates"]], "number_of_isolates": [[524, "number-of-isolates"]], "DiGraphMatcher.__init__": [[525, "digraphmatcher-init"]], "DiGraphMatcher.candidate_pairs_iter": [[526, "digraphmatcher-candidate-pairs-iter"]], "DiGraphMatcher.initialize": [[527, "digraphmatcher-initialize"]], "DiGraphMatcher.is_isomorphic": [[528, "digraphmatcher-is-isomorphic"]], "DiGraphMatcher.isomorphisms_iter": [[529, "digraphmatcher-isomorphisms-iter"]], "DiGraphMatcher.match": [[530, "digraphmatcher-match"]], "DiGraphMatcher.semantic_feasibility": [[531, "digraphmatcher-semantic-feasibility"]], "DiGraphMatcher.subgraph_is_isomorphic": [[532, "digraphmatcher-subgraph-is-isomorphic"]], "DiGraphMatcher.subgraph_isomorphisms_iter": [[533, "digraphmatcher-subgraph-isomorphisms-iter"]], "DiGraphMatcher.syntactic_feasibility": [[534, "digraphmatcher-syntactic-feasibility"]], "GraphMatcher.__init__": [[535, "graphmatcher-init"]], "GraphMatcher.candidate_pairs_iter": [[536, "graphmatcher-candidate-pairs-iter"]], "GraphMatcher.initialize": [[537, "graphmatcher-initialize"]], "GraphMatcher.is_isomorphic": [[538, "graphmatcher-is-isomorphic"]], "GraphMatcher.isomorphisms_iter": [[539, "graphmatcher-isomorphisms-iter"]], "GraphMatcher.match": [[540, "graphmatcher-match"]], "GraphMatcher.semantic_feasibility": [[541, "graphmatcher-semantic-feasibility"]], "GraphMatcher.subgraph_is_isomorphic": [[542, "graphmatcher-subgraph-is-isomorphic"]], "GraphMatcher.subgraph_isomorphisms_iter": [[543, "graphmatcher-subgraph-isomorphisms-iter"]], "GraphMatcher.syntactic_feasibility": [[544, "graphmatcher-syntactic-feasibility"]], "networkx.algorithms.isomorphism.ISMAGS": [[545, "networkx-algorithms-isomorphism-ismags"]], "categorical_edge_match": [[546, "categorical-edge-match"]], "categorical_multiedge_match": [[547, "categorical-multiedge-match"]], "categorical_node_match": [[548, "categorical-node-match"]], "could_be_isomorphic": [[549, "could-be-isomorphic"]], "fast_could_be_isomorphic": [[550, "fast-could-be-isomorphic"]], "faster_could_be_isomorphic": [[551, "faster-could-be-isomorphic"]], "generic_edge_match": [[552, "generic-edge-match"]], "generic_multiedge_match": [[553, "generic-multiedge-match"]], "generic_node_match": [[554, "generic-node-match"]], "is_isomorphic": [[555, "is-isomorphic"]], "numerical_edge_match": [[556, "numerical-edge-match"]], "numerical_multiedge_match": [[557, "numerical-multiedge-match"]], "numerical_node_match": [[558, "numerical-node-match"]], "rooted_tree_isomorphism": [[559, "rooted-tree-isomorphism"]], "tree_isomorphism": [[560, "tree-isomorphism"]], "vf2pp_all_isomorphisms": [[561, "vf2pp-all-isomorphisms"]], "vf2pp_is_isomorphic": [[562, "vf2pp-is-isomorphic"]], "vf2pp_isomorphism": [[563, "vf2pp-isomorphism"]], "hits": [[564, "hits"]], "google_matrix": [[565, "google-matrix"]], "pagerank": [[566, "pagerank"]], "adamic_adar_index": [[567, "adamic-adar-index"]], "cn_soundarajan_hopcroft": [[568, "cn-soundarajan-hopcroft"]], "common_neighbor_centrality": [[569, "common-neighbor-centrality"]], "jaccard_coefficient": [[570, "jaccard-coefficient"]], "preferential_attachment": [[571, "preferential-attachment"]], "ra_index_soundarajan_hopcroft": [[572, "ra-index-soundarajan-hopcroft"]], "resource_allocation_index": [[573, "resource-allocation-index"]], "within_inter_cluster": [[574, "within-inter-cluster"]], "all_pairs_lowest_common_ancestor": [[575, "all-pairs-lowest-common-ancestor"]], "lowest_common_ancestor": [[576, "lowest-common-ancestor"]], "tree_all_pairs_lowest_common_ancestor": [[577, "tree-all-pairs-lowest-common-ancestor"]], "is_matching": [[578, "is-matching"]], "is_maximal_matching": [[579, "is-maximal-matching"]], "is_perfect_matching": [[580, "is-perfect-matching"]], "max_weight_matching": [[581, "max-weight-matching"]], "maximal_matching": [[582, "maximal-matching"]], "min_weight_matching": [[583, "min-weight-matching"]], "contracted_edge": [[584, "contracted-edge"]], "contracted_nodes": [[585, "contracted-nodes"]], "equivalence_classes": [[586, "equivalence-classes"]], "identified_nodes": [[587, "identified-nodes"]], "quotient_graph": [[588, "quotient-graph"]], "maximal_independent_set": [[589, "maximal-independent-set"]], "moral_graph": [[590, "moral-graph"]], "harmonic_function": [[591, "harmonic-function"]], "local_and_global_consistency": [[592, "local-and-global-consistency"]], "non_randomness": [[593, "non-randomness"]], "compose_all": [[594, "compose-all"]], "disjoint_union_all": [[595, "disjoint-union-all"]], "intersection_all": [[596, "intersection-all"]], "union_all": [[597, "union-all"]], "compose": [[598, "compose"]], "difference": [[599, "difference"]], "disjoint_union": [[600, "disjoint-union"]], "full_join": [[601, "full-join"]], "intersection": [[602, "intersection"]], "symmetric_difference": [[603, "symmetric-difference"]], "union": [[604, "union"]], "cartesian_product": [[605, "cartesian-product"]], "corona_product": [[606, "corona-product"]], "lexicographic_product": [[607, "lexicographic-product"]], "power": [[608, "power"]], "rooted_product": [[609, "rooted-product"]], "strong_product": [[610, "strong-product"]], "tensor_product": [[611, "tensor-product"]], "complement": [[612, "complement"]], "reverse": [[613, "reverse"]], "combinatorial_embedding_to_pos": [[614, "combinatorial-embedding-to-pos"]], "networkx.algorithms.planarity.PlanarEmbedding": [[615, "networkx-algorithms-planarity-planarembedding"]], "check_planarity": [[616, "check-planarity"]], "is_planar": [[617, "is-planar"]], "chromatic_polynomial": [[618, "chromatic-polynomial"]], "tutte_polynomial": [[619, "tutte-polynomial"]], "overall_reciprocity": [[620, "overall-reciprocity"]], "reciprocity": [[621, "reciprocity"]], "is_k_regular": [[622, "is-k-regular"]], "is_regular": [[623, "is-regular"]], "k_factor": [[624, "k-factor"]], "rich_club_coefficient": [[625, "rich-club-coefficient"]], "astar_path": [[626, "astar-path"]], "astar_path_length": [[627, "astar-path-length"]], "floyd_warshall": [[628, "floyd-warshall"]], "floyd_warshall_numpy": [[629, "floyd-warshall-numpy"]], "floyd_warshall_predecessor_and_distance": [[630, "floyd-warshall-predecessor-and-distance"]], "reconstruct_path": [[631, "reconstruct-path"]], "all_shortest_paths": [[632, "all-shortest-paths"]], "average_shortest_path_length": [[633, "average-shortest-path-length"]], "has_path": [[634, "has-path"]], "shortest_path": [[635, "shortest-path"]], "shortest_path_length": [[636, "shortest-path-length"]], "all_pairs_shortest_path": [[637, "all-pairs-shortest-path"]], "all_pairs_shortest_path_length": [[638, "all-pairs-shortest-path-length"]], "bidirectional_shortest_path": [[639, "bidirectional-shortest-path"]], "predecessor": [[640, "predecessor"]], "single_source_shortest_path": [[641, "single-source-shortest-path"]], "single_source_shortest_path_length": [[642, "single-source-shortest-path-length"]], "single_target_shortest_path": [[643, "single-target-shortest-path"]], "single_target_shortest_path_length": [[644, "single-target-shortest-path-length"]], "all_pairs_bellman_ford_path": [[645, "all-pairs-bellman-ford-path"]], "all_pairs_bellman_ford_path_length": [[646, "all-pairs-bellman-ford-path-length"]], "all_pairs_dijkstra": [[647, "all-pairs-dijkstra"]], "all_pairs_dijkstra_path": [[648, "all-pairs-dijkstra-path"]], "all_pairs_dijkstra_path_length": [[649, "all-pairs-dijkstra-path-length"]], "bellman_ford_path": [[650, "bellman-ford-path"]], "bellman_ford_path_length": [[651, "bellman-ford-path-length"]], "bellman_ford_predecessor_and_distance": [[652, "bellman-ford-predecessor-and-distance"]], "bidirectional_dijkstra": [[653, "bidirectional-dijkstra"]], "dijkstra_path": [[654, "dijkstra-path"]], "dijkstra_path_length": [[655, "dijkstra-path-length"]], "dijkstra_predecessor_and_distance": [[656, "dijkstra-predecessor-and-distance"]], "find_negative_cycle": [[657, "find-negative-cycle"]], "goldberg_radzik": [[658, "goldberg-radzik"]], "johnson": [[659, "johnson"]], "multi_source_dijkstra": [[660, "multi-source-dijkstra"]], "multi_source_dijkstra_path": [[661, "multi-source-dijkstra-path"]], "multi_source_dijkstra_path_length": [[662, "multi-source-dijkstra-path-length"]], "negative_edge_cycle": [[663, "negative-edge-cycle"]], "single_source_bellman_ford": [[664, "single-source-bellman-ford"]], "single_source_bellman_ford_path": [[665, "single-source-bellman-ford-path"]], "single_source_bellman_ford_path_length": [[666, "single-source-bellman-ford-path-length"]], "single_source_dijkstra": [[667, "single-source-dijkstra"]], "single_source_dijkstra_path": [[668, "single-source-dijkstra-path"]], "single_source_dijkstra_path_length": [[669, "single-source-dijkstra-path-length"]], "generate_random_paths": [[670, "generate-random-paths"]], "graph_edit_distance": [[671, "graph-edit-distance"]], "optimal_edit_paths": [[672, "optimal-edit-paths"]], "optimize_edit_paths": [[673, "optimize-edit-paths"]], "optimize_graph_edit_distance": [[674, "optimize-graph-edit-distance"]], "panther_similarity": [[675, "panther-similarity"]], "simrank_similarity": [[676, "simrank-similarity"]], "all_simple_edge_paths": [[677, "all-simple-edge-paths"]], "all_simple_paths": [[678, "all-simple-paths"]], "is_simple_path": [[679, "is-simple-path"]], "shortest_simple_paths": [[680, "shortest-simple-paths"]], "lattice_reference": [[681, "lattice-reference"]], "omega": [[682, "omega"]], "random_reference": [[683, "random-reference"]], "sigma": [[684, "sigma"]], "s_metric": [[685, "s-metric"]], "spanner": [[686, "spanner"]], "constraint": [[687, "constraint"]], "effective_size": [[688, "effective-size"]], "local_constraint": [[689, "local-constraint"]], "dedensify": [[690, "dedensify"]], "snap_aggregation": [[691, "snap-aggregation"]], "connected_double_edge_swap": [[692, "connected-double-edge-swap"]], "directed_edge_swap": [[693, "directed-edge-swap"]], "double_edge_swap": [[694, "double-edge-swap"]], "find_threshold_graph": [[695, "find-threshold-graph"]], "is_threshold_graph": [[696, "is-threshold-graph"]], "hamiltonian_path": [[697, "hamiltonian-path"]], "is_reachable": [[698, "is-reachable"]], "is_tournament": [[700, "is-tournament"]], "random_tournament": [[701, "random-tournament"]], "score_sequence": [[702, "score-sequence"]], "bfs_beam_edges": [[703, "bfs-beam-edges"]], "bfs_edges": [[704, "bfs-edges"]], "bfs_layers": [[705, "bfs-layers"]], "bfs_predecessors": [[706, "bfs-predecessors"]], "bfs_successors": [[707, "bfs-successors"]], "bfs_tree": [[708, "bfs-tree"]], "descendants_at_distance": [[709, "descendants-at-distance"]], "dfs_edges": [[710, "dfs-edges"]], "dfs_labeled_edges": [[711, "dfs-labeled-edges"]], "dfs_postorder_nodes": [[712, "dfs-postorder-nodes"]], "dfs_predecessors": [[713, "dfs-predecessors"]], "dfs_preorder_nodes": [[714, "dfs-preorder-nodes"]], "dfs_successors": [[715, "dfs-successors"]], "dfs_tree": [[716, "dfs-tree"]], "edge_bfs": [[717, "edge-bfs"]], "edge_dfs": [[718, "edge-dfs"]], "networkx.algorithms.tree.branchings.ArborescenceIterator": [[719, "networkx-algorithms-tree-branchings-arborescenceiterator"]], "networkx.algorithms.tree.branchings.Edmonds": [[720, "networkx-algorithms-tree-branchings-edmonds"]], "branching_weight": [[721, "branching-weight"]], "greedy_branching": [[722, "greedy-branching"]], "maximum_branching": [[723, "maximum-branching"]], "maximum_spanning_arborescence": [[724, "maximum-spanning-arborescence"]], "minimum_branching": [[725, "minimum-branching"]], "minimum_spanning_arborescence": [[726, "minimum-spanning-arborescence"]], "NotATree": [[727, "notatree"]], "from_nested_tuple": [[728, "from-nested-tuple"]], "from_prufer_sequence": [[729, "from-prufer-sequence"]], "to_nested_tuple": [[730, "to-nested-tuple"]], "to_prufer_sequence": [[731, "to-prufer-sequence"]], "junction_tree": [[732, "junction-tree"]], "networkx.algorithms.tree.mst.SpanningTreeIterator": [[733, "networkx-algorithms-tree-mst-spanningtreeiterator"]], "maximum_spanning_edges": [[734, "maximum-spanning-edges"]], "maximum_spanning_tree": [[735, "maximum-spanning-tree"]], "minimum_spanning_edges": [[736, "minimum-spanning-edges"]], "minimum_spanning_tree": [[737, "minimum-spanning-tree"]], "random_spanning_tree": [[738, "random-spanning-tree"]], "join": [[739, "join"]], "is_arborescence": [[740, "is-arborescence"]], "is_branching": [[741, "is-branching"]], "is_forest": [[742, "is-forest"]], "is_tree": [[743, "is-tree"]], "all_triads": [[744, "all-triads"]], "all_triplets": [[745, "all-triplets"]], "is_triad": [[746, "is-triad"]], "random_triad": [[747, "random-triad"]], "triad_type": [[748, "triad-type"]], "triadic_census": [[749, "triadic-census"]], "triads_by_type": [[750, "triads-by-type"]], "closeness_vitality": [[751, "closeness-vitality"]], "voronoi_cells": [[752, "voronoi-cells"]], "wiener_index": [[753, "wiener-index"]], "Graph Hashing": [[754, "module-networkx.algorithms.graph_hashing"]], "Graphical degree sequence": [[755, "module-networkx.algorithms.graphical"]], "Hierarchy": [[756, "module-networkx.algorithms.hierarchy"]], "Hybrid": [[757, "module-networkx.algorithms.hybrid"]], "Isolates": [[759, "module-networkx.algorithms.isolate"]], "Isomorphism": [[760, "isomorphism"]], "VF2++": [[760, "module-networkx.algorithms.isomorphism.vf2pp"]], "VF2++ Algorithm": [[760, "vf2-algorithm"]], "Tree Isomorphism": [[760, "module-networkx.algorithms.isomorphism.tree_isomorphism"]], "Advanced Interfaces": [[760, "advanced-interfaces"]], "ISMAGS Algorithm": [[761, "module-networkx.algorithms.isomorphism.ismags"]], "Notes": [[761, "notes"], [762, "notes"]], "ISMAGS object": [[761, "ismags-object"]], "VF2 Algorithm": [[762, "module-networkx.algorithms.isomorphism.isomorphvf2"]], "Subgraph Isomorphism": [[762, "subgraph-isomorphism"]], "Graph Matcher": [[762, "graph-matcher"]], "DiGraph Matcher": [[762, "digraph-matcher"]], "Match helpers": [[762, "match-helpers"]], "Link Analysis": [[763, "link-analysis"]], "PageRank": [[763, "module-networkx.algorithms.link_analysis.pagerank_alg"]], "Hits": [[763, "module-networkx.algorithms.link_analysis.hits_alg"]], "Link Prediction": [[764, "module-networkx.algorithms.link_prediction"]], "Lowest Common Ancestor": [[765, "module-networkx.algorithms.lowest_common_ancestors"]], "Minors": [[767, "module-networkx.algorithms.minors"]], "Maximal independent set": [[768, "module-networkx.algorithms.mis"]], "Moral": [[769, "module-networkx.algorithms.moral"]], "Node Classification": [[770, "module-networkx.algorithms.node_classification"]], "non-randomness": [[771, "module-networkx.algorithms.non_randomness"]], "Operators": [[772, "operators"]], "Planar Drawing": [[773, "module-networkx.algorithms.planar_drawing"]], "Planarity": [[774, "module-networkx.algorithms.planarity"]], "Graph Polynomials": [[775, "module-networkx.algorithms.polynomials"]], "Reciprocity": [[776, "module-networkx.algorithms.reciprocity"]], "Regular": [[777, "module-networkx.algorithms.regular"]], "Rich Club": [[778, "module-networkx.algorithms.richclub"]], "Shortest Paths": [[779, "module-networkx.algorithms.shortest_paths.generic"]], "Advanced Interface": [[779, "module-networkx.algorithms.shortest_paths.unweighted"]], "Dense Graphs": [[779, "module-networkx.algorithms.shortest_paths.dense"]], "A* Algorithm": [[779, "module-networkx.algorithms.shortest_paths.astar"]], "Similarity Measures": [[780, "module-networkx.algorithms.similarity"]], "Simple Paths": [[781, "module-networkx.algorithms.simple_paths"]], "Small-world": [[782, "module-networkx.algorithms.smallworld"]], "s 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"FilterMultiInner.get": [[819, "filtermultiinner-get"]], "FilterMultiInner.items": [[820, "filtermultiinner-items"]], "FilterMultiInner.keys": [[821, "filtermultiinner-keys"]], "FilterMultiInner.values": [[822, "filtermultiinner-values"]], "MultiAdjacencyView.copy": [[823, "multiadjacencyview-copy"]], "MultiAdjacencyView.get": [[824, "multiadjacencyview-get"]], "MultiAdjacencyView.items": [[825, "multiadjacencyview-items"]], "MultiAdjacencyView.keys": [[826, "multiadjacencyview-keys"]], "MultiAdjacencyView.values": [[827, "multiadjacencyview-values"]], "UnionAdjacency.copy": [[828, "unionadjacency-copy"]], "UnionAdjacency.get": [[829, "unionadjacency-get"]], "UnionAdjacency.items": [[830, "unionadjacency-items"]], "UnionAdjacency.keys": [[831, "unionadjacency-keys"]], "UnionAdjacency.values": [[832, "unionadjacency-values"]], "UnionAtlas.copy": [[833, "unionatlas-copy"]], "UnionAtlas.get": [[834, "unionatlas-get"]], "UnionAtlas.items": [[835, "unionatlas-items"]], "UnionAtlas.keys": 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"MultiDiGraph.__iter__": [[932, "multidigraph-iter"]], "MultiDiGraph.__len__": [[933, "multidigraph-len"]], "MultiDiGraph.add_edge": [[934, "multidigraph-add-edge"]], "MultiDiGraph.add_edges_from": [[935, "multidigraph-add-edges-from"]], "MultiDiGraph.add_node": [[936, "multidigraph-add-node"]], "MultiDiGraph.add_nodes_from": [[937, "multidigraph-add-nodes-from"]], "MultiDiGraph.add_weighted_edges_from": [[938, "multidigraph-add-weighted-edges-from"]], "MultiDiGraph.adj": [[939, "multidigraph-adj"]], "MultiDiGraph.adjacency": [[940, "multidigraph-adjacency"]], "MultiDiGraph.clear": [[941, "multidigraph-clear"]], "MultiDiGraph.clear_edges": [[942, "multidigraph-clear-edges"]], "MultiDiGraph.copy": [[943, "multidigraph-copy"]], "MultiDiGraph.degree": [[944, "multidigraph-degree"]], "MultiDiGraph.edge_subgraph": [[945, "multidigraph-edge-subgraph"]], "MultiDiGraph.edges": [[946, "multidigraph-edges"]], "MultiDiGraph.get_edge_data": [[947, "multidigraph-get-edge-data"]], "MultiDiGraph.has_edge": [[948, "multidigraph-has-edge"]], "MultiDiGraph.has_node": [[949, "multidigraph-has-node"]], "MultiDiGraph.in_degree": [[950, "multidigraph-in-degree"]], "MultiDiGraph.in_edges": [[951, "multidigraph-in-edges"]], "MultiDiGraph.nbunch_iter": [[952, "multidigraph-nbunch-iter"]], "MultiDiGraph.neighbors": [[953, "multidigraph-neighbors"]], "MultiDiGraph.new_edge_key": [[954, "multidigraph-new-edge-key"]], "MultiDiGraph.nodes": [[955, "multidigraph-nodes"]], "MultiDiGraph.number_of_edges": [[956, "multidigraph-number-of-edges"]], "MultiDiGraph.number_of_nodes": [[957, "multidigraph-number-of-nodes"]], "MultiDiGraph.order": [[958, "multidigraph-order"]], "MultiDiGraph.out_degree": [[959, "multidigraph-out-degree"]], "MultiDiGraph.out_edges": [[960, "multidigraph-out-edges"]], "MultiDiGraph.predecessors": [[961, "multidigraph-predecessors"]], "MultiDiGraph.remove_edge": [[962, "multidigraph-remove-edge"]], "MultiDiGraph.remove_edges_from": [[963, 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"hide-diedges"]], "hide_edges": [[1024, "hide-edges"]], "hide_multidiedges": [[1025, "hide-multidiedges"]], "hide_multiedges": [[1026, "hide-multiedges"]], "hide_nodes": [[1027, "hide-nodes"]], "no_filter": [[1028, "no-filter"]], "show_diedges": [[1029, "show-diedges"]], "show_edges": [[1030, "show-edges"]], "show_multidiedges": [[1031, "show-multidiedges"]], "show_multiedges": [[1032, "show-multiedges"]], "networkx.classes.filters.show_nodes": [[1033, "networkx-classes-filters-show-nodes"]], "generic_graph_view": [[1034, "generic-graph-view"]], "reverse_view": [[1035, "reverse-view"], [1083, "reverse-view"]], "subgraph_view": [[1036, "subgraph-view"], [1088, "subgraph-view"]], "Graph\u2014Undirected graphs with self loops": [[1037, "graph-undirected-graphs-with-self-loops"]], "Graph types": [[1038, "graph-types"]], "Which graph class should I use?": [[1038, "which-graph-class-should-i-use"]], "Basic graph types": [[1038, "basic-graph-types"]], "Graph Views": [[1038, "module-networkx.classes.graphviews"]], "Core Views": [[1038, "module-networkx.classes.coreviews"]], "Filters": [[1038, "filters"]], "Backends": [[1038, "backends"]], "Create a Dispatcher": [[1038, "create-a-dispatcher"]], "MultiDiGraph\u2014Directed graphs with self loops and parallel edges": [[1039, "multidigraph-directed-graphs-with-self-loops-and-parallel-edges"]], "Adding and Removing Nodes and Edges": [[1039, "adding-and-removing-nodes-and-edges"]], "MultiGraph\u2014Undirected graphs with self loops and parallel edges": [[1040, "multigraph-undirected-graphs-with-self-loops-and-parallel-edges"]], "Converting to and from other data formats": [[1041, "converting-to-and-from-other-data-formats"]], "To NetworkX Graph": [[1041, "module-networkx.convert"]], "Dictionaries": [[1041, "dictionaries"]], "Lists": [[1041, "lists"]], "Numpy": [[1041, "module-networkx.convert_matrix"]], "Scipy": [[1041, "scipy"]], "Pandas": [[1041, "pandas"]], "Matplotlib": [[1042, 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"module-networkx.readwrite.gml"], [1383, "module-networkx.readwrite.graphml"], [1385, "module-networkx.readwrite.json_graph"], [1386, "module-networkx.readwrite.leda"], [1388, "module-networkx.readwrite.multiline_adjlist"], [1389, "module-networkx.readwrite.pajek"], [1390, "module-networkx.readwrite.graph6"], [1390, "module-networkx.readwrite.sparse6"], [1391, "module-networkx.relabel"], [1392, "module-networkx.utils"], [1392, "module-networkx.utils.decorators"], [1392, "module-networkx.utils.mapped_queue"], [1392, "module-networkx.utils.misc"], [1392, "module-networkx.utils.random_sequence"], [1392, "module-networkx.utils.rcm"], [1392, "module-networkx.utils.union_find"]], "networkx.algorithms.approximation": [[112, "module-networkx.algorithms.approximation"]], "networkx.algorithms.approximation.clique": [[112, "module-networkx.algorithms.approximation.clique"]], "networkx.algorithms.approximation.clustering_coefficient": [[112, "module-networkx.algorithms.approximation.clustering_coefficient"]], "networkx.algorithms.approximation.connectivity": [[112, "module-networkx.algorithms.approximation.connectivity"]], "networkx.algorithms.approximation.distance_measures": [[112, "module-networkx.algorithms.approximation.distance_measures"]], "networkx.algorithms.approximation.dominating_set": [[112, "module-networkx.algorithms.approximation.dominating_set"]], "networkx.algorithms.approximation.kcomponents": [[112, "module-networkx.algorithms.approximation.kcomponents"]], "networkx.algorithms.approximation.matching": [[112, "module-networkx.algorithms.approximation.matching"]], "networkx.algorithms.approximation.maxcut": [[112, "module-networkx.algorithms.approximation.maxcut"]], "networkx.algorithms.approximation.ramsey": [[112, "module-networkx.algorithms.approximation.ramsey"]], "networkx.algorithms.approximation.steinertree": [[112, "module-networkx.algorithms.approximation.steinertree"]], "networkx.algorithms.approximation.traveling_salesman": [[112, "module-networkx.algorithms.approximation.traveling_salesman"]], "networkx.algorithms.approximation.treewidth": [[112, "module-networkx.algorithms.approximation.treewidth"]], "networkx.algorithms.approximation.vertex_cover": [[112, "module-networkx.algorithms.approximation.vertex_cover"]], "networkx.algorithms.assortativity": [[113, "module-networkx.algorithms.assortativity"]], "networkx.algorithms.asteroidal": [[114, "module-networkx.algorithms.asteroidal"]], "networkx.algorithms.bipartite": [[115, "module-networkx.algorithms.bipartite"]], "networkx.algorithms.bipartite.basic": [[115, "module-networkx.algorithms.bipartite.basic"]], "networkx.algorithms.bipartite.centrality": [[115, "module-networkx.algorithms.bipartite.centrality"]], "networkx.algorithms.bipartite.cluster": [[115, "module-networkx.algorithms.bipartite.cluster"]], "networkx.algorithms.bipartite.covering": [[115, "module-networkx.algorithms.bipartite.covering"]], "networkx.algorithms.bipartite.edgelist": [[115, "module-networkx.algorithms.bipartite.edgelist"]], "networkx.algorithms.bipartite.generators": [[115, "module-networkx.algorithms.bipartite.generators"]], "networkx.algorithms.bipartite.matching": [[115, "module-networkx.algorithms.bipartite.matching"]], "networkx.algorithms.bipartite.matrix": [[115, "module-networkx.algorithms.bipartite.matrix"]], "networkx.algorithms.bipartite.projection": [[115, "module-networkx.algorithms.bipartite.projection"]], "networkx.algorithms.bipartite.redundancy": [[115, "module-networkx.algorithms.bipartite.redundancy"]], "networkx.algorithms.bipartite.spectral": [[115, "module-networkx.algorithms.bipartite.spectral"]], "networkx.algorithms.boundary": [[116, "module-networkx.algorithms.boundary"]], "networkx.algorithms.bridges": [[117, "module-networkx.algorithms.bridges"]], "networkx.algorithms.centrality": [[118, "module-networkx.algorithms.centrality"]], "networkx.algorithms.chains": [[119, "module-networkx.algorithms.chains"]], "networkx.algorithms.chordal": [[120, "module-networkx.algorithms.chordal"]], "networkx.algorithms.clique": [[121, "module-networkx.algorithms.clique"]], "networkx.algorithms.cluster": [[122, "module-networkx.algorithms.cluster"]], "networkx.algorithms.coloring": [[123, "module-networkx.algorithms.coloring"]], "networkx.algorithms.communicability_alg": [[124, "module-networkx.algorithms.communicability_alg"]], "networkx.algorithms.community": [[125, "module-networkx.algorithms.community"]], "networkx.algorithms.community.asyn_fluid": [[125, "module-networkx.algorithms.community.asyn_fluid"]], "networkx.algorithms.community.centrality": [[125, "module-networkx.algorithms.community.centrality"]], "networkx.algorithms.community.community_utils": [[125, "module-networkx.algorithms.community.community_utils"]], "networkx.algorithms.community.kclique": [[125, "module-networkx.algorithms.community.kclique"]], "networkx.algorithms.community.kernighan_lin": [[125, "module-networkx.algorithms.community.kernighan_lin"]], "networkx.algorithms.community.label_propagation": [[125, "module-networkx.algorithms.community.label_propagation"]], "networkx.algorithms.community.louvain": [[125, "module-networkx.algorithms.community.louvain"]], "networkx.algorithms.community.lukes": [[125, "module-networkx.algorithms.community.lukes"]], "networkx.algorithms.community.modularity_max": [[125, "module-networkx.algorithms.community.modularity_max"]], "networkx.algorithms.community.quality": [[125, "module-networkx.algorithms.community.quality"]], "networkx.algorithms.components": [[126, "module-networkx.algorithms.components"]], "networkx.algorithms.connectivity": [[127, "module-networkx.algorithms.connectivity"]], "networkx.algorithms.connectivity.connectivity": [[127, "module-networkx.algorithms.connectivity.connectivity"]], "networkx.algorithms.connectivity.cuts": [[127, "module-networkx.algorithms.connectivity.cuts"]], "networkx.algorithms.connectivity.disjoint_paths": [[127, "module-networkx.algorithms.connectivity.disjoint_paths"]], "networkx.algorithms.connectivity.edge_augmentation": [[127, "module-networkx.algorithms.connectivity.edge_augmentation"]], "networkx.algorithms.connectivity.edge_kcomponents": [[127, "module-networkx.algorithms.connectivity.edge_kcomponents"]], "networkx.algorithms.connectivity.kcomponents": [[127, "module-networkx.algorithms.connectivity.kcomponents"]], "networkx.algorithms.connectivity.kcutsets": [[127, "module-networkx.algorithms.connectivity.kcutsets"]], "networkx.algorithms.connectivity.stoerwagner": [[127, "module-networkx.algorithms.connectivity.stoerwagner"]], "networkx.algorithms.connectivity.utils": [[127, "module-networkx.algorithms.connectivity.utils"]], "networkx.algorithms.core": [[128, "module-networkx.algorithms.core"]], "networkx.algorithms.covering": [[129, "module-networkx.algorithms.covering"]], "networkx.algorithms.cuts": [[130, "module-networkx.algorithms.cuts"]], "networkx.algorithms.cycles": [[131, "module-networkx.algorithms.cycles"]], "networkx.algorithms.d_separation": [[132, "module-networkx.algorithms.d_separation"]], "networkx.algorithms.dag": [[133, "module-networkx.algorithms.dag"]], "networkx.algorithms.distance_measures": [[134, "module-networkx.algorithms.distance_measures"]], "networkx.algorithms.distance_regular": [[135, "module-networkx.algorithms.distance_regular"]], "networkx.algorithms.dominance": [[136, "module-networkx.algorithms.dominance"]], "networkx.algorithms.dominating": [[137, "module-networkx.algorithms.dominating"]], "networkx.algorithms.efficiency_measures": [[138, "module-networkx.algorithms.efficiency_measures"]], "networkx.algorithms.euler": [[139, "module-networkx.algorithms.euler"]], "networkx.algorithms.flow": [[140, "module-networkx.algorithms.flow"]], "construct() (edgecomponentauxgraph class method)": [[141, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.construct"]], "k_edge_components() (edgecomponentauxgraph method)": [[142, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.k_edge_components"]], "k_edge_subgraphs() (edgecomponentauxgraph method)": [[143, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.k_edge_subgraphs"]], "analyze_symmetry() (ismags method)": [[144, "networkx.algorithms.isomorphism.ISMAGS.analyze_symmetry"]], "find_isomorphisms() (ismags method)": [[145, "networkx.algorithms.isomorphism.ISMAGS.find_isomorphisms"]], "is_isomorphic() (ismags method)": [[146, "networkx.algorithms.isomorphism.ISMAGS.is_isomorphic"]], "isomorphisms_iter() (ismags method)": [[147, "networkx.algorithms.isomorphism.ISMAGS.isomorphisms_iter"]], "largest_common_subgraph() (ismags method)": [[148, "networkx.algorithms.isomorphism.ISMAGS.largest_common_subgraph"]], "subgraph_is_isomorphic() (ismags method)": [[149, "networkx.algorithms.isomorphism.ISMAGS.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (ismags method)": [[150, "networkx.algorithms.isomorphism.ISMAGS.subgraph_isomorphisms_iter"]], "add_edge() (planarembedding method)": [[151, "networkx.algorithms.planarity.PlanarEmbedding.add_edge"]], "add_edges_from() (planarembedding method)": [[152, "networkx.algorithms.planarity.PlanarEmbedding.add_edges_from"]], "add_half_edge_ccw() (planarembedding method)": [[153, "networkx.algorithms.planarity.PlanarEmbedding.add_half_edge_ccw"]], "add_half_edge_cw() (planarembedding method)": [[154, "networkx.algorithms.planarity.PlanarEmbedding.add_half_edge_cw"]], "add_half_edge_first() (planarembedding method)": [[155, "networkx.algorithms.planarity.PlanarEmbedding.add_half_edge_first"]], "add_node() (planarembedding method)": [[156, "networkx.algorithms.planarity.PlanarEmbedding.add_node"]], "add_nodes_from() (planarembedding method)": [[157, "networkx.algorithms.planarity.PlanarEmbedding.add_nodes_from"]], "add_weighted_edges_from() (planarembedding method)": [[158, "networkx.algorithms.planarity.PlanarEmbedding.add_weighted_edges_from"]], "adj (planarembedding property)": [[159, "networkx.algorithms.planarity.PlanarEmbedding.adj"]], "adjacency() (planarembedding method)": [[160, "networkx.algorithms.planarity.PlanarEmbedding.adjacency"]], "check_structure() (planarembedding method)": [[161, "networkx.algorithms.planarity.PlanarEmbedding.check_structure"]], "clear() (planarembedding method)": [[162, "networkx.algorithms.planarity.PlanarEmbedding.clear"]], "clear_edges() (planarembedding method)": [[163, "networkx.algorithms.planarity.PlanarEmbedding.clear_edges"]], "connect_components() (planarembedding method)": [[164, "networkx.algorithms.planarity.PlanarEmbedding.connect_components"]], "copy() (planarembedding method)": [[165, "networkx.algorithms.planarity.PlanarEmbedding.copy"]], "degree (planarembedding property)": [[166, "networkx.algorithms.planarity.PlanarEmbedding.degree"]], "edge_subgraph() (planarembedding method)": [[167, "networkx.algorithms.planarity.PlanarEmbedding.edge_subgraph"]], "edges (planarembedding property)": [[168, "networkx.algorithms.planarity.PlanarEmbedding.edges"]], "get_data() (planarembedding method)": [[169, "networkx.algorithms.planarity.PlanarEmbedding.get_data"]], "get_edge_data() (planarembedding method)": [[170, "networkx.algorithms.planarity.PlanarEmbedding.get_edge_data"]], "has_edge() (planarembedding method)": [[171, "networkx.algorithms.planarity.PlanarEmbedding.has_edge"]], "has_node() (planarembedding method)": [[172, "networkx.algorithms.planarity.PlanarEmbedding.has_node"]], "has_predecessor() (planarembedding method)": [[173, "networkx.algorithms.planarity.PlanarEmbedding.has_predecessor"]], "has_successor() (planarembedding method)": [[174, "networkx.algorithms.planarity.PlanarEmbedding.has_successor"]], "in_degree (planarembedding property)": [[175, "networkx.algorithms.planarity.PlanarEmbedding.in_degree"]], "in_edges (planarembedding property)": [[176, "networkx.algorithms.planarity.PlanarEmbedding.in_edges"]], "is_directed() (planarembedding method)": [[177, "networkx.algorithms.planarity.PlanarEmbedding.is_directed"]], "is_multigraph() (planarembedding method)": [[178, "networkx.algorithms.planarity.PlanarEmbedding.is_multigraph"]], "name (planarembedding property)": [[179, "networkx.algorithms.planarity.PlanarEmbedding.name"]], "nbunch_iter() (planarembedding method)": [[180, "networkx.algorithms.planarity.PlanarEmbedding.nbunch_iter"]], "neighbors() (planarembedding method)": [[181, "networkx.algorithms.planarity.PlanarEmbedding.neighbors"]], "neighbors_cw_order() (planarembedding method)": [[182, "networkx.algorithms.planarity.PlanarEmbedding.neighbors_cw_order"]], "next_face_half_edge() (planarembedding method)": [[183, "networkx.algorithms.planarity.PlanarEmbedding.next_face_half_edge"]], "nodes (planarembedding property)": [[184, "networkx.algorithms.planarity.PlanarEmbedding.nodes"]], "number_of_edges() (planarembedding method)": [[185, "networkx.algorithms.planarity.PlanarEmbedding.number_of_edges"]], "number_of_nodes() (planarembedding method)": [[186, "networkx.algorithms.planarity.PlanarEmbedding.number_of_nodes"]], "order() (planarembedding method)": [[187, "networkx.algorithms.planarity.PlanarEmbedding.order"]], "out_degree (planarembedding property)": [[188, "networkx.algorithms.planarity.PlanarEmbedding.out_degree"]], "out_edges (planarembedding property)": [[189, "networkx.algorithms.planarity.PlanarEmbedding.out_edges"]], "pred (planarembedding property)": [[190, "networkx.algorithms.planarity.PlanarEmbedding.pred"]], "predecessors() (planarembedding method)": [[191, "networkx.algorithms.planarity.PlanarEmbedding.predecessors"]], "remove_edge() (planarembedding method)": [[192, "networkx.algorithms.planarity.PlanarEmbedding.remove_edge"]], "remove_edges_from() (planarembedding method)": [[193, "networkx.algorithms.planarity.PlanarEmbedding.remove_edges_from"]], "remove_node() (planarembedding method)": [[194, "networkx.algorithms.planarity.PlanarEmbedding.remove_node"]], "remove_nodes_from() (planarembedding method)": [[195, "networkx.algorithms.planarity.PlanarEmbedding.remove_nodes_from"]], "reverse() (planarembedding method)": [[196, "networkx.algorithms.planarity.PlanarEmbedding.reverse"]], "set_data() (planarembedding method)": [[197, "networkx.algorithms.planarity.PlanarEmbedding.set_data"]], "size() (planarembedding method)": [[198, "networkx.algorithms.planarity.PlanarEmbedding.size"]], "subgraph() (planarembedding method)": [[199, "networkx.algorithms.planarity.PlanarEmbedding.subgraph"]], "succ (planarembedding property)": [[200, "networkx.algorithms.planarity.PlanarEmbedding.succ"]], "successors() (planarembedding method)": [[201, "networkx.algorithms.planarity.PlanarEmbedding.successors"]], "to_directed() (planarembedding method)": [[202, "networkx.algorithms.planarity.PlanarEmbedding.to_directed"]], "to_directed_class() (planarembedding method)": [[203, "networkx.algorithms.planarity.PlanarEmbedding.to_directed_class"]], "to_undirected() (planarembedding method)": [[204, "networkx.algorithms.planarity.PlanarEmbedding.to_undirected"]], "to_undirected_class() (planarembedding method)": [[205, "networkx.algorithms.planarity.PlanarEmbedding.to_undirected_class"]], "traverse_face() (planarembedding method)": [[206, "networkx.algorithms.planarity.PlanarEmbedding.traverse_face"]], "update() (planarembedding method)": [[207, "networkx.algorithms.planarity.PlanarEmbedding.update"]], "find_optimum() (edmonds method)": [[208, "networkx.algorithms.tree.branchings.Edmonds.find_optimum"]], "clique_removal() (in module networkx.algorithms.approximation.clique)": [[209, "networkx.algorithms.approximation.clique.clique_removal"]], "large_clique_size() (in module networkx.algorithms.approximation.clique)": [[210, "networkx.algorithms.approximation.clique.large_clique_size"]], "max_clique() (in module networkx.algorithms.approximation.clique)": [[211, "networkx.algorithms.approximation.clique.max_clique"]], "maximum_independent_set() (in module networkx.algorithms.approximation.clique)": [[212, "networkx.algorithms.approximation.clique.maximum_independent_set"]], "average_clustering() (in module networkx.algorithms.approximation.clustering_coefficient)": [[213, "networkx.algorithms.approximation.clustering_coefficient.average_clustering"]], "all_pairs_node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[214, "networkx.algorithms.approximation.connectivity.all_pairs_node_connectivity"]], "local_node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[215, "networkx.algorithms.approximation.connectivity.local_node_connectivity"]], "node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[216, "networkx.algorithms.approximation.connectivity.node_connectivity"]], "diameter() (in module networkx.algorithms.approximation.distance_measures)": [[217, "networkx.algorithms.approximation.distance_measures.diameter"]], "min_edge_dominating_set() (in module networkx.algorithms.approximation.dominating_set)": [[218, "networkx.algorithms.approximation.dominating_set.min_edge_dominating_set"]], "min_weighted_dominating_set() (in module networkx.algorithms.approximation.dominating_set)": [[219, "networkx.algorithms.approximation.dominating_set.min_weighted_dominating_set"]], "k_components() (in module networkx.algorithms.approximation.kcomponents)": [[220, "networkx.algorithms.approximation.kcomponents.k_components"]], "min_maximal_matching() (in module networkx.algorithms.approximation.matching)": [[221, "networkx.algorithms.approximation.matching.min_maximal_matching"]], "one_exchange() (in module networkx.algorithms.approximation.maxcut)": [[222, "networkx.algorithms.approximation.maxcut.one_exchange"]], "randomized_partitioning() (in module networkx.algorithms.approximation.maxcut)": [[223, "networkx.algorithms.approximation.maxcut.randomized_partitioning"]], "ramsey_r2() (in module networkx.algorithms.approximation.ramsey)": [[224, "networkx.algorithms.approximation.ramsey.ramsey_R2"]], "metric_closure() (in module networkx.algorithms.approximation.steinertree)": [[225, "networkx.algorithms.approximation.steinertree.metric_closure"]], "steiner_tree() (in module networkx.algorithms.approximation.steinertree)": [[226, "networkx.algorithms.approximation.steinertree.steiner_tree"]], "asadpour_atsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[227, "networkx.algorithms.approximation.traveling_salesman.asadpour_atsp"]], "christofides() (in module networkx.algorithms.approximation.traveling_salesman)": [[228, "networkx.algorithms.approximation.traveling_salesman.christofides"]], "greedy_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[229, "networkx.algorithms.approximation.traveling_salesman.greedy_tsp"]], "simulated_annealing_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[230, "networkx.algorithms.approximation.traveling_salesman.simulated_annealing_tsp"]], "threshold_accepting_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[231, "networkx.algorithms.approximation.traveling_salesman.threshold_accepting_tsp"]], "traveling_salesman_problem() (in module networkx.algorithms.approximation.traveling_salesman)": [[232, "networkx.algorithms.approximation.traveling_salesman.traveling_salesman_problem"]], "treewidth_min_degree() (in module networkx.algorithms.approximation.treewidth)": [[233, "networkx.algorithms.approximation.treewidth.treewidth_min_degree"]], "treewidth_min_fill_in() (in module networkx.algorithms.approximation.treewidth)": [[234, "networkx.algorithms.approximation.treewidth.treewidth_min_fill_in"]], "min_weighted_vertex_cover() (in module networkx.algorithms.approximation.vertex_cover)": [[235, "networkx.algorithms.approximation.vertex_cover.min_weighted_vertex_cover"]], "attribute_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[236, "networkx.algorithms.assortativity.attribute_assortativity_coefficient"]], "attribute_mixing_dict() (in module networkx.algorithms.assortativity)": [[237, "networkx.algorithms.assortativity.attribute_mixing_dict"]], "attribute_mixing_matrix() (in module networkx.algorithms.assortativity)": [[238, "networkx.algorithms.assortativity.attribute_mixing_matrix"]], "average_degree_connectivity() (in module networkx.algorithms.assortativity)": [[239, "networkx.algorithms.assortativity.average_degree_connectivity"]], "average_neighbor_degree() (in module networkx.algorithms.assortativity)": [[240, "networkx.algorithms.assortativity.average_neighbor_degree"]], "degree_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[241, "networkx.algorithms.assortativity.degree_assortativity_coefficient"]], "degree_mixing_dict() (in module networkx.algorithms.assortativity)": [[242, "networkx.algorithms.assortativity.degree_mixing_dict"]], "degree_mixing_matrix() (in module networkx.algorithms.assortativity)": [[243, "networkx.algorithms.assortativity.degree_mixing_matrix"]], "degree_pearson_correlation_coefficient() (in module networkx.algorithms.assortativity)": [[244, "networkx.algorithms.assortativity.degree_pearson_correlation_coefficient"]], "mixing_dict() (in module networkx.algorithms.assortativity)": [[245, "networkx.algorithms.assortativity.mixing_dict"]], "node_attribute_xy() (in module networkx.algorithms.assortativity)": [[246, "networkx.algorithms.assortativity.node_attribute_xy"]], "node_degree_xy() (in module networkx.algorithms.assortativity)": [[247, "networkx.algorithms.assortativity.node_degree_xy"]], "numeric_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[248, "networkx.algorithms.assortativity.numeric_assortativity_coefficient"]], "find_asteroidal_triple() (in module networkx.algorithms.asteroidal)": [[249, "networkx.algorithms.asteroidal.find_asteroidal_triple"]], "is_at_free() (in module networkx.algorithms.asteroidal)": [[250, "networkx.algorithms.asteroidal.is_at_free"]], "color() (in module networkx.algorithms.bipartite.basic)": [[251, "networkx.algorithms.bipartite.basic.color"]], "degrees() (in module networkx.algorithms.bipartite.basic)": [[252, "networkx.algorithms.bipartite.basic.degrees"]], "density() (in module networkx.algorithms.bipartite.basic)": [[253, "networkx.algorithms.bipartite.basic.density"]], "is_bipartite() (in module networkx.algorithms.bipartite.basic)": [[254, "networkx.algorithms.bipartite.basic.is_bipartite"]], "is_bipartite_node_set() (in module networkx.algorithms.bipartite.basic)": [[255, "networkx.algorithms.bipartite.basic.is_bipartite_node_set"]], "sets() (in module networkx.algorithms.bipartite.basic)": [[256, "networkx.algorithms.bipartite.basic.sets"]], "betweenness_centrality() (in module networkx.algorithms.bipartite.centrality)": [[257, "networkx.algorithms.bipartite.centrality.betweenness_centrality"]], "closeness_centrality() (in module networkx.algorithms.bipartite.centrality)": [[258, "networkx.algorithms.bipartite.centrality.closeness_centrality"]], "degree_centrality() (in module networkx.algorithms.bipartite.centrality)": [[259, "networkx.algorithms.bipartite.centrality.degree_centrality"]], "average_clustering() (in module networkx.algorithms.bipartite.cluster)": [[260, "networkx.algorithms.bipartite.cluster.average_clustering"]], "clustering() (in module networkx.algorithms.bipartite.cluster)": [[261, "networkx.algorithms.bipartite.cluster.clustering"]], "latapy_clustering() (in module networkx.algorithms.bipartite.cluster)": [[262, "networkx.algorithms.bipartite.cluster.latapy_clustering"]], "robins_alexander_clustering() (in module networkx.algorithms.bipartite.cluster)": [[263, "networkx.algorithms.bipartite.cluster.robins_alexander_clustering"]], "min_edge_cover() (in module networkx.algorithms.bipartite.covering)": [[264, "networkx.algorithms.bipartite.covering.min_edge_cover"]], "generate_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[265, "networkx.algorithms.bipartite.edgelist.generate_edgelist"]], "parse_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[266, "networkx.algorithms.bipartite.edgelist.parse_edgelist"]], "read_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[267, "networkx.algorithms.bipartite.edgelist.read_edgelist"]], "write_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[268, "networkx.algorithms.bipartite.edgelist.write_edgelist"]], "alternating_havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[269, "networkx.algorithms.bipartite.generators.alternating_havel_hakimi_graph"]], "complete_bipartite_graph() (in module networkx.algorithms.bipartite.generators)": [[270, "networkx.algorithms.bipartite.generators.complete_bipartite_graph"]], "configuration_model() (in module networkx.algorithms.bipartite.generators)": [[271, "networkx.algorithms.bipartite.generators.configuration_model"]], "gnmk_random_graph() (in module networkx.algorithms.bipartite.generators)": [[272, "networkx.algorithms.bipartite.generators.gnmk_random_graph"]], "havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[273, "networkx.algorithms.bipartite.generators.havel_hakimi_graph"]], "preferential_attachment_graph() (in module networkx.algorithms.bipartite.generators)": [[274, "networkx.algorithms.bipartite.generators.preferential_attachment_graph"]], "random_graph() (in module networkx.algorithms.bipartite.generators)": [[275, "networkx.algorithms.bipartite.generators.random_graph"]], "reverse_havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[276, "networkx.algorithms.bipartite.generators.reverse_havel_hakimi_graph"]], "eppstein_matching() (in module networkx.algorithms.bipartite.matching)": [[277, "networkx.algorithms.bipartite.matching.eppstein_matching"]], "hopcroft_karp_matching() (in module networkx.algorithms.bipartite.matching)": [[278, "networkx.algorithms.bipartite.matching.hopcroft_karp_matching"]], "maximum_matching() (in module networkx.algorithms.bipartite.matching)": [[279, "networkx.algorithms.bipartite.matching.maximum_matching"]], "minimum_weight_full_matching() (in module networkx.algorithms.bipartite.matching)": [[280, "networkx.algorithms.bipartite.matching.minimum_weight_full_matching"]], "to_vertex_cover() (in module networkx.algorithms.bipartite.matching)": [[281, "networkx.algorithms.bipartite.matching.to_vertex_cover"]], "biadjacency_matrix() (in module networkx.algorithms.bipartite.matrix)": [[282, "networkx.algorithms.bipartite.matrix.biadjacency_matrix"]], "from_biadjacency_matrix() (in module networkx.algorithms.bipartite.matrix)": [[283, "networkx.algorithms.bipartite.matrix.from_biadjacency_matrix"]], "collaboration_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[284, "networkx.algorithms.bipartite.projection.collaboration_weighted_projected_graph"]], "generic_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[285, "networkx.algorithms.bipartite.projection.generic_weighted_projected_graph"]], "overlap_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[286, "networkx.algorithms.bipartite.projection.overlap_weighted_projected_graph"]], "projected_graph() (in module networkx.algorithms.bipartite.projection)": [[287, "networkx.algorithms.bipartite.projection.projected_graph"]], "weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[288, "networkx.algorithms.bipartite.projection.weighted_projected_graph"]], "node_redundancy() (in module networkx.algorithms.bipartite.redundancy)": [[289, "networkx.algorithms.bipartite.redundancy.node_redundancy"]], "spectral_bipartivity() (in module networkx.algorithms.bipartite.spectral)": [[290, "networkx.algorithms.bipartite.spectral.spectral_bipartivity"]], "edge_boundary() (in module networkx.algorithms.boundary)": [[291, "networkx.algorithms.boundary.edge_boundary"]], "node_boundary() (in module networkx.algorithms.boundary)": [[292, "networkx.algorithms.boundary.node_boundary"]], "bridges() (in module networkx.algorithms.bridges)": [[293, "networkx.algorithms.bridges.bridges"]], "has_bridges() (in module networkx.algorithms.bridges)": [[294, "networkx.algorithms.bridges.has_bridges"]], "local_bridges() (in module networkx.algorithms.bridges)": [[295, "networkx.algorithms.bridges.local_bridges"]], "approximate_current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[296, "networkx.algorithms.centrality.approximate_current_flow_betweenness_centrality"]], "betweenness_centrality() (in module networkx.algorithms.centrality)": [[297, "networkx.algorithms.centrality.betweenness_centrality"]], "betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[298, "networkx.algorithms.centrality.betweenness_centrality_subset"]], "closeness_centrality() (in module networkx.algorithms.centrality)": [[299, "networkx.algorithms.centrality.closeness_centrality"]], "communicability_betweenness_centrality() (in module networkx.algorithms.centrality)": [[300, "networkx.algorithms.centrality.communicability_betweenness_centrality"]], "current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[301, "networkx.algorithms.centrality.current_flow_betweenness_centrality"]], "current_flow_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[302, "networkx.algorithms.centrality.current_flow_betweenness_centrality_subset"]], "current_flow_closeness_centrality() (in module networkx.algorithms.centrality)": [[303, "networkx.algorithms.centrality.current_flow_closeness_centrality"]], "degree_centrality() (in module networkx.algorithms.centrality)": [[304, "networkx.algorithms.centrality.degree_centrality"]], "dispersion() (in module networkx.algorithms.centrality)": [[305, "networkx.algorithms.centrality.dispersion"]], "edge_betweenness_centrality() (in module networkx.algorithms.centrality)": [[306, "networkx.algorithms.centrality.edge_betweenness_centrality"]], "edge_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[307, "networkx.algorithms.centrality.edge_betweenness_centrality_subset"]], "edge_current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[308, "networkx.algorithms.centrality.edge_current_flow_betweenness_centrality"]], "edge_current_flow_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[309, "networkx.algorithms.centrality.edge_current_flow_betweenness_centrality_subset"]], "edge_load_centrality() (in module networkx.algorithms.centrality)": [[310, "networkx.algorithms.centrality.edge_load_centrality"]], "eigenvector_centrality() (in module networkx.algorithms.centrality)": [[311, "networkx.algorithms.centrality.eigenvector_centrality"]], "eigenvector_centrality_numpy() (in module networkx.algorithms.centrality)": [[312, "networkx.algorithms.centrality.eigenvector_centrality_numpy"]], "estrada_index() (in module networkx.algorithms.centrality)": [[313, "networkx.algorithms.centrality.estrada_index"]], "global_reaching_centrality() (in module networkx.algorithms.centrality)": [[314, "networkx.algorithms.centrality.global_reaching_centrality"]], "group_betweenness_centrality() (in module networkx.algorithms.centrality)": [[315, "networkx.algorithms.centrality.group_betweenness_centrality"]], "group_closeness_centrality() (in module networkx.algorithms.centrality)": [[316, "networkx.algorithms.centrality.group_closeness_centrality"]], "group_degree_centrality() (in module networkx.algorithms.centrality)": [[317, "networkx.algorithms.centrality.group_degree_centrality"]], "group_in_degree_centrality() (in module networkx.algorithms.centrality)": [[318, "networkx.algorithms.centrality.group_in_degree_centrality"]], "group_out_degree_centrality() (in module networkx.algorithms.centrality)": [[319, "networkx.algorithms.centrality.group_out_degree_centrality"]], "harmonic_centrality() (in module networkx.algorithms.centrality)": [[320, "networkx.algorithms.centrality.harmonic_centrality"]], "in_degree_centrality() (in module networkx.algorithms.centrality)": [[321, "networkx.algorithms.centrality.in_degree_centrality"]], "incremental_closeness_centrality() (in module networkx.algorithms.centrality)": [[322, "networkx.algorithms.centrality.incremental_closeness_centrality"]], "information_centrality() (in module networkx.algorithms.centrality)": [[323, "networkx.algorithms.centrality.information_centrality"]], "katz_centrality() (in module networkx.algorithms.centrality)": [[324, "networkx.algorithms.centrality.katz_centrality"]], "katz_centrality_numpy() (in module networkx.algorithms.centrality)": [[325, "networkx.algorithms.centrality.katz_centrality_numpy"]], "load_centrality() (in module networkx.algorithms.centrality)": [[326, "networkx.algorithms.centrality.load_centrality"]], "local_reaching_centrality() (in module networkx.algorithms.centrality)": [[327, "networkx.algorithms.centrality.local_reaching_centrality"]], "out_degree_centrality() (in module networkx.algorithms.centrality)": [[328, "networkx.algorithms.centrality.out_degree_centrality"]], "percolation_centrality() (in module networkx.algorithms.centrality)": [[329, "networkx.algorithms.centrality.percolation_centrality"]], "prominent_group() (in module networkx.algorithms.centrality)": [[330, "networkx.algorithms.centrality.prominent_group"]], "second_order_centrality() (in module networkx.algorithms.centrality)": [[331, "networkx.algorithms.centrality.second_order_centrality"]], "subgraph_centrality() (in module networkx.algorithms.centrality)": [[332, "networkx.algorithms.centrality.subgraph_centrality"]], "subgraph_centrality_exp() (in module networkx.algorithms.centrality)": [[333, "networkx.algorithms.centrality.subgraph_centrality_exp"]], "trophic_differences() (in module networkx.algorithms.centrality)": [[334, "networkx.algorithms.centrality.trophic_differences"]], "trophic_incoherence_parameter() (in module networkx.algorithms.centrality)": [[335, "networkx.algorithms.centrality.trophic_incoherence_parameter"]], "trophic_levels() (in module networkx.algorithms.centrality)": [[336, "networkx.algorithms.centrality.trophic_levels"]], "voterank() (in module networkx.algorithms.centrality)": [[337, "networkx.algorithms.centrality.voterank"]], "chain_decomposition() (in module networkx.algorithms.chains)": [[338, "networkx.algorithms.chains.chain_decomposition"]], "chordal_graph_cliques() (in module networkx.algorithms.chordal)": [[339, "networkx.algorithms.chordal.chordal_graph_cliques"]], "chordal_graph_treewidth() (in module networkx.algorithms.chordal)": [[340, "networkx.algorithms.chordal.chordal_graph_treewidth"]], "complete_to_chordal_graph() (in module networkx.algorithms.chordal)": [[341, "networkx.algorithms.chordal.complete_to_chordal_graph"]], "find_induced_nodes() (in module networkx.algorithms.chordal)": [[342, "networkx.algorithms.chordal.find_induced_nodes"]], "is_chordal() (in module networkx.algorithms.chordal)": [[343, "networkx.algorithms.chordal.is_chordal"]], "cliques_containing_node() (in module networkx.algorithms.clique)": [[344, "networkx.algorithms.clique.cliques_containing_node"]], "enumerate_all_cliques() (in module networkx.algorithms.clique)": [[345, "networkx.algorithms.clique.enumerate_all_cliques"]], "find_cliques() (in module networkx.algorithms.clique)": [[346, "networkx.algorithms.clique.find_cliques"]], "find_cliques_recursive() (in module networkx.algorithms.clique)": [[347, "networkx.algorithms.clique.find_cliques_recursive"]], "graph_clique_number() (in module networkx.algorithms.clique)": [[348, "networkx.algorithms.clique.graph_clique_number"]], "graph_number_of_cliques() (in module networkx.algorithms.clique)": [[349, "networkx.algorithms.clique.graph_number_of_cliques"]], "make_clique_bipartite() (in module networkx.algorithms.clique)": [[350, "networkx.algorithms.clique.make_clique_bipartite"]], "make_max_clique_graph() (in module networkx.algorithms.clique)": [[351, "networkx.algorithms.clique.make_max_clique_graph"]], "max_weight_clique() (in module networkx.algorithms.clique)": [[352, "networkx.algorithms.clique.max_weight_clique"]], "node_clique_number() (in module networkx.algorithms.clique)": [[353, "networkx.algorithms.clique.node_clique_number"]], "number_of_cliques() (in module networkx.algorithms.clique)": [[354, "networkx.algorithms.clique.number_of_cliques"]], "average_clustering() (in module networkx.algorithms.cluster)": [[355, "networkx.algorithms.cluster.average_clustering"]], "clustering() (in module networkx.algorithms.cluster)": [[356, "networkx.algorithms.cluster.clustering"]], "generalized_degree() (in module networkx.algorithms.cluster)": [[357, "networkx.algorithms.cluster.generalized_degree"]], "square_clustering() (in module networkx.algorithms.cluster)": [[358, "networkx.algorithms.cluster.square_clustering"]], "transitivity() (in module networkx.algorithms.cluster)": [[359, "networkx.algorithms.cluster.transitivity"]], "triangles() (in module networkx.algorithms.cluster)": [[360, "networkx.algorithms.cluster.triangles"]], "equitable_color() (in module networkx.algorithms.coloring)": [[361, "networkx.algorithms.coloring.equitable_color"]], "greedy_color() (in module networkx.algorithms.coloring)": [[362, "networkx.algorithms.coloring.greedy_color"]], "strategy_connected_sequential() (in module networkx.algorithms.coloring)": [[363, "networkx.algorithms.coloring.strategy_connected_sequential"]], "strategy_connected_sequential_bfs() (in module networkx.algorithms.coloring)": [[364, "networkx.algorithms.coloring.strategy_connected_sequential_bfs"]], "strategy_connected_sequential_dfs() (in module networkx.algorithms.coloring)": [[365, "networkx.algorithms.coloring.strategy_connected_sequential_dfs"]], "strategy_independent_set() (in module networkx.algorithms.coloring)": [[366, "networkx.algorithms.coloring.strategy_independent_set"]], "strategy_largest_first() (in module networkx.algorithms.coloring)": [[367, "networkx.algorithms.coloring.strategy_largest_first"]], "strategy_random_sequential() (in module networkx.algorithms.coloring)": [[368, "networkx.algorithms.coloring.strategy_random_sequential"]], "strategy_saturation_largest_first() (in module networkx.algorithms.coloring)": [[369, "networkx.algorithms.coloring.strategy_saturation_largest_first"]], "strategy_smallest_last() (in module networkx.algorithms.coloring)": [[370, "networkx.algorithms.coloring.strategy_smallest_last"]], "communicability() (in module networkx.algorithms.communicability_alg)": [[371, "networkx.algorithms.communicability_alg.communicability"]], "communicability_exp() (in module networkx.algorithms.communicability_alg)": [[372, "networkx.algorithms.communicability_alg.communicability_exp"]], "asyn_fluidc() (in module networkx.algorithms.community.asyn_fluid)": [[373, "networkx.algorithms.community.asyn_fluid.asyn_fluidc"]], "girvan_newman() (in module networkx.algorithms.community.centrality)": [[374, "networkx.algorithms.community.centrality.girvan_newman"]], "is_partition() (in module networkx.algorithms.community.community_utils)": [[375, "networkx.algorithms.community.community_utils.is_partition"]], "k_clique_communities() (in module networkx.algorithms.community.kclique)": [[376, "networkx.algorithms.community.kclique.k_clique_communities"]], "kernighan_lin_bisection() (in module networkx.algorithms.community.kernighan_lin)": [[377, "networkx.algorithms.community.kernighan_lin.kernighan_lin_bisection"]], "asyn_lpa_communities() (in module networkx.algorithms.community.label_propagation)": [[378, "networkx.algorithms.community.label_propagation.asyn_lpa_communities"]], "label_propagation_communities() (in module networkx.algorithms.community.label_propagation)": [[379, "networkx.algorithms.community.label_propagation.label_propagation_communities"]], "louvain_communities() (in module networkx.algorithms.community.louvain)": [[380, "networkx.algorithms.community.louvain.louvain_communities"]], "louvain_partitions() (in module networkx.algorithms.community.louvain)": [[381, "networkx.algorithms.community.louvain.louvain_partitions"]], "lukes_partitioning() (in module networkx.algorithms.community.lukes)": [[382, "networkx.algorithms.community.lukes.lukes_partitioning"]], "greedy_modularity_communities() (in module networkx.algorithms.community.modularity_max)": [[383, "networkx.algorithms.community.modularity_max.greedy_modularity_communities"]], "naive_greedy_modularity_communities() (in module networkx.algorithms.community.modularity_max)": [[384, "networkx.algorithms.community.modularity_max.naive_greedy_modularity_communities"]], "modularity() (in module networkx.algorithms.community.quality)": [[385, "networkx.algorithms.community.quality.modularity"]], "partition_quality() (in module networkx.algorithms.community.quality)": [[386, "networkx.algorithms.community.quality.partition_quality"]], "articulation_points() (in module networkx.algorithms.components)": [[387, "networkx.algorithms.components.articulation_points"]], "attracting_components() (in module networkx.algorithms.components)": [[388, "networkx.algorithms.components.attracting_components"]], "biconnected_component_edges() (in module networkx.algorithms.components)": [[389, "networkx.algorithms.components.biconnected_component_edges"]], "biconnected_components() (in module networkx.algorithms.components)": [[390, "networkx.algorithms.components.biconnected_components"]], "condensation() (in module networkx.algorithms.components)": [[391, "networkx.algorithms.components.condensation"]], "connected_components() (in module networkx.algorithms.components)": [[392, "networkx.algorithms.components.connected_components"]], "is_attracting_component() (in module networkx.algorithms.components)": [[393, "networkx.algorithms.components.is_attracting_component"]], "is_biconnected() (in module networkx.algorithms.components)": [[394, "networkx.algorithms.components.is_biconnected"]], "is_connected() (in module networkx.algorithms.components)": [[395, "networkx.algorithms.components.is_connected"]], "is_semiconnected() (in module networkx.algorithms.components)": [[396, "networkx.algorithms.components.is_semiconnected"]], "is_strongly_connected() (in module networkx.algorithms.components)": [[397, "networkx.algorithms.components.is_strongly_connected"]], "is_weakly_connected() (in module networkx.algorithms.components)": [[398, "networkx.algorithms.components.is_weakly_connected"]], "kosaraju_strongly_connected_components() (in module networkx.algorithms.components)": [[399, "networkx.algorithms.components.kosaraju_strongly_connected_components"]], "node_connected_component() (in module networkx.algorithms.components)": [[400, "networkx.algorithms.components.node_connected_component"]], "number_attracting_components() (in module networkx.algorithms.components)": [[401, "networkx.algorithms.components.number_attracting_components"]], "number_connected_components() (in module networkx.algorithms.components)": [[402, "networkx.algorithms.components.number_connected_components"]], "number_strongly_connected_components() (in module networkx.algorithms.components)": [[403, "networkx.algorithms.components.number_strongly_connected_components"]], "number_weakly_connected_components() (in module networkx.algorithms.components)": [[404, "networkx.algorithms.components.number_weakly_connected_components"]], "strongly_connected_components() (in module networkx.algorithms.components)": [[405, "networkx.algorithms.components.strongly_connected_components"]], "strongly_connected_components_recursive() (in module networkx.algorithms.components)": [[406, "networkx.algorithms.components.strongly_connected_components_recursive"]], "weakly_connected_components() (in module networkx.algorithms.components)": [[407, "networkx.algorithms.components.weakly_connected_components"]], "all_pairs_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[408, "networkx.algorithms.connectivity.connectivity.all_pairs_node_connectivity"]], "average_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[409, "networkx.algorithms.connectivity.connectivity.average_node_connectivity"]], "edge_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[410, "networkx.algorithms.connectivity.connectivity.edge_connectivity"]], "local_edge_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[411, "networkx.algorithms.connectivity.connectivity.local_edge_connectivity"]], "local_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[412, "networkx.algorithms.connectivity.connectivity.local_node_connectivity"]], "node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[413, "networkx.algorithms.connectivity.connectivity.node_connectivity"]], "minimum_edge_cut() (in module networkx.algorithms.connectivity.cuts)": [[414, "networkx.algorithms.connectivity.cuts.minimum_edge_cut"]], "minimum_node_cut() (in module networkx.algorithms.connectivity.cuts)": [[415, "networkx.algorithms.connectivity.cuts.minimum_node_cut"]], "minimum_st_edge_cut() (in module networkx.algorithms.connectivity.cuts)": [[416, "networkx.algorithms.connectivity.cuts.minimum_st_edge_cut"]], "minimum_st_node_cut() (in module networkx.algorithms.connectivity.cuts)": [[417, "networkx.algorithms.connectivity.cuts.minimum_st_node_cut"]], "edge_disjoint_paths() (in module networkx.algorithms.connectivity.disjoint_paths)": [[418, "networkx.algorithms.connectivity.disjoint_paths.edge_disjoint_paths"]], "node_disjoint_paths() (in module networkx.algorithms.connectivity.disjoint_paths)": [[419, "networkx.algorithms.connectivity.disjoint_paths.node_disjoint_paths"]], "is_k_edge_connected() (in module networkx.algorithms.connectivity.edge_augmentation)": [[420, "networkx.algorithms.connectivity.edge_augmentation.is_k_edge_connected"]], "is_locally_k_edge_connected() (in module networkx.algorithms.connectivity.edge_augmentation)": [[421, "networkx.algorithms.connectivity.edge_augmentation.is_locally_k_edge_connected"]], "k_edge_augmentation() (in module networkx.algorithms.connectivity.edge_augmentation)": [[422, "networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation"]], "edgecomponentauxgraph (class in networkx.algorithms.connectivity.edge_kcomponents)": [[423, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph"]], "__init__() (edgecomponentauxgraph method)": [[423, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.__init__"]], "bridge_components() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[424, "networkx.algorithms.connectivity.edge_kcomponents.bridge_components"]], "k_edge_components() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[425, "networkx.algorithms.connectivity.edge_kcomponents.k_edge_components"]], "k_edge_subgraphs() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[426, "networkx.algorithms.connectivity.edge_kcomponents.k_edge_subgraphs"]], "k_components() (in module networkx.algorithms.connectivity.kcomponents)": [[427, "networkx.algorithms.connectivity.kcomponents.k_components"]], "all_node_cuts() (in module networkx.algorithms.connectivity.kcutsets)": [[428, "networkx.algorithms.connectivity.kcutsets.all_node_cuts"]], "stoer_wagner() (in module networkx.algorithms.connectivity.stoerwagner)": [[429, "networkx.algorithms.connectivity.stoerwagner.stoer_wagner"]], "build_auxiliary_edge_connectivity() (in module networkx.algorithms.connectivity.utils)": [[430, "networkx.algorithms.connectivity.utils.build_auxiliary_edge_connectivity"]], "build_auxiliary_node_connectivity() (in module networkx.algorithms.connectivity.utils)": [[431, "networkx.algorithms.connectivity.utils.build_auxiliary_node_connectivity"]], "core_number() (in module networkx.algorithms.core)": [[432, "networkx.algorithms.core.core_number"]], "k_core() (in module networkx.algorithms.core)": [[433, "networkx.algorithms.core.k_core"]], "k_corona() (in module networkx.algorithms.core)": [[434, "networkx.algorithms.core.k_corona"]], "k_crust() (in module networkx.algorithms.core)": [[435, "networkx.algorithms.core.k_crust"]], "k_shell() (in module networkx.algorithms.core)": [[436, "networkx.algorithms.core.k_shell"]], "k_truss() (in module networkx.algorithms.core)": [[437, "networkx.algorithms.core.k_truss"]], "onion_layers() (in module networkx.algorithms.core)": [[438, "networkx.algorithms.core.onion_layers"]], "is_edge_cover() (in module networkx.algorithms.covering)": [[439, "networkx.algorithms.covering.is_edge_cover"]], "min_edge_cover() (in module networkx.algorithms.covering)": [[440, "networkx.algorithms.covering.min_edge_cover"]], "boundary_expansion() (in module networkx.algorithms.cuts)": [[441, "networkx.algorithms.cuts.boundary_expansion"]], "conductance() (in module networkx.algorithms.cuts)": [[442, "networkx.algorithms.cuts.conductance"]], "cut_size() (in module networkx.algorithms.cuts)": [[443, "networkx.algorithms.cuts.cut_size"]], "edge_expansion() (in module networkx.algorithms.cuts)": [[444, "networkx.algorithms.cuts.edge_expansion"]], "mixing_expansion() (in module networkx.algorithms.cuts)": [[445, "networkx.algorithms.cuts.mixing_expansion"]], "node_expansion() (in module networkx.algorithms.cuts)": [[446, "networkx.algorithms.cuts.node_expansion"]], "normalized_cut_size() (in module networkx.algorithms.cuts)": [[447, "networkx.algorithms.cuts.normalized_cut_size"]], "volume() (in module networkx.algorithms.cuts)": [[448, "networkx.algorithms.cuts.volume"]], "cycle_basis() (in module networkx.algorithms.cycles)": [[449, "networkx.algorithms.cycles.cycle_basis"]], "find_cycle() (in module networkx.algorithms.cycles)": [[450, "networkx.algorithms.cycles.find_cycle"]], "minimum_cycle_basis() (in module networkx.algorithms.cycles)": [[451, "networkx.algorithms.cycles.minimum_cycle_basis"]], "recursive_simple_cycles() (in module networkx.algorithms.cycles)": [[452, "networkx.algorithms.cycles.recursive_simple_cycles"]], "simple_cycles() (in module networkx.algorithms.cycles)": [[453, "networkx.algorithms.cycles.simple_cycles"]], "d_separated() (in module networkx.algorithms.d_separation)": [[454, "networkx.algorithms.d_separation.d_separated"]], "all_topological_sorts() (in module networkx.algorithms.dag)": [[455, "networkx.algorithms.dag.all_topological_sorts"]], "ancestors() (in module networkx.algorithms.dag)": [[456, "networkx.algorithms.dag.ancestors"]], "antichains() (in module networkx.algorithms.dag)": [[457, "networkx.algorithms.dag.antichains"]], "dag_longest_path() (in module networkx.algorithms.dag)": [[458, "networkx.algorithms.dag.dag_longest_path"]], "dag_longest_path_length() (in module networkx.algorithms.dag)": [[459, "networkx.algorithms.dag.dag_longest_path_length"]], "dag_to_branching() (in module networkx.algorithms.dag)": [[460, "networkx.algorithms.dag.dag_to_branching"]], "descendants() (in module networkx.algorithms.dag)": [[461, "networkx.algorithms.dag.descendants"]], "is_aperiodic() (in module networkx.algorithms.dag)": [[462, "networkx.algorithms.dag.is_aperiodic"]], "is_directed_acyclic_graph() (in module networkx.algorithms.dag)": [[463, "networkx.algorithms.dag.is_directed_acyclic_graph"]], "lexicographical_topological_sort() (in module networkx.algorithms.dag)": [[464, "networkx.algorithms.dag.lexicographical_topological_sort"]], "topological_generations() (in module networkx.algorithms.dag)": [[465, "networkx.algorithms.dag.topological_generations"]], "topological_sort() (in module networkx.algorithms.dag)": [[466, "networkx.algorithms.dag.topological_sort"]], "transitive_closure() (in module networkx.algorithms.dag)": [[467, "networkx.algorithms.dag.transitive_closure"]], "transitive_closure_dag() (in module networkx.algorithms.dag)": [[468, "networkx.algorithms.dag.transitive_closure_dag"]], "transitive_reduction() (in module networkx.algorithms.dag)": [[469, "networkx.algorithms.dag.transitive_reduction"]], "barycenter() (in module networkx.algorithms.distance_measures)": [[470, "networkx.algorithms.distance_measures.barycenter"]], "center() (in module networkx.algorithms.distance_measures)": [[471, "networkx.algorithms.distance_measures.center"]], "diameter() (in module networkx.algorithms.distance_measures)": [[472, "networkx.algorithms.distance_measures.diameter"]], "eccentricity() (in module networkx.algorithms.distance_measures)": [[473, "networkx.algorithms.distance_measures.eccentricity"]], "periphery() (in module networkx.algorithms.distance_measures)": [[474, "networkx.algorithms.distance_measures.periphery"]], "radius() (in module networkx.algorithms.distance_measures)": [[475, "networkx.algorithms.distance_measures.radius"]], "resistance_distance() (in module networkx.algorithms.distance_measures)": [[476, "networkx.algorithms.distance_measures.resistance_distance"]], "global_parameters() (in module networkx.algorithms.distance_regular)": [[477, "networkx.algorithms.distance_regular.global_parameters"]], "intersection_array() (in module networkx.algorithms.distance_regular)": [[478, "networkx.algorithms.distance_regular.intersection_array"]], "is_distance_regular() (in module networkx.algorithms.distance_regular)": [[479, "networkx.algorithms.distance_regular.is_distance_regular"]], "is_strongly_regular() (in module networkx.algorithms.distance_regular)": [[480, "networkx.algorithms.distance_regular.is_strongly_regular"]], "dominance_frontiers() (in module networkx.algorithms.dominance)": [[481, "networkx.algorithms.dominance.dominance_frontiers"]], "immediate_dominators() (in module networkx.algorithms.dominance)": [[482, "networkx.algorithms.dominance.immediate_dominators"]], "dominating_set() (in module networkx.algorithms.dominating)": [[483, "networkx.algorithms.dominating.dominating_set"]], "is_dominating_set() (in module networkx.algorithms.dominating)": [[484, "networkx.algorithms.dominating.is_dominating_set"]], "efficiency() (in module networkx.algorithms.efficiency_measures)": [[485, "networkx.algorithms.efficiency_measures.efficiency"]], "global_efficiency() (in module networkx.algorithms.efficiency_measures)": [[486, "networkx.algorithms.efficiency_measures.global_efficiency"]], "local_efficiency() (in module networkx.algorithms.efficiency_measures)": [[487, "networkx.algorithms.efficiency_measures.local_efficiency"]], "eulerian_circuit() (in module networkx.algorithms.euler)": [[488, "networkx.algorithms.euler.eulerian_circuit"]], "eulerian_path() (in module networkx.algorithms.euler)": [[489, "networkx.algorithms.euler.eulerian_path"]], "eulerize() (in module networkx.algorithms.euler)": [[490, "networkx.algorithms.euler.eulerize"]], "has_eulerian_path() (in module networkx.algorithms.euler)": [[491, "networkx.algorithms.euler.has_eulerian_path"]], "is_eulerian() (in module networkx.algorithms.euler)": [[492, "networkx.algorithms.euler.is_eulerian"]], "is_semieulerian() (in module networkx.algorithms.euler)": [[493, "networkx.algorithms.euler.is_semieulerian"]], "boykov_kolmogorov() (in module networkx.algorithms.flow)": [[494, "networkx.algorithms.flow.boykov_kolmogorov"]], "build_residual_network() (in module networkx.algorithms.flow)": [[495, "networkx.algorithms.flow.build_residual_network"]], "capacity_scaling() (in module networkx.algorithms.flow)": [[496, "networkx.algorithms.flow.capacity_scaling"]], "cost_of_flow() (in module networkx.algorithms.flow)": [[497, "networkx.algorithms.flow.cost_of_flow"]], "dinitz() (in module networkx.algorithms.flow)": [[498, "networkx.algorithms.flow.dinitz"]], "edmonds_karp() (in module networkx.algorithms.flow)": [[499, "networkx.algorithms.flow.edmonds_karp"]], "gomory_hu_tree() (in module networkx.algorithms.flow)": [[500, "networkx.algorithms.flow.gomory_hu_tree"]], "max_flow_min_cost() (in module networkx.algorithms.flow)": [[501, "networkx.algorithms.flow.max_flow_min_cost"]], "maximum_flow() (in module networkx.algorithms.flow)": [[502, "networkx.algorithms.flow.maximum_flow"]], "maximum_flow_value() (in module networkx.algorithms.flow)": [[503, "networkx.algorithms.flow.maximum_flow_value"]], "min_cost_flow() (in module networkx.algorithms.flow)": [[504, "networkx.algorithms.flow.min_cost_flow"]], "min_cost_flow_cost() (in module networkx.algorithms.flow)": [[505, "networkx.algorithms.flow.min_cost_flow_cost"]], "minimum_cut() (in module networkx.algorithms.flow)": [[506, "networkx.algorithms.flow.minimum_cut"]], "minimum_cut_value() (in module networkx.algorithms.flow)": [[507, "networkx.algorithms.flow.minimum_cut_value"]], "network_simplex() (in module networkx.algorithms.flow)": [[508, "networkx.algorithms.flow.network_simplex"]], "preflow_push() (in module networkx.algorithms.flow)": [[509, "networkx.algorithms.flow.preflow_push"]], "shortest_augmenting_path() (in module networkx.algorithms.flow)": [[510, "networkx.algorithms.flow.shortest_augmenting_path"]], "weisfeiler_lehman_graph_hash() (in module networkx.algorithms.graph_hashing)": [[511, "networkx.algorithms.graph_hashing.weisfeiler_lehman_graph_hash"]], "weisfeiler_lehman_subgraph_hashes() (in module networkx.algorithms.graph_hashing)": [[512, "networkx.algorithms.graph_hashing.weisfeiler_lehman_subgraph_hashes"]], "is_digraphical() (in module networkx.algorithms.graphical)": [[513, "networkx.algorithms.graphical.is_digraphical"]], "is_graphical() (in module networkx.algorithms.graphical)": [[514, "networkx.algorithms.graphical.is_graphical"]], "is_multigraphical() (in module networkx.algorithms.graphical)": [[515, "networkx.algorithms.graphical.is_multigraphical"]], "is_pseudographical() (in module networkx.algorithms.graphical)": [[516, "networkx.algorithms.graphical.is_pseudographical"]], "is_valid_degree_sequence_erdos_gallai() (in module networkx.algorithms.graphical)": [[517, "networkx.algorithms.graphical.is_valid_degree_sequence_erdos_gallai"]], "is_valid_degree_sequence_havel_hakimi() (in module networkx.algorithms.graphical)": [[518, "networkx.algorithms.graphical.is_valid_degree_sequence_havel_hakimi"]], "flow_hierarchy() (in module networkx.algorithms.hierarchy)": [[519, "networkx.algorithms.hierarchy.flow_hierarchy"]], "is_kl_connected() (in module networkx.algorithms.hybrid)": [[520, "networkx.algorithms.hybrid.is_kl_connected"]], "kl_connected_subgraph() (in module networkx.algorithms.hybrid)": [[521, "networkx.algorithms.hybrid.kl_connected_subgraph"]], "is_isolate() (in module networkx.algorithms.isolate)": [[522, "networkx.algorithms.isolate.is_isolate"]], "isolates() (in module networkx.algorithms.isolate)": [[523, "networkx.algorithms.isolate.isolates"]], "number_of_isolates() (in module networkx.algorithms.isolate)": [[524, "networkx.algorithms.isolate.number_of_isolates"]], "__init__() (digraphmatcher method)": [[525, "networkx.algorithms.isomorphism.DiGraphMatcher.__init__"]], "candidate_pairs_iter() (digraphmatcher method)": [[526, "networkx.algorithms.isomorphism.DiGraphMatcher.candidate_pairs_iter"]], "initialize() (digraphmatcher method)": [[527, "networkx.algorithms.isomorphism.DiGraphMatcher.initialize"]], "is_isomorphic() (digraphmatcher method)": [[528, "networkx.algorithms.isomorphism.DiGraphMatcher.is_isomorphic"]], "isomorphisms_iter() (digraphmatcher method)": [[529, "networkx.algorithms.isomorphism.DiGraphMatcher.isomorphisms_iter"]], "match() (digraphmatcher method)": [[530, "networkx.algorithms.isomorphism.DiGraphMatcher.match"]], "semantic_feasibility() (digraphmatcher method)": [[531, "networkx.algorithms.isomorphism.DiGraphMatcher.semantic_feasibility"]], "subgraph_is_isomorphic() (digraphmatcher method)": [[532, "networkx.algorithms.isomorphism.DiGraphMatcher.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (digraphmatcher method)": [[533, "networkx.algorithms.isomorphism.DiGraphMatcher.subgraph_isomorphisms_iter"]], "syntactic_feasibility() (digraphmatcher method)": [[534, "networkx.algorithms.isomorphism.DiGraphMatcher.syntactic_feasibility"]], "__init__() (graphmatcher method)": [[535, "networkx.algorithms.isomorphism.GraphMatcher.__init__"]], "candidate_pairs_iter() (graphmatcher method)": [[536, "networkx.algorithms.isomorphism.GraphMatcher.candidate_pairs_iter"]], "initialize() (graphmatcher method)": [[537, "networkx.algorithms.isomorphism.GraphMatcher.initialize"]], "is_isomorphic() (graphmatcher method)": [[538, "networkx.algorithms.isomorphism.GraphMatcher.is_isomorphic"]], "isomorphisms_iter() (graphmatcher method)": [[539, "networkx.algorithms.isomorphism.GraphMatcher.isomorphisms_iter"]], "match() (graphmatcher method)": [[540, "networkx.algorithms.isomorphism.GraphMatcher.match"]], "semantic_feasibility() (graphmatcher method)": [[541, "networkx.algorithms.isomorphism.GraphMatcher.semantic_feasibility"]], "subgraph_is_isomorphic() (graphmatcher method)": [[542, "networkx.algorithms.isomorphism.GraphMatcher.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (graphmatcher method)": [[543, "networkx.algorithms.isomorphism.GraphMatcher.subgraph_isomorphisms_iter"]], "syntactic_feasibility() (graphmatcher method)": [[544, "networkx.algorithms.isomorphism.GraphMatcher.syntactic_feasibility"]], "ismags (class in networkx.algorithms.isomorphism)": [[545, "networkx.algorithms.isomorphism.ISMAGS"]], "__init__() (ismags method)": [[545, "networkx.algorithms.isomorphism.ISMAGS.__init__"]], "categorical_edge_match() (in module networkx.algorithms.isomorphism)": [[546, "networkx.algorithms.isomorphism.categorical_edge_match"]], "categorical_multiedge_match() (in module networkx.algorithms.isomorphism)": [[547, "networkx.algorithms.isomorphism.categorical_multiedge_match"]], "categorical_node_match() (in module networkx.algorithms.isomorphism)": [[548, "networkx.algorithms.isomorphism.categorical_node_match"]], "could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[549, "networkx.algorithms.isomorphism.could_be_isomorphic"]], "fast_could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[550, "networkx.algorithms.isomorphism.fast_could_be_isomorphic"]], "faster_could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[551, "networkx.algorithms.isomorphism.faster_could_be_isomorphic"]], "generic_edge_match() (in module networkx.algorithms.isomorphism)": [[552, "networkx.algorithms.isomorphism.generic_edge_match"]], "generic_multiedge_match() (in module networkx.algorithms.isomorphism)": [[553, "networkx.algorithms.isomorphism.generic_multiedge_match"]], "generic_node_match() (in module networkx.algorithms.isomorphism)": [[554, "networkx.algorithms.isomorphism.generic_node_match"]], "is_isomorphic() (in module networkx.algorithms.isomorphism)": [[555, "networkx.algorithms.isomorphism.is_isomorphic"]], "numerical_edge_match() (in module networkx.algorithms.isomorphism)": [[556, "networkx.algorithms.isomorphism.numerical_edge_match"]], "numerical_multiedge_match() (in module networkx.algorithms.isomorphism)": [[557, "networkx.algorithms.isomorphism.numerical_multiedge_match"]], "numerical_node_match() (in module networkx.algorithms.isomorphism)": [[558, "networkx.algorithms.isomorphism.numerical_node_match"]], "rooted_tree_isomorphism() (in module networkx.algorithms.isomorphism.tree_isomorphism)": [[559, "networkx.algorithms.isomorphism.tree_isomorphism.rooted_tree_isomorphism"]], "tree_isomorphism() (in module networkx.algorithms.isomorphism.tree_isomorphism)": [[560, "networkx.algorithms.isomorphism.tree_isomorphism.tree_isomorphism"]], "vf2pp_all_isomorphisms() (in module networkx.algorithms.isomorphism.vf2pp)": [[561, "networkx.algorithms.isomorphism.vf2pp.vf2pp_all_isomorphisms"]], "vf2pp_is_isomorphic() (in module networkx.algorithms.isomorphism.vf2pp)": [[562, "networkx.algorithms.isomorphism.vf2pp.vf2pp_is_isomorphic"]], "vf2pp_isomorphism() (in module networkx.algorithms.isomorphism.vf2pp)": [[563, "networkx.algorithms.isomorphism.vf2pp.vf2pp_isomorphism"]], "hits() (in module networkx.algorithms.link_analysis.hits_alg)": [[564, "networkx.algorithms.link_analysis.hits_alg.hits"]], "google_matrix() (in module networkx.algorithms.link_analysis.pagerank_alg)": [[565, "networkx.algorithms.link_analysis.pagerank_alg.google_matrix"]], "pagerank() (in module networkx.algorithms.link_analysis.pagerank_alg)": [[566, "networkx.algorithms.link_analysis.pagerank_alg.pagerank"]], "adamic_adar_index() (in module networkx.algorithms.link_prediction)": [[567, "networkx.algorithms.link_prediction.adamic_adar_index"]], "cn_soundarajan_hopcroft() (in module networkx.algorithms.link_prediction)": [[568, "networkx.algorithms.link_prediction.cn_soundarajan_hopcroft"]], "common_neighbor_centrality() (in module networkx.algorithms.link_prediction)": [[569, "networkx.algorithms.link_prediction.common_neighbor_centrality"]], "jaccard_coefficient() (in module networkx.algorithms.link_prediction)": [[570, "networkx.algorithms.link_prediction.jaccard_coefficient"]], "preferential_attachment() (in module networkx.algorithms.link_prediction)": [[571, "networkx.algorithms.link_prediction.preferential_attachment"]], "ra_index_soundarajan_hopcroft() (in module networkx.algorithms.link_prediction)": [[572, "networkx.algorithms.link_prediction.ra_index_soundarajan_hopcroft"]], "resource_allocation_index() (in module networkx.algorithms.link_prediction)": [[573, "networkx.algorithms.link_prediction.resource_allocation_index"]], "within_inter_cluster() (in module networkx.algorithms.link_prediction)": [[574, "networkx.algorithms.link_prediction.within_inter_cluster"]], "all_pairs_lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[575, "networkx.algorithms.lowest_common_ancestors.all_pairs_lowest_common_ancestor"]], "lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[576, "networkx.algorithms.lowest_common_ancestors.lowest_common_ancestor"]], "tree_all_pairs_lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[577, "networkx.algorithms.lowest_common_ancestors.tree_all_pairs_lowest_common_ancestor"]], "is_matching() (in module networkx.algorithms.matching)": [[578, "networkx.algorithms.matching.is_matching"]], "is_maximal_matching() (in module networkx.algorithms.matching)": [[579, "networkx.algorithms.matching.is_maximal_matching"]], "is_perfect_matching() (in module networkx.algorithms.matching)": [[580, "networkx.algorithms.matching.is_perfect_matching"]], "max_weight_matching() (in module networkx.algorithms.matching)": [[581, "networkx.algorithms.matching.max_weight_matching"]], "maximal_matching() (in module networkx.algorithms.matching)": [[582, "networkx.algorithms.matching.maximal_matching"]], "min_weight_matching() (in module networkx.algorithms.matching)": [[583, "networkx.algorithms.matching.min_weight_matching"]], "contracted_edge() (in module networkx.algorithms.minors)": [[584, "networkx.algorithms.minors.contracted_edge"]], "contracted_nodes() (in module networkx.algorithms.minors)": [[585, "networkx.algorithms.minors.contracted_nodes"]], "equivalence_classes() (in module networkx.algorithms.minors)": [[586, "networkx.algorithms.minors.equivalence_classes"]], "identified_nodes() (in module networkx.algorithms.minors)": [[587, "networkx.algorithms.minors.identified_nodes"]], "quotient_graph() (in module networkx.algorithms.minors)": [[588, "networkx.algorithms.minors.quotient_graph"]], "maximal_independent_set() (in module networkx.algorithms.mis)": [[589, "networkx.algorithms.mis.maximal_independent_set"]], "moral_graph() (in module networkx.algorithms.moral)": [[590, "networkx.algorithms.moral.moral_graph"]], "harmonic_function() (in module networkx.algorithms.node_classification)": [[591, "networkx.algorithms.node_classification.harmonic_function"]], "local_and_global_consistency() (in module networkx.algorithms.node_classification)": [[592, "networkx.algorithms.node_classification.local_and_global_consistency"]], "non_randomness() (in module networkx.algorithms.non_randomness)": [[593, "networkx.algorithms.non_randomness.non_randomness"]], "compose_all() (in module networkx.algorithms.operators.all)": [[594, "networkx.algorithms.operators.all.compose_all"]], "disjoint_union_all() (in module networkx.algorithms.operators.all)": [[595, "networkx.algorithms.operators.all.disjoint_union_all"]], "intersection_all() (in module networkx.algorithms.operators.all)": [[596, "networkx.algorithms.operators.all.intersection_all"]], "union_all() (in module networkx.algorithms.operators.all)": [[597, "networkx.algorithms.operators.all.union_all"]], "compose() (in module networkx.algorithms.operators.binary)": [[598, "networkx.algorithms.operators.binary.compose"]], "difference() (in module networkx.algorithms.operators.binary)": [[599, "networkx.algorithms.operators.binary.difference"]], "disjoint_union() (in module networkx.algorithms.operators.binary)": [[600, "networkx.algorithms.operators.binary.disjoint_union"]], "full_join() (in module networkx.algorithms.operators.binary)": [[601, "networkx.algorithms.operators.binary.full_join"]], "intersection() (in module networkx.algorithms.operators.binary)": [[602, "networkx.algorithms.operators.binary.intersection"]], "symmetric_difference() (in module networkx.algorithms.operators.binary)": [[603, "networkx.algorithms.operators.binary.symmetric_difference"]], "union() (in module networkx.algorithms.operators.binary)": [[604, "networkx.algorithms.operators.binary.union"]], "cartesian_product() (in module networkx.algorithms.operators.product)": [[605, "networkx.algorithms.operators.product.cartesian_product"]], "corona_product() (in module networkx.algorithms.operators.product)": [[606, "networkx.algorithms.operators.product.corona_product"]], "lexicographic_product() (in module networkx.algorithms.operators.product)": [[607, "networkx.algorithms.operators.product.lexicographic_product"]], "power() (in module networkx.algorithms.operators.product)": [[608, "networkx.algorithms.operators.product.power"]], "rooted_product() (in module networkx.algorithms.operators.product)": [[609, "networkx.algorithms.operators.product.rooted_product"]], "strong_product() (in module networkx.algorithms.operators.product)": [[610, "networkx.algorithms.operators.product.strong_product"]], "tensor_product() (in module networkx.algorithms.operators.product)": [[611, "networkx.algorithms.operators.product.tensor_product"]], "complement() (in module networkx.algorithms.operators.unary)": [[612, "networkx.algorithms.operators.unary.complement"]], "reverse() (in module networkx.algorithms.operators.unary)": [[613, "networkx.algorithms.operators.unary.reverse"]], "combinatorial_embedding_to_pos() (in module networkx.algorithms.planar_drawing)": [[614, "networkx.algorithms.planar_drawing.combinatorial_embedding_to_pos"]], "planarembedding (class in networkx.algorithms.planarity)": [[615, "networkx.algorithms.planarity.PlanarEmbedding"]], "__init__() (planarembedding method)": [[615, "networkx.algorithms.planarity.PlanarEmbedding.__init__"]], "check_planarity() (in module networkx.algorithms.planarity)": [[616, "networkx.algorithms.planarity.check_planarity"]], "is_planar() (in module networkx.algorithms.planarity)": [[617, "networkx.algorithms.planarity.is_planar"]], "chromatic_polynomial() (in module networkx.algorithms.polynomials)": [[618, "networkx.algorithms.polynomials.chromatic_polynomial"]], "tutte_polynomial() (in module networkx.algorithms.polynomials)": [[619, "networkx.algorithms.polynomials.tutte_polynomial"]], "overall_reciprocity() (in module networkx.algorithms.reciprocity)": [[620, "networkx.algorithms.reciprocity.overall_reciprocity"]], "reciprocity() (in module networkx.algorithms.reciprocity)": [[621, "networkx.algorithms.reciprocity.reciprocity"]], "is_k_regular() (in module networkx.algorithms.regular)": [[622, "networkx.algorithms.regular.is_k_regular"]], "is_regular() (in module networkx.algorithms.regular)": [[623, "networkx.algorithms.regular.is_regular"]], "k_factor() (in module networkx.algorithms.regular)": [[624, "networkx.algorithms.regular.k_factor"]], "rich_club_coefficient() (in module networkx.algorithms.richclub)": [[625, "networkx.algorithms.richclub.rich_club_coefficient"]], "astar_path() (in module networkx.algorithms.shortest_paths.astar)": [[626, "networkx.algorithms.shortest_paths.astar.astar_path"]], "astar_path_length() (in module networkx.algorithms.shortest_paths.astar)": [[627, "networkx.algorithms.shortest_paths.astar.astar_path_length"]], "floyd_warshall() (in module networkx.algorithms.shortest_paths.dense)": [[628, "networkx.algorithms.shortest_paths.dense.floyd_warshall"]], "floyd_warshall_numpy() (in module networkx.algorithms.shortest_paths.dense)": [[629, "networkx.algorithms.shortest_paths.dense.floyd_warshall_numpy"]], "floyd_warshall_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.dense)": [[630, "networkx.algorithms.shortest_paths.dense.floyd_warshall_predecessor_and_distance"]], "reconstruct_path() (in module networkx.algorithms.shortest_paths.dense)": [[631, "networkx.algorithms.shortest_paths.dense.reconstruct_path"]], "all_shortest_paths() (in module networkx.algorithms.shortest_paths.generic)": [[632, "networkx.algorithms.shortest_paths.generic.all_shortest_paths"]], "average_shortest_path_length() (in module networkx.algorithms.shortest_paths.generic)": [[633, "networkx.algorithms.shortest_paths.generic.average_shortest_path_length"]], "has_path() (in module networkx.algorithms.shortest_paths.generic)": [[634, "networkx.algorithms.shortest_paths.generic.has_path"]], "shortest_path() (in module networkx.algorithms.shortest_paths.generic)": [[635, "networkx.algorithms.shortest_paths.generic.shortest_path"]], "shortest_path_length() (in module networkx.algorithms.shortest_paths.generic)": [[636, "networkx.algorithms.shortest_paths.generic.shortest_path_length"]], "all_pairs_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[637, "networkx.algorithms.shortest_paths.unweighted.all_pairs_shortest_path"]], "all_pairs_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[638, "networkx.algorithms.shortest_paths.unweighted.all_pairs_shortest_path_length"]], "bidirectional_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[639, "networkx.algorithms.shortest_paths.unweighted.bidirectional_shortest_path"]], "predecessor() (in module networkx.algorithms.shortest_paths.unweighted)": [[640, "networkx.algorithms.shortest_paths.unweighted.predecessor"]], "single_source_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[641, "networkx.algorithms.shortest_paths.unweighted.single_source_shortest_path"]], "single_source_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[642, "networkx.algorithms.shortest_paths.unweighted.single_source_shortest_path_length"]], "single_target_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[643, "networkx.algorithms.shortest_paths.unweighted.single_target_shortest_path"]], "single_target_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[644, "networkx.algorithms.shortest_paths.unweighted.single_target_shortest_path_length"]], "all_pairs_bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[645, "networkx.algorithms.shortest_paths.weighted.all_pairs_bellman_ford_path"]], "all_pairs_bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[646, "networkx.algorithms.shortest_paths.weighted.all_pairs_bellman_ford_path_length"]], "all_pairs_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[647, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra"]], "all_pairs_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[648, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra_path"]], "all_pairs_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[649, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra_path_length"]], "bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[650, "networkx.algorithms.shortest_paths.weighted.bellman_ford_path"]], "bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[651, "networkx.algorithms.shortest_paths.weighted.bellman_ford_path_length"]], "bellman_ford_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.weighted)": [[652, "networkx.algorithms.shortest_paths.weighted.bellman_ford_predecessor_and_distance"]], "bidirectional_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[653, "networkx.algorithms.shortest_paths.weighted.bidirectional_dijkstra"]], "dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[654, "networkx.algorithms.shortest_paths.weighted.dijkstra_path"]], "dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[655, "networkx.algorithms.shortest_paths.weighted.dijkstra_path_length"]], "dijkstra_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.weighted)": [[656, "networkx.algorithms.shortest_paths.weighted.dijkstra_predecessor_and_distance"]], "find_negative_cycle() (in module networkx.algorithms.shortest_paths.weighted)": [[657, "networkx.algorithms.shortest_paths.weighted.find_negative_cycle"]], "goldberg_radzik() (in module networkx.algorithms.shortest_paths.weighted)": [[658, "networkx.algorithms.shortest_paths.weighted.goldberg_radzik"]], "johnson() (in module networkx.algorithms.shortest_paths.weighted)": [[659, "networkx.algorithms.shortest_paths.weighted.johnson"]], "multi_source_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[660, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra"]], "multi_source_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[661, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra_path"]], "multi_source_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[662, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra_path_length"]], "negative_edge_cycle() (in module networkx.algorithms.shortest_paths.weighted)": [[663, "networkx.algorithms.shortest_paths.weighted.negative_edge_cycle"]], "single_source_bellman_ford() (in module networkx.algorithms.shortest_paths.weighted)": [[664, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford"]], "single_source_bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[665, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford_path"]], "single_source_bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[666, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford_path_length"]], "single_source_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[667, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra"]], "single_source_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[668, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra_path"]], "single_source_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[669, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra_path_length"]], "generate_random_paths() (in module networkx.algorithms.similarity)": [[670, "networkx.algorithms.similarity.generate_random_paths"]], "graph_edit_distance() (in module networkx.algorithms.similarity)": [[671, "networkx.algorithms.similarity.graph_edit_distance"]], "optimal_edit_paths() (in module networkx.algorithms.similarity)": [[672, "networkx.algorithms.similarity.optimal_edit_paths"]], "optimize_edit_paths() (in module networkx.algorithms.similarity)": [[673, "networkx.algorithms.similarity.optimize_edit_paths"]], "optimize_graph_edit_distance() (in module networkx.algorithms.similarity)": [[674, "networkx.algorithms.similarity.optimize_graph_edit_distance"]], "panther_similarity() (in module networkx.algorithms.similarity)": [[675, "networkx.algorithms.similarity.panther_similarity"]], "simrank_similarity() (in module networkx.algorithms.similarity)": [[676, "networkx.algorithms.similarity.simrank_similarity"]], "all_simple_edge_paths() (in module networkx.algorithms.simple_paths)": [[677, "networkx.algorithms.simple_paths.all_simple_edge_paths"]], "all_simple_paths() (in module networkx.algorithms.simple_paths)": [[678, "networkx.algorithms.simple_paths.all_simple_paths"]], "is_simple_path() (in module networkx.algorithms.simple_paths)": [[679, "networkx.algorithms.simple_paths.is_simple_path"]], "shortest_simple_paths() (in module networkx.algorithms.simple_paths)": [[680, "networkx.algorithms.simple_paths.shortest_simple_paths"]], "lattice_reference() (in module networkx.algorithms.smallworld)": [[681, "networkx.algorithms.smallworld.lattice_reference"]], "omega() (in module networkx.algorithms.smallworld)": [[682, "networkx.algorithms.smallworld.omega"]], "random_reference() (in module networkx.algorithms.smallworld)": [[683, "networkx.algorithms.smallworld.random_reference"]], "sigma() (in module networkx.algorithms.smallworld)": [[684, "networkx.algorithms.smallworld.sigma"]], "s_metric() (in module networkx.algorithms.smetric)": [[685, "networkx.algorithms.smetric.s_metric"]], "spanner() (in module networkx.algorithms.sparsifiers)": [[686, "networkx.algorithms.sparsifiers.spanner"]], "constraint() (in module networkx.algorithms.structuralholes)": [[687, "networkx.algorithms.structuralholes.constraint"]], "effective_size() (in module networkx.algorithms.structuralholes)": [[688, "networkx.algorithms.structuralholes.effective_size"]], "local_constraint() (in module networkx.algorithms.structuralholes)": [[689, "networkx.algorithms.structuralholes.local_constraint"]], "dedensify() (in module networkx.algorithms.summarization)": [[690, "networkx.algorithms.summarization.dedensify"]], "snap_aggregation() (in module networkx.algorithms.summarization)": [[691, "networkx.algorithms.summarization.snap_aggregation"]], "connected_double_edge_swap() (in module networkx.algorithms.swap)": [[692, "networkx.algorithms.swap.connected_double_edge_swap"]], "directed_edge_swap() (in module networkx.algorithms.swap)": [[693, "networkx.algorithms.swap.directed_edge_swap"]], "double_edge_swap() (in module networkx.algorithms.swap)": [[694, "networkx.algorithms.swap.double_edge_swap"]], "find_threshold_graph() (in module networkx.algorithms.threshold)": [[695, "networkx.algorithms.threshold.find_threshold_graph"]], "is_threshold_graph() (in module networkx.algorithms.threshold)": [[696, "networkx.algorithms.threshold.is_threshold_graph"]], "hamiltonian_path() (in module networkx.algorithms.tournament)": [[697, "networkx.algorithms.tournament.hamiltonian_path"]], "is_reachable() (in module networkx.algorithms.tournament)": [[698, "networkx.algorithms.tournament.is_reachable"]], "is_strongly_connected() (in module networkx.algorithms.tournament)": [[699, "networkx.algorithms.tournament.is_strongly_connected"]], "is_tournament() (in module networkx.algorithms.tournament)": [[700, "networkx.algorithms.tournament.is_tournament"]], "random_tournament() (in module networkx.algorithms.tournament)": [[701, "networkx.algorithms.tournament.random_tournament"]], "score_sequence() (in module networkx.algorithms.tournament)": [[702, "networkx.algorithms.tournament.score_sequence"]], "bfs_beam_edges() (in module networkx.algorithms.traversal.beamsearch)": [[703, "networkx.algorithms.traversal.beamsearch.bfs_beam_edges"]], "bfs_edges() (in module networkx.algorithms.traversal.breadth_first_search)": [[704, "networkx.algorithms.traversal.breadth_first_search.bfs_edges"]], "bfs_layers() (in module networkx.algorithms.traversal.breadth_first_search)": [[705, "networkx.algorithms.traversal.breadth_first_search.bfs_layers"]], "bfs_predecessors() (in module networkx.algorithms.traversal.breadth_first_search)": [[706, "networkx.algorithms.traversal.breadth_first_search.bfs_predecessors"]], "bfs_successors() (in module networkx.algorithms.traversal.breadth_first_search)": [[707, "networkx.algorithms.traversal.breadth_first_search.bfs_successors"]], "bfs_tree() (in module networkx.algorithms.traversal.breadth_first_search)": [[708, "networkx.algorithms.traversal.breadth_first_search.bfs_tree"]], "descendants_at_distance() (in module networkx.algorithms.traversal.breadth_first_search)": [[709, "networkx.algorithms.traversal.breadth_first_search.descendants_at_distance"]], "dfs_edges() (in module networkx.algorithms.traversal.depth_first_search)": [[710, "networkx.algorithms.traversal.depth_first_search.dfs_edges"]], "dfs_labeled_edges() (in module networkx.algorithms.traversal.depth_first_search)": [[711, "networkx.algorithms.traversal.depth_first_search.dfs_labeled_edges"]], "dfs_postorder_nodes() (in module networkx.algorithms.traversal.depth_first_search)": [[712, "networkx.algorithms.traversal.depth_first_search.dfs_postorder_nodes"]], "dfs_predecessors() (in module networkx.algorithms.traversal.depth_first_search)": [[713, "networkx.algorithms.traversal.depth_first_search.dfs_predecessors"]], "dfs_preorder_nodes() (in module networkx.algorithms.traversal.depth_first_search)": [[714, "networkx.algorithms.traversal.depth_first_search.dfs_preorder_nodes"]], "dfs_successors() (in module networkx.algorithms.traversal.depth_first_search)": [[715, "networkx.algorithms.traversal.depth_first_search.dfs_successors"]], "dfs_tree() (in module networkx.algorithms.traversal.depth_first_search)": [[716, "networkx.algorithms.traversal.depth_first_search.dfs_tree"]], "edge_bfs() (in module networkx.algorithms.traversal.edgebfs)": [[717, "networkx.algorithms.traversal.edgebfs.edge_bfs"]], "edge_dfs() (in module networkx.algorithms.traversal.edgedfs)": [[718, "networkx.algorithms.traversal.edgedfs.edge_dfs"]], "arborescenceiterator (class in networkx.algorithms.tree.branchings)": [[719, "networkx.algorithms.tree.branchings.ArborescenceIterator"]], "__init__() (arborescenceiterator method)": [[719, "networkx.algorithms.tree.branchings.ArborescenceIterator.__init__"]], "edmonds (class in networkx.algorithms.tree.branchings)": [[720, "networkx.algorithms.tree.branchings.Edmonds"]], "__init__() (edmonds method)": [[720, "networkx.algorithms.tree.branchings.Edmonds.__init__"]], "branching_weight() (in module networkx.algorithms.tree.branchings)": [[721, "networkx.algorithms.tree.branchings.branching_weight"]], "greedy_branching() (in module networkx.algorithms.tree.branchings)": [[722, "networkx.algorithms.tree.branchings.greedy_branching"]], "maximum_branching() (in module networkx.algorithms.tree.branchings)": [[723, "networkx.algorithms.tree.branchings.maximum_branching"]], "maximum_spanning_arborescence() (in module networkx.algorithms.tree.branchings)": [[724, "networkx.algorithms.tree.branchings.maximum_spanning_arborescence"]], "minimum_branching() (in module networkx.algorithms.tree.branchings)": [[725, "networkx.algorithms.tree.branchings.minimum_branching"]], "minimum_spanning_arborescence() (in module networkx.algorithms.tree.branchings)": [[726, "networkx.algorithms.tree.branchings.minimum_spanning_arborescence"]], "notatree": [[727, "networkx.algorithms.tree.coding.NotATree"]], "from_nested_tuple() (in module networkx.algorithms.tree.coding)": [[728, "networkx.algorithms.tree.coding.from_nested_tuple"]], "from_prufer_sequence() (in module networkx.algorithms.tree.coding)": [[729, "networkx.algorithms.tree.coding.from_prufer_sequence"]], "to_nested_tuple() (in module networkx.algorithms.tree.coding)": [[730, "networkx.algorithms.tree.coding.to_nested_tuple"]], "to_prufer_sequence() (in module networkx.algorithms.tree.coding)": [[731, "networkx.algorithms.tree.coding.to_prufer_sequence"]], "junction_tree() (in module networkx.algorithms.tree.decomposition)": [[732, "networkx.algorithms.tree.decomposition.junction_tree"]], "spanningtreeiterator (class in networkx.algorithms.tree.mst)": [[733, "networkx.algorithms.tree.mst.SpanningTreeIterator"]], "__init__() (spanningtreeiterator method)": [[733, "networkx.algorithms.tree.mst.SpanningTreeIterator.__init__"]], "maximum_spanning_edges() (in module networkx.algorithms.tree.mst)": [[734, "networkx.algorithms.tree.mst.maximum_spanning_edges"]], "maximum_spanning_tree() (in module networkx.algorithms.tree.mst)": [[735, "networkx.algorithms.tree.mst.maximum_spanning_tree"]], "minimum_spanning_edges() (in module networkx.algorithms.tree.mst)": [[736, "networkx.algorithms.tree.mst.minimum_spanning_edges"]], "minimum_spanning_tree() (in module networkx.algorithms.tree.mst)": [[737, "networkx.algorithms.tree.mst.minimum_spanning_tree"]], "random_spanning_tree() (in module networkx.algorithms.tree.mst)": [[738, 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"order() (multigraph method)": [[1001, "networkx.MultiGraph.order"]], "remove_edge() (multigraph method)": [[1002, "networkx.MultiGraph.remove_edge"]], "remove_edges_from() (multigraph method)": [[1003, "networkx.MultiGraph.remove_edges_from"]], "remove_node() (multigraph method)": [[1004, "networkx.MultiGraph.remove_node"]], "remove_nodes_from() (multigraph method)": [[1005, "networkx.MultiGraph.remove_nodes_from"]], "size() (multigraph method)": [[1006, "networkx.MultiGraph.size"]], "subgraph() (multigraph method)": [[1007, "networkx.MultiGraph.subgraph"]], "to_directed() (multigraph method)": [[1008, "networkx.MultiGraph.to_directed"]], "to_undirected() (multigraph method)": [[1009, "networkx.MultiGraph.to_undirected"]], "update() (multigraph method)": [[1010, "networkx.MultiGraph.update"]], "_dispatch() (in module networkx.classes.backends)": [[1011, "networkx.classes.backends._dispatch"]], "adjacencyview (class in networkx.classes.coreviews)": [[1012, "networkx.classes.coreviews.AdjacencyView"]], "__init__() (adjacencyview method)": [[1012, "networkx.classes.coreviews.AdjacencyView.__init__"]], "atlasview (class in networkx.classes.coreviews)": [[1013, "networkx.classes.coreviews.AtlasView"]], "__init__() (atlasview method)": [[1013, "networkx.classes.coreviews.AtlasView.__init__"]], "filteradjacency (class in networkx.classes.coreviews)": [[1014, "networkx.classes.coreviews.FilterAdjacency"]], "__init__() (filteradjacency method)": [[1014, "networkx.classes.coreviews.FilterAdjacency.__init__"]], "filteratlas (class in networkx.classes.coreviews)": [[1015, "networkx.classes.coreviews.FilterAtlas"]], "__init__() (filteratlas method)": [[1015, "networkx.classes.coreviews.FilterAtlas.__init__"]], "filtermultiadjacency (class in networkx.classes.coreviews)": [[1016, "networkx.classes.coreviews.FilterMultiAdjacency"]], "__init__() (filtermultiadjacency method)": [[1016, "networkx.classes.coreviews.FilterMultiAdjacency.__init__"]], "filtermultiinner (class in networkx.classes.coreviews)": [[1017, "networkx.classes.coreviews.FilterMultiInner"]], "__init__() (filtermultiinner method)": [[1017, "networkx.classes.coreviews.FilterMultiInner.__init__"]], "multiadjacencyview (class in networkx.classes.coreviews)": [[1018, "networkx.classes.coreviews.MultiAdjacencyView"]], "__init__() (multiadjacencyview method)": [[1018, "networkx.classes.coreviews.MultiAdjacencyView.__init__"]], "unionadjacency (class in networkx.classes.coreviews)": [[1019, "networkx.classes.coreviews.UnionAdjacency"]], "__init__() (unionadjacency method)": [[1019, "networkx.classes.coreviews.UnionAdjacency.__init__"]], "unionatlas (class in networkx.classes.coreviews)": [[1020, "networkx.classes.coreviews.UnionAtlas"]], "__init__() (unionatlas method)": [[1020, "networkx.classes.coreviews.UnionAtlas.__init__"]], "unionmultiadjacency (class in networkx.classes.coreviews)": [[1021, "networkx.classes.coreviews.UnionMultiAdjacency"]], "__init__() 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networkx.classes.filters)": [[1029, "networkx.classes.filters.show_diedges"]], "show_edges() (in module networkx.classes.filters)": [[1030, "networkx.classes.filters.show_edges"]], "show_multidiedges() (in module networkx.classes.filters)": [[1031, "networkx.classes.filters.show_multidiedges"]], "show_multiedges() (in module networkx.classes.filters)": [[1032, "networkx.classes.filters.show_multiedges"]], "__init__() (show_nodes method)": [[1033, "networkx.classes.filters.show_nodes.__init__"]], "show_nodes (class in networkx.classes.filters)": [[1033, "networkx.classes.filters.show_nodes"]], "generic_graph_view() (in module networkx.classes.graphviews)": [[1034, "networkx.classes.graphviews.generic_graph_view"]], "reverse_view() (in module networkx.classes.graphviews)": [[1035, "networkx.classes.graphviews.reverse_view"]], "subgraph_view() (in module networkx.classes.graphviews)": [[1036, "networkx.classes.graphviews.subgraph_view"]], "graph (class in networkx)": [[1037, "networkx.Graph"]], "networkx.classes.backends": [[1038, "module-networkx.classes.backends"]], "networkx.classes.coreviews": [[1038, "module-networkx.classes.coreviews"]], "networkx.classes.filters": [[1038, "module-networkx.classes.filters"]], "networkx.classes.graphviews": [[1038, "module-networkx.classes.graphviews"]], "multidigraph (class in networkx)": [[1039, "networkx.MultiDiGraph"]], "multigraph (class in networkx)": [[1040, "networkx.MultiGraph"]], "networkx.convert": [[1041, "module-networkx.convert"]], "networkx.convert_matrix": [[1041, "module-networkx.convert_matrix"]], "networkx.drawing.layout": [[1042, "module-networkx.drawing.layout"]], "networkx.drawing.nx_agraph": [[1042, "module-networkx.drawing.nx_agraph"]], "networkx.drawing.nx_pydot": [[1042, "module-networkx.drawing.nx_pydot"]], "networkx.drawing.nx_pylab": [[1042, "module-networkx.drawing.nx_pylab"]], "ambiguoussolution (class in networkx)": [[1043, "networkx.AmbiguousSolution"]], "exceededmaxiterations (class in networkx)": [[1043, "networkx.ExceededMaxIterations"]], "hasacycle (class in networkx)": [[1043, "networkx.HasACycle"]], "networkxalgorithmerror (class in networkx)": [[1043, "networkx.NetworkXAlgorithmError"]], "networkxerror (class in networkx)": [[1043, "networkx.NetworkXError"]], "networkxexception (class in networkx)": [[1043, "networkx.NetworkXException"]], "networkxnocycle (class in networkx)": [[1043, "networkx.NetworkXNoCycle"]], "networkxnopath (class in networkx)": [[1043, "networkx.NetworkXNoPath"]], "networkxnotimplemented (class in networkx)": [[1043, "networkx.NetworkXNotImplemented"]], "networkxpointlessconcept (class in networkx)": [[1043, "networkx.NetworkXPointlessConcept"]], "networkxunbounded (class in networkx)": [[1043, "networkx.NetworkXUnbounded"]], "networkxunfeasible (class in networkx)": [[1043, "networkx.NetworkXUnfeasible"]], "nodenotfound (class in networkx)": [[1043, "networkx.NodeNotFound"]], "poweriterationfailedconvergence (class in networkx)": [[1043, "networkx.PowerIterationFailedConvergence"]], "networkx.exception": [[1043, "module-networkx.exception"]], "networkx.classes.function": [[1044, "module-networkx.classes.function"]], "assemble() (argmap method)": [[1045, "networkx.utils.decorators.argmap.assemble"]], "compile() (argmap method)": [[1046, "networkx.utils.decorators.argmap.compile"]], "signature() (argmap class method)": [[1047, "networkx.utils.decorators.argmap.signature"]], "pop() (mappedqueue method)": [[1048, "networkx.utils.mapped_queue.MappedQueue.pop"]], "push() (mappedqueue method)": [[1049, "networkx.utils.mapped_queue.MappedQueue.push"]], "remove() (mappedqueue method)": [[1050, "networkx.utils.mapped_queue.MappedQueue.remove"]], "update() (mappedqueue method)": [[1051, "networkx.utils.mapped_queue.MappedQueue.update"]], "add_cycle() (in module networkx.classes.function)": [[1052, "networkx.classes.function.add_cycle"]], "add_path() (in module networkx.classes.function)": [[1053, "networkx.classes.function.add_path"]], "add_star() (in module networkx.classes.function)": [[1054, "networkx.classes.function.add_star"]], "all_neighbors() (in module networkx.classes.function)": [[1055, "networkx.classes.function.all_neighbors"]], "common_neighbors() (in module networkx.classes.function)": [[1056, "networkx.classes.function.common_neighbors"]], "create_empty_copy() (in module networkx.classes.function)": [[1057, "networkx.classes.function.create_empty_copy"]], "degree() (in module networkx.classes.function)": [[1058, "networkx.classes.function.degree"]], "degree_histogram() (in module networkx.classes.function)": [[1059, "networkx.classes.function.degree_histogram"]], "density() (in module networkx.classes.function)": [[1060, "networkx.classes.function.density"]], "edge_subgraph() (in module networkx.classes.function)": [[1061, "networkx.classes.function.edge_subgraph"]], "edges() (in module networkx.classes.function)": [[1062, "networkx.classes.function.edges"]], "freeze() (in module networkx.classes.function)": [[1063, "networkx.classes.function.freeze"]], "get_edge_attributes() (in module networkx.classes.function)": [[1064, "networkx.classes.function.get_edge_attributes"]], "get_node_attributes() (in module networkx.classes.function)": [[1065, "networkx.classes.function.get_node_attributes"]], "induced_subgraph() (in module networkx.classes.function)": [[1066, "networkx.classes.function.induced_subgraph"]], "is_directed() (in module networkx.classes.function)": [[1067, "networkx.classes.function.is_directed"]], "is_empty() (in module networkx.classes.function)": [[1068, "networkx.classes.function.is_empty"]], "is_frozen() (in module networkx.classes.function)": [[1069, "networkx.classes.function.is_frozen"]], "is_negatively_weighted() (in module networkx.classes.function)": [[1070, "networkx.classes.function.is_negatively_weighted"]], "is_path() (in module networkx.classes.function)": [[1071, "networkx.classes.function.is_path"]], "is_weighted() (in module networkx.classes.function)": [[1072, "networkx.classes.function.is_weighted"]], "neighbors() (in module networkx.classes.function)": [[1073, "networkx.classes.function.neighbors"]], "nodes() (in module networkx.classes.function)": [[1074, "networkx.classes.function.nodes"]], "nodes_with_selfloops() (in module networkx.classes.function)": [[1075, "networkx.classes.function.nodes_with_selfloops"]], "non_edges() (in module networkx.classes.function)": [[1076, "networkx.classes.function.non_edges"]], "non_neighbors() (in module networkx.classes.function)": [[1077, "networkx.classes.function.non_neighbors"]], "number_of_edges() (in module networkx.classes.function)": [[1078, "networkx.classes.function.number_of_edges"]], "number_of_nodes() (in module networkx.classes.function)": [[1079, "networkx.classes.function.number_of_nodes"]], "number_of_selfloops() (in module networkx.classes.function)": [[1080, "networkx.classes.function.number_of_selfloops"]], "path_weight() (in module networkx.classes.function)": [[1081, "networkx.classes.function.path_weight"]], "restricted_view() (in module networkx.classes.function)": [[1082, "networkx.classes.function.restricted_view"]], "reverse_view() (in module networkx.classes.function)": [[1083, "networkx.classes.function.reverse_view"]], "selfloop_edges() (in module networkx.classes.function)": [[1084, "networkx.classes.function.selfloop_edges"]], "set_edge_attributes() (in module networkx.classes.function)": [[1085, "networkx.classes.function.set_edge_attributes"]], "set_node_attributes() (in module networkx.classes.function)": [[1086, "networkx.classes.function.set_node_attributes"]], "subgraph() (in module networkx.classes.function)": [[1087, "networkx.classes.function.subgraph"]], "subgraph_view() (in module networkx.classes.function)": [[1088, "networkx.classes.function.subgraph_view"]], "to_directed() (in module networkx.classes.function)": [[1089, "networkx.classes.function.to_directed"]], "to_undirected() (in module networkx.classes.function)": [[1090, "networkx.classes.function.to_undirected"]], "from_dict_of_dicts() (in module networkx.convert)": [[1091, "networkx.convert.from_dict_of_dicts"]], "from_dict_of_lists() (in module networkx.convert)": [[1092, "networkx.convert.from_dict_of_lists"]], "from_edgelist() (in module networkx.convert)": [[1093, "networkx.convert.from_edgelist"]], "to_dict_of_dicts() (in module networkx.convert)": [[1094, "networkx.convert.to_dict_of_dicts"]], "to_dict_of_lists() (in module networkx.convert)": [[1095, "networkx.convert.to_dict_of_lists"]], "to_edgelist() (in module networkx.convert)": [[1096, "networkx.convert.to_edgelist"]], "to_networkx_graph() (in module networkx.convert)": [[1097, "networkx.convert.to_networkx_graph"]], "from_numpy_array() (in module networkx.convert_matrix)": [[1098, "networkx.convert_matrix.from_numpy_array"]], "from_pandas_adjacency() (in module networkx.convert_matrix)": [[1099, "networkx.convert_matrix.from_pandas_adjacency"]], "from_pandas_edgelist() (in module networkx.convert_matrix)": [[1100, "networkx.convert_matrix.from_pandas_edgelist"]], "from_scipy_sparse_array() (in module networkx.convert_matrix)": [[1101, "networkx.convert_matrix.from_scipy_sparse_array"]], "to_numpy_array() (in module networkx.convert_matrix)": [[1102, "networkx.convert_matrix.to_numpy_array"]], "to_pandas_adjacency() (in module networkx.convert_matrix)": [[1103, "networkx.convert_matrix.to_pandas_adjacency"]], "to_pandas_edgelist() (in module networkx.convert_matrix)": [[1104, "networkx.convert_matrix.to_pandas_edgelist"]], "to_scipy_sparse_array() (in module networkx.convert_matrix)": [[1105, "networkx.convert_matrix.to_scipy_sparse_array"]], "bipartite_layout() (in module networkx.drawing.layout)": [[1106, "networkx.drawing.layout.bipartite_layout"]], "circular_layout() (in module networkx.drawing.layout)": [[1107, "networkx.drawing.layout.circular_layout"]], "kamada_kawai_layout() (in module networkx.drawing.layout)": [[1108, "networkx.drawing.layout.kamada_kawai_layout"]], "multipartite_layout() (in module networkx.drawing.layout)": [[1109, "networkx.drawing.layout.multipartite_layout"]], "planar_layout() (in module networkx.drawing.layout)": [[1110, "networkx.drawing.layout.planar_layout"]], "random_layout() (in module networkx.drawing.layout)": [[1111, "networkx.drawing.layout.random_layout"]], "rescale_layout() (in module networkx.drawing.layout)": [[1112, "networkx.drawing.layout.rescale_layout"]], "rescale_layout_dict() (in module networkx.drawing.layout)": [[1113, "networkx.drawing.layout.rescale_layout_dict"]], "shell_layout() (in module networkx.drawing.layout)": [[1114, "networkx.drawing.layout.shell_layout"]], "spectral_layout() (in module networkx.drawing.layout)": [[1115, "networkx.drawing.layout.spectral_layout"]], "spiral_layout() (in module networkx.drawing.layout)": [[1116, "networkx.drawing.layout.spiral_layout"]], "spring_layout() (in module networkx.drawing.layout)": [[1117, "networkx.drawing.layout.spring_layout"]], "from_agraph() (in module networkx.drawing.nx_agraph)": [[1118, "networkx.drawing.nx_agraph.from_agraph"]], "graphviz_layout() (in module networkx.drawing.nx_agraph)": [[1119, "networkx.drawing.nx_agraph.graphviz_layout"]], "pygraphviz_layout() (in module networkx.drawing.nx_agraph)": [[1120, "networkx.drawing.nx_agraph.pygraphviz_layout"]], "read_dot() (in module networkx.drawing.nx_agraph)": [[1121, "networkx.drawing.nx_agraph.read_dot"]], "to_agraph() (in module networkx.drawing.nx_agraph)": [[1122, "networkx.drawing.nx_agraph.to_agraph"]], "write_dot() (in module networkx.drawing.nx_agraph)": [[1123, "networkx.drawing.nx_agraph.write_dot"]], "from_pydot() (in module networkx.drawing.nx_pydot)": [[1124, "networkx.drawing.nx_pydot.from_pydot"]], "graphviz_layout() (in module networkx.drawing.nx_pydot)": [[1125, "networkx.drawing.nx_pydot.graphviz_layout"]], "pydot_layout() (in module networkx.drawing.nx_pydot)": [[1126, "networkx.drawing.nx_pydot.pydot_layout"]], "read_dot() (in module networkx.drawing.nx_pydot)": [[1127, "networkx.drawing.nx_pydot.read_dot"]], "to_pydot() (in module networkx.drawing.nx_pydot)": [[1128, "networkx.drawing.nx_pydot.to_pydot"]], "write_dot() (in module networkx.drawing.nx_pydot)": [[1129, "networkx.drawing.nx_pydot.write_dot"]], "draw() (in module networkx.drawing.nx_pylab)": [[1130, "networkx.drawing.nx_pylab.draw"]], "draw_circular() (in module networkx.drawing.nx_pylab)": [[1131, "networkx.drawing.nx_pylab.draw_circular"]], "draw_kamada_kawai() (in module networkx.drawing.nx_pylab)": [[1132, "networkx.drawing.nx_pylab.draw_kamada_kawai"]], "draw_networkx() (in module networkx.drawing.nx_pylab)": [[1133, "networkx.drawing.nx_pylab.draw_networkx"]], "draw_networkx_edge_labels() (in module networkx.drawing.nx_pylab)": [[1134, "networkx.drawing.nx_pylab.draw_networkx_edge_labels"]], "draw_networkx_edges() (in module networkx.drawing.nx_pylab)": [[1135, "networkx.drawing.nx_pylab.draw_networkx_edges"]], "draw_networkx_labels() (in module networkx.drawing.nx_pylab)": [[1136, "networkx.drawing.nx_pylab.draw_networkx_labels"]], "draw_networkx_nodes() (in module networkx.drawing.nx_pylab)": [[1137, "networkx.drawing.nx_pylab.draw_networkx_nodes"]], "draw_planar() (in module networkx.drawing.nx_pylab)": [[1138, "networkx.drawing.nx_pylab.draw_planar"]], "draw_random() (in module networkx.drawing.nx_pylab)": [[1139, "networkx.drawing.nx_pylab.draw_random"]], "draw_shell() (in module networkx.drawing.nx_pylab)": [[1140, "networkx.drawing.nx_pylab.draw_shell"]], "draw_spectral() (in module networkx.drawing.nx_pylab)": [[1141, "networkx.drawing.nx_pylab.draw_spectral"]], "draw_spring() (in module networkx.drawing.nx_pylab)": [[1142, "networkx.drawing.nx_pylab.draw_spring"]], "graph_atlas() (in module networkx.generators.atlas)": [[1143, "networkx.generators.atlas.graph_atlas"]], "graph_atlas_g() (in module networkx.generators.atlas)": [[1144, "networkx.generators.atlas.graph_atlas_g"]], "balanced_tree() (in module networkx.generators.classic)": [[1145, "networkx.generators.classic.balanced_tree"]], "barbell_graph() (in module networkx.generators.classic)": [[1146, "networkx.generators.classic.barbell_graph"]], "binomial_tree() (in module networkx.generators.classic)": [[1147, "networkx.generators.classic.binomial_tree"]], "circulant_graph() (in module networkx.generators.classic)": [[1148, "networkx.generators.classic.circulant_graph"]], "circular_ladder_graph() (in module networkx.generators.classic)": [[1149, "networkx.generators.classic.circular_ladder_graph"]], "complete_graph() (in module networkx.generators.classic)": [[1150, "networkx.generators.classic.complete_graph"]], "complete_multipartite_graph() (in module networkx.generators.classic)": [[1151, "networkx.generators.classic.complete_multipartite_graph"]], "cycle_graph() (in module networkx.generators.classic)": [[1152, "networkx.generators.classic.cycle_graph"]], "dorogovtsev_goltsev_mendes_graph() (in module networkx.generators.classic)": [[1153, "networkx.generators.classic.dorogovtsev_goltsev_mendes_graph"]], "empty_graph() (in module networkx.generators.classic)": [[1154, "networkx.generators.classic.empty_graph"]], "full_rary_tree() (in module networkx.generators.classic)": [[1155, "networkx.generators.classic.full_rary_tree"]], "ladder_graph() (in module networkx.generators.classic)": [[1156, "networkx.generators.classic.ladder_graph"]], "lollipop_graph() (in module networkx.generators.classic)": [[1157, "networkx.generators.classic.lollipop_graph"]], "null_graph() (in module networkx.generators.classic)": [[1158, "networkx.generators.classic.null_graph"]], "path_graph() (in module networkx.generators.classic)": [[1159, "networkx.generators.classic.path_graph"]], "star_graph() (in module networkx.generators.classic)": [[1160, "networkx.generators.classic.star_graph"]], "trivial_graph() (in module networkx.generators.classic)": [[1161, "networkx.generators.classic.trivial_graph"]], "turan_graph() (in module networkx.generators.classic)": [[1162, "networkx.generators.classic.turan_graph"]], "wheel_graph() (in module networkx.generators.classic)": [[1163, "networkx.generators.classic.wheel_graph"]], "random_cograph() (in module networkx.generators.cographs)": [[1164, "networkx.generators.cographs.random_cograph"]], "lfr_benchmark_graph() (in module networkx.generators.community)": [[1165, "networkx.generators.community.LFR_benchmark_graph"]], "caveman_graph() (in module networkx.generators.community)": [[1166, "networkx.generators.community.caveman_graph"]], "connected_caveman_graph() (in module networkx.generators.community)": [[1167, "networkx.generators.community.connected_caveman_graph"]], "gaussian_random_partition_graph() (in module networkx.generators.community)": [[1168, "networkx.generators.community.gaussian_random_partition_graph"]], "planted_partition_graph() (in module networkx.generators.community)": [[1169, "networkx.generators.community.planted_partition_graph"]], "random_partition_graph() (in module networkx.generators.community)": [[1170, "networkx.generators.community.random_partition_graph"]], "relaxed_caveman_graph() (in module networkx.generators.community)": [[1171, "networkx.generators.community.relaxed_caveman_graph"]], "ring_of_cliques() (in module networkx.generators.community)": [[1172, "networkx.generators.community.ring_of_cliques"]], "stochastic_block_model() (in module networkx.generators.community)": [[1173, "networkx.generators.community.stochastic_block_model"]], "windmill_graph() (in module networkx.generators.community)": [[1174, "networkx.generators.community.windmill_graph"]], "configuration_model() (in module networkx.generators.degree_seq)": [[1175, "networkx.generators.degree_seq.configuration_model"]], "degree_sequence_tree() (in module networkx.generators.degree_seq)": [[1176, "networkx.generators.degree_seq.degree_sequence_tree"]], "directed_configuration_model() (in module networkx.generators.degree_seq)": [[1177, "networkx.generators.degree_seq.directed_configuration_model"]], "directed_havel_hakimi_graph() (in module networkx.generators.degree_seq)": [[1178, "networkx.generators.degree_seq.directed_havel_hakimi_graph"]], "expected_degree_graph() (in module networkx.generators.degree_seq)": [[1179, "networkx.generators.degree_seq.expected_degree_graph"]], "havel_hakimi_graph() (in module networkx.generators.degree_seq)": [[1180, "networkx.generators.degree_seq.havel_hakimi_graph"]], "random_degree_sequence_graph() (in module networkx.generators.degree_seq)": [[1181, "networkx.generators.degree_seq.random_degree_sequence_graph"]], "gn_graph() (in module networkx.generators.directed)": [[1182, "networkx.generators.directed.gn_graph"]], "gnc_graph() (in module networkx.generators.directed)": [[1183, "networkx.generators.directed.gnc_graph"]], "gnr_graph() (in module networkx.generators.directed)": [[1184, "networkx.generators.directed.gnr_graph"]], "random_k_out_graph() (in module networkx.generators.directed)": [[1185, "networkx.generators.directed.random_k_out_graph"]], "scale_free_graph() (in module networkx.generators.directed)": [[1186, "networkx.generators.directed.scale_free_graph"]], "duplication_divergence_graph() (in module networkx.generators.duplication)": [[1187, "networkx.generators.duplication.duplication_divergence_graph"]], "partial_duplication_graph() (in module networkx.generators.duplication)": [[1188, "networkx.generators.duplication.partial_duplication_graph"]], "ego_graph() (in module networkx.generators.ego)": [[1189, "networkx.generators.ego.ego_graph"]], "chordal_cycle_graph() (in module networkx.generators.expanders)": [[1190, "networkx.generators.expanders.chordal_cycle_graph"]], "margulis_gabber_galil_graph() (in module networkx.generators.expanders)": [[1191, "networkx.generators.expanders.margulis_gabber_galil_graph"]], "paley_graph() (in module networkx.generators.expanders)": [[1192, "networkx.generators.expanders.paley_graph"]], "geographical_threshold_graph() (in module networkx.generators.geometric)": [[1193, "networkx.generators.geometric.geographical_threshold_graph"]], "geometric_edges() (in module networkx.generators.geometric)": [[1194, "networkx.generators.geometric.geometric_edges"]], "navigable_small_world_graph() (in module networkx.generators.geometric)": [[1195, "networkx.generators.geometric.navigable_small_world_graph"]], "random_geometric_graph() (in module networkx.generators.geometric)": [[1196, "networkx.generators.geometric.random_geometric_graph"]], "soft_random_geometric_graph() (in module networkx.generators.geometric)": [[1197, "networkx.generators.geometric.soft_random_geometric_graph"]], "thresholded_random_geometric_graph() (in module networkx.generators.geometric)": [[1198, "networkx.generators.geometric.thresholded_random_geometric_graph"]], "waxman_graph() (in module networkx.generators.geometric)": [[1199, "networkx.generators.geometric.waxman_graph"]], "hkn_harary_graph() (in module networkx.generators.harary_graph)": [[1200, "networkx.generators.harary_graph.hkn_harary_graph"]], "hnm_harary_graph() (in module networkx.generators.harary_graph)": [[1201, "networkx.generators.harary_graph.hnm_harary_graph"]], "random_internet_as_graph() (in module networkx.generators.internet_as_graphs)": [[1202, "networkx.generators.internet_as_graphs.random_internet_as_graph"]], "general_random_intersection_graph() (in module networkx.generators.intersection)": [[1203, "networkx.generators.intersection.general_random_intersection_graph"]], "k_random_intersection_graph() (in module networkx.generators.intersection)": [[1204, "networkx.generators.intersection.k_random_intersection_graph"]], "uniform_random_intersection_graph() (in module networkx.generators.intersection)": [[1205, "networkx.generators.intersection.uniform_random_intersection_graph"]], "interval_graph() (in module networkx.generators.interval_graph)": [[1206, "networkx.generators.interval_graph.interval_graph"]], "directed_joint_degree_graph() (in module networkx.generators.joint_degree_seq)": [[1207, "networkx.generators.joint_degree_seq.directed_joint_degree_graph"]], "is_valid_directed_joint_degree() (in module networkx.generators.joint_degree_seq)": [[1208, "networkx.generators.joint_degree_seq.is_valid_directed_joint_degree"]], "is_valid_joint_degree() (in module networkx.generators.joint_degree_seq)": [[1209, "networkx.generators.joint_degree_seq.is_valid_joint_degree"]], "joint_degree_graph() (in module networkx.generators.joint_degree_seq)": [[1210, "networkx.generators.joint_degree_seq.joint_degree_graph"]], "grid_2d_graph() (in module networkx.generators.lattice)": [[1211, "networkx.generators.lattice.grid_2d_graph"]], "grid_graph() (in module networkx.generators.lattice)": [[1212, "networkx.generators.lattice.grid_graph"]], "hexagonal_lattice_graph() (in module networkx.generators.lattice)": [[1213, "networkx.generators.lattice.hexagonal_lattice_graph"]], "hypercube_graph() (in module networkx.generators.lattice)": [[1214, "networkx.generators.lattice.hypercube_graph"]], "triangular_lattice_graph() (in module networkx.generators.lattice)": [[1215, "networkx.generators.lattice.triangular_lattice_graph"]], "inverse_line_graph() (in module networkx.generators.line)": [[1216, "networkx.generators.line.inverse_line_graph"]], "line_graph() (in module networkx.generators.line)": [[1217, "networkx.generators.line.line_graph"]], "mycielski_graph() (in module networkx.generators.mycielski)": [[1218, "networkx.generators.mycielski.mycielski_graph"]], "mycielskian() (in module networkx.generators.mycielski)": [[1219, "networkx.generators.mycielski.mycielskian"]], "nonisomorphic_trees() (in module networkx.generators.nonisomorphic_trees)": [[1220, "networkx.generators.nonisomorphic_trees.nonisomorphic_trees"]], "number_of_nonisomorphic_trees() (in module networkx.generators.nonisomorphic_trees)": [[1221, "networkx.generators.nonisomorphic_trees.number_of_nonisomorphic_trees"]], "random_clustered_graph() (in module networkx.generators.random_clustered)": [[1222, "networkx.generators.random_clustered.random_clustered_graph"]], "barabasi_albert_graph() (in module networkx.generators.random_graphs)": [[1223, "networkx.generators.random_graphs.barabasi_albert_graph"]], "binomial_graph() (in module networkx.generators.random_graphs)": [[1224, "networkx.generators.random_graphs.binomial_graph"]], "connected_watts_strogatz_graph() (in module networkx.generators.random_graphs)": [[1225, "networkx.generators.random_graphs.connected_watts_strogatz_graph"]], "dense_gnm_random_graph() (in module networkx.generators.random_graphs)": [[1226, "networkx.generators.random_graphs.dense_gnm_random_graph"]], "dual_barabasi_albert_graph() (in module networkx.generators.random_graphs)": [[1227, "networkx.generators.random_graphs.dual_barabasi_albert_graph"]], "erdos_renyi_graph() (in module networkx.generators.random_graphs)": [[1228, "networkx.generators.random_graphs.erdos_renyi_graph"]], "extended_barabasi_albert_graph() (in module networkx.generators.random_graphs)": [[1229, "networkx.generators.random_graphs.extended_barabasi_albert_graph"]], "fast_gnp_random_graph() (in module networkx.generators.random_graphs)": [[1230, "networkx.generators.random_graphs.fast_gnp_random_graph"]], "gnm_random_graph() (in module networkx.generators.random_graphs)": [[1231, "networkx.generators.random_graphs.gnm_random_graph"]], "gnp_random_graph() (in module networkx.generators.random_graphs)": [[1232, "networkx.generators.random_graphs.gnp_random_graph"]], "newman_watts_strogatz_graph() (in module networkx.generators.random_graphs)": [[1233, "networkx.generators.random_graphs.newman_watts_strogatz_graph"]], "powerlaw_cluster_graph() (in module networkx.generators.random_graphs)": [[1234, "networkx.generators.random_graphs.powerlaw_cluster_graph"]], "random_kernel_graph() (in module networkx.generators.random_graphs)": [[1235, "networkx.generators.random_graphs.random_kernel_graph"]], "random_lobster() (in module networkx.generators.random_graphs)": [[1236, "networkx.generators.random_graphs.random_lobster"]], "random_powerlaw_tree() (in module networkx.generators.random_graphs)": [[1237, "networkx.generators.random_graphs.random_powerlaw_tree"]], "random_powerlaw_tree_sequence() (in module networkx.generators.random_graphs)": [[1238, "networkx.generators.random_graphs.random_powerlaw_tree_sequence"]], "random_regular_graph() (in module networkx.generators.random_graphs)": [[1239, "networkx.generators.random_graphs.random_regular_graph"]], "random_shell_graph() (in module networkx.generators.random_graphs)": [[1240, "networkx.generators.random_graphs.random_shell_graph"]], "watts_strogatz_graph() (in module networkx.generators.random_graphs)": [[1241, "networkx.generators.random_graphs.watts_strogatz_graph"]], "lcf_graph() (in module networkx.generators.small)": [[1242, "networkx.generators.small.LCF_graph"]], "bull_graph() (in module networkx.generators.small)": [[1243, "networkx.generators.small.bull_graph"]], "chvatal_graph() (in module networkx.generators.small)": [[1244, "networkx.generators.small.chvatal_graph"]], "cubical_graph() (in module networkx.generators.small)": [[1245, "networkx.generators.small.cubical_graph"]], "desargues_graph() (in module networkx.generators.small)": [[1246, "networkx.generators.small.desargues_graph"]], "diamond_graph() (in module networkx.generators.small)": [[1247, "networkx.generators.small.diamond_graph"]], "dodecahedral_graph() (in module networkx.generators.small)": [[1248, "networkx.generators.small.dodecahedral_graph"]], "frucht_graph() (in module networkx.generators.small)": [[1249, "networkx.generators.small.frucht_graph"]], "heawood_graph() (in module networkx.generators.small)": [[1250, "networkx.generators.small.heawood_graph"]], "hoffman_singleton_graph() (in module networkx.generators.small)": [[1251, "networkx.generators.small.hoffman_singleton_graph"]], "house_graph() (in module networkx.generators.small)": [[1252, "networkx.generators.small.house_graph"]], "house_x_graph() (in module networkx.generators.small)": [[1253, "networkx.generators.small.house_x_graph"]], "icosahedral_graph() (in module networkx.generators.small)": [[1254, "networkx.generators.small.icosahedral_graph"]], "krackhardt_kite_graph() (in module networkx.generators.small)": [[1255, "networkx.generators.small.krackhardt_kite_graph"]], "moebius_kantor_graph() (in module networkx.generators.small)": [[1256, "networkx.generators.small.moebius_kantor_graph"]], "octahedral_graph() (in module networkx.generators.small)": [[1257, "networkx.generators.small.octahedral_graph"]], "pappus_graph() (in module networkx.generators.small)": [[1258, "networkx.generators.small.pappus_graph"]], "petersen_graph() (in module networkx.generators.small)": [[1259, "networkx.generators.small.petersen_graph"]], "sedgewick_maze_graph() (in module networkx.generators.small)": [[1260, "networkx.generators.small.sedgewick_maze_graph"]], "tetrahedral_graph() (in module networkx.generators.small)": [[1261, "networkx.generators.small.tetrahedral_graph"]], "truncated_cube_graph() (in module networkx.generators.small)": [[1262, "networkx.generators.small.truncated_cube_graph"]], "truncated_tetrahedron_graph() (in module networkx.generators.small)": [[1263, "networkx.generators.small.truncated_tetrahedron_graph"]], "tutte_graph() (in module networkx.generators.small)": [[1264, "networkx.generators.small.tutte_graph"]], "davis_southern_women_graph() (in module networkx.generators.social)": [[1265, "networkx.generators.social.davis_southern_women_graph"]], "florentine_families_graph() (in module networkx.generators.social)": [[1266, "networkx.generators.social.florentine_families_graph"]], "karate_club_graph() (in module networkx.generators.social)": [[1267, "networkx.generators.social.karate_club_graph"]], "les_miserables_graph() (in module networkx.generators.social)": [[1268, "networkx.generators.social.les_miserables_graph"]], "spectral_graph_forge() (in module networkx.generators.spectral_graph_forge)": [[1269, "networkx.generators.spectral_graph_forge.spectral_graph_forge"]], "stochastic_graph() (in module networkx.generators.stochastic)": [[1270, "networkx.generators.stochastic.stochastic_graph"]], "sudoku_graph() (in module networkx.generators.sudoku)": [[1271, "networkx.generators.sudoku.sudoku_graph"]], "prefix_tree() (in module networkx.generators.trees)": [[1272, "networkx.generators.trees.prefix_tree"]], "random_tree() (in module networkx.generators.trees)": [[1273, "networkx.generators.trees.random_tree"]], "triad_graph() (in module networkx.generators.triads)": [[1274, "networkx.generators.triads.triad_graph"]], "algebraic_connectivity() (in module networkx.linalg.algebraicconnectivity)": [[1275, "networkx.linalg.algebraicconnectivity.algebraic_connectivity"]], "fiedler_vector() (in module networkx.linalg.algebraicconnectivity)": [[1276, "networkx.linalg.algebraicconnectivity.fiedler_vector"]], "spectral_ordering() (in module networkx.linalg.algebraicconnectivity)": [[1277, "networkx.linalg.algebraicconnectivity.spectral_ordering"]], "attr_matrix() (in module networkx.linalg.attrmatrix)": [[1278, "networkx.linalg.attrmatrix.attr_matrix"]], "attr_sparse_matrix() (in module networkx.linalg.attrmatrix)": [[1279, "networkx.linalg.attrmatrix.attr_sparse_matrix"]], "bethe_hessian_matrix() (in module networkx.linalg.bethehessianmatrix)": [[1280, "networkx.linalg.bethehessianmatrix.bethe_hessian_matrix"]], "adjacency_matrix() (in module networkx.linalg.graphmatrix)": [[1281, "networkx.linalg.graphmatrix.adjacency_matrix"]], "incidence_matrix() (in module networkx.linalg.graphmatrix)": [[1282, "networkx.linalg.graphmatrix.incidence_matrix"]], "directed_combinatorial_laplacian_matrix() (in module networkx.linalg.laplacianmatrix)": [[1283, "networkx.linalg.laplacianmatrix.directed_combinatorial_laplacian_matrix"]], "directed_laplacian_matrix() (in module networkx.linalg.laplacianmatrix)": [[1284, "networkx.linalg.laplacianmatrix.directed_laplacian_matrix"]], "laplacian_matrix() (in module networkx.linalg.laplacianmatrix)": [[1285, "networkx.linalg.laplacianmatrix.laplacian_matrix"]], "normalized_laplacian_matrix() (in module networkx.linalg.laplacianmatrix)": [[1286, "networkx.linalg.laplacianmatrix.normalized_laplacian_matrix"]], "directed_modularity_matrix() (in module networkx.linalg.modularitymatrix)": [[1287, "networkx.linalg.modularitymatrix.directed_modularity_matrix"]], "modularity_matrix() (in module networkx.linalg.modularitymatrix)": [[1288, "networkx.linalg.modularitymatrix.modularity_matrix"]], "adjacency_spectrum() (in module networkx.linalg.spectrum)": [[1289, "networkx.linalg.spectrum.adjacency_spectrum"]], "bethe_hessian_spectrum() (in module networkx.linalg.spectrum)": [[1290, "networkx.linalg.spectrum.bethe_hessian_spectrum"]], "laplacian_spectrum() (in module networkx.linalg.spectrum)": [[1291, "networkx.linalg.spectrum.laplacian_spectrum"]], "modularity_spectrum() (in module networkx.linalg.spectrum)": [[1292, "networkx.linalg.spectrum.modularity_spectrum"]], "normalized_laplacian_spectrum() (in module networkx.linalg.spectrum)": [[1293, "networkx.linalg.spectrum.normalized_laplacian_spectrum"]], "convert_node_labels_to_integers() (in module networkx.relabel)": [[1294, "networkx.relabel.convert_node_labels_to_integers"]], "relabel_nodes() (in module networkx.relabel)": [[1295, "networkx.relabel.relabel_nodes"]], "__init__() (argmap method)": [[1296, "networkx.utils.decorators.argmap.__init__"]], "argmap (class in networkx.utils.decorators)": [[1296, "networkx.utils.decorators.argmap"]], "nodes_or_number() (in module networkx.utils.decorators)": [[1297, "networkx.utils.decorators.nodes_or_number"]], "not_implemented_for() (in module networkx.utils.decorators)": [[1298, "networkx.utils.decorators.not_implemented_for"]], "np_random_state() (in module networkx.utils.decorators)": [[1299, "networkx.utils.decorators.np_random_state"]], "open_file() (in module networkx.utils.decorators)": [[1300, "networkx.utils.decorators.open_file"]], "py_random_state() (in module networkx.utils.decorators)": [[1301, "networkx.utils.decorators.py_random_state"]], "mappedqueue (class in networkx.utils.mapped_queue)": [[1302, "networkx.utils.mapped_queue.MappedQueue"]], "__init__() (mappedqueue method)": [[1302, "networkx.utils.mapped_queue.MappedQueue.__init__"]], "arbitrary_element() (in module networkx.utils.misc)": [[1303, "networkx.utils.misc.arbitrary_element"]], "create_py_random_state() (in module networkx.utils.misc)": [[1304, "networkx.utils.misc.create_py_random_state"]], "create_random_state() (in module networkx.utils.misc)": [[1305, "networkx.utils.misc.create_random_state"]], "dict_to_numpy_array() (in module networkx.utils.misc)": [[1306, "networkx.utils.misc.dict_to_numpy_array"]], "edges_equal() (in module networkx.utils.misc)": [[1307, "networkx.utils.misc.edges_equal"]], "flatten() (in module networkx.utils.misc)": [[1308, "networkx.utils.misc.flatten"]], "graphs_equal() (in module networkx.utils.misc)": [[1309, "networkx.utils.misc.graphs_equal"]], "groups() (in module networkx.utils.misc)": [[1310, "networkx.utils.misc.groups"]], "make_list_of_ints() (in module networkx.utils.misc)": [[1311, "networkx.utils.misc.make_list_of_ints"]], "nodes_equal() (in module networkx.utils.misc)": [[1312, "networkx.utils.misc.nodes_equal"]], "pairwise() (in module networkx.utils.misc)": [[1313, "networkx.utils.misc.pairwise"]], "cumulative_distribution() (in module networkx.utils.random_sequence)": [[1314, "networkx.utils.random_sequence.cumulative_distribution"]], "discrete_sequence() (in module networkx.utils.random_sequence)": [[1315, "networkx.utils.random_sequence.discrete_sequence"]], "powerlaw_sequence() (in module networkx.utils.random_sequence)": [[1316, "networkx.utils.random_sequence.powerlaw_sequence"]], "random_weighted_sample() (in module networkx.utils.random_sequence)": [[1317, "networkx.utils.random_sequence.random_weighted_sample"]], "weighted_choice() (in module networkx.utils.random_sequence)": [[1318, "networkx.utils.random_sequence.weighted_choice"]], "zipf_rv() (in module networkx.utils.random_sequence)": [[1319, "networkx.utils.random_sequence.zipf_rv"]], "cuthill_mckee_ordering() (in module networkx.utils.rcm)": [[1320, "networkx.utils.rcm.cuthill_mckee_ordering"]], "reverse_cuthill_mckee_ordering() (in module networkx.utils.rcm)": [[1321, "networkx.utils.rcm.reverse_cuthill_mckee_ordering"]], "union() (unionfind method)": [[1322, "networkx.utils.union_find.UnionFind.union"]], "networkx.generators.atlas": [[1323, "module-networkx.generators.atlas"]], "networkx.generators.classic": [[1323, "module-networkx.generators.classic"]], "networkx.generators.cographs": [[1323, "module-networkx.generators.cographs"]], "networkx.generators.community": [[1323, "module-networkx.generators.community"]], "networkx.generators.degree_seq": [[1323, "module-networkx.generators.degree_seq"]], "networkx.generators.directed": [[1323, "module-networkx.generators.directed"]], "networkx.generators.duplication": [[1323, "module-networkx.generators.duplication"]], "networkx.generators.ego": [[1323, "module-networkx.generators.ego"]], "networkx.generators.expanders": [[1323, "module-networkx.generators.expanders"]], "networkx.generators.geometric": [[1323, "module-networkx.generators.geometric"]], "networkx.generators.harary_graph": [[1323, 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"PlanarEmbedding.connect_components", "PlanarEmbedding.copy", "PlanarEmbedding.degree", "PlanarEmbedding.edge_subgraph", "PlanarEmbedding.edges", "PlanarEmbedding.get_data", "PlanarEmbedding.get_edge_data", "PlanarEmbedding.has_edge", "PlanarEmbedding.has_node", "PlanarEmbedding.has_predecessor", "PlanarEmbedding.has_successor", "PlanarEmbedding.in_degree", "PlanarEmbedding.in_edges", "PlanarEmbedding.is_directed", "PlanarEmbedding.is_multigraph", "PlanarEmbedding.name", "PlanarEmbedding.nbunch_iter", "PlanarEmbedding.neighbors", "PlanarEmbedding.neighbors_cw_order", "PlanarEmbedding.next_face_half_edge", "PlanarEmbedding.nodes", "PlanarEmbedding.number_of_edges", "PlanarEmbedding.number_of_nodes", "PlanarEmbedding.order", "PlanarEmbedding.out_degree", "PlanarEmbedding.out_edges", "PlanarEmbedding.pred", "PlanarEmbedding.predecessors", "PlanarEmbedding.remove_edge", "PlanarEmbedding.remove_edges_from", "PlanarEmbedding.remove_node", "PlanarEmbedding.remove_nodes_from", 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562, 563, 586, 619, 682, 691, 703, 704, 717, 730, 753, 762, 786, 1036, 1085, 1086, 1088, 1112, 1117, 1140, 1150, 1168, 1175, 1185, 1196, 1197, 1198, 1215, 1235, 1295, 1307, 1309, 1312, 1326, 1336, 1393, 1405, 1412, 1414, 1426], "posbm": 7, "xy": [7, 245], "212": 7, "392": [7, 17], "plot_blockmodel": [7, 17], "convert": [8, 34, 50, 52, 54, 55, 56, 57, 58, 74, 75, 99, 102, 112, 169, 266, 267, 293, 375, 464, 565, 566, 615, 676, 679, 850, 895, 931, 934, 976, 979, 1038, 1085, 1097, 1098, 1099, 1166, 1167, 1273, 1281, 1296, 1297, 1299, 1301, 1306, 1310, 1325, 1332, 1333, 1336, 1337, 1338, 1342, 1345, 1346, 1347, 1348, 1349, 1350, 1353, 1356, 1357, 1361, 1362, 1363, 1364, 1370, 1371, 1376, 1379, 1403, 1404, 1406, 1409, 1411, 1412, 1413, 1416, 1421, 1426], "formula": [8, 299, 316, 322, 380, 385, 618, 688, 1421], "can": [8, 15, 24, 34, 38, 40, 43, 52, 54, 55, 56, 57, 58, 67, 69, 70, 71, 75, 76, 84, 88, 91, 92, 93, 94, 95, 96, 99, 100, 101, 102, 103, 105, 107, 110, 111, 112, 115, 125, 132, 141, 142, 143, 144, 151, 152, 156, 157, 158, 165, 168, 171, 176, 180, 184, 185, 189, 190, 193, 199, 200, 207, 220, 222, 224, 227, 229, 230, 231, 238, 239, 240, 243, 251, 260, 261, 262, 264, 278, 281, 282, 297, 298, 301, 302, 305, 306, 307, 308, 309, 315, 316, 324, 325, 329, 330, 332, 333, 337, 339, 340, 342, 344, 345, 346, 347, 353, 354, 357, 358, 361, 362, 374, 376, 380, 382, 383, 385, 387, 388, 389, 390, 394, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 422, 423, 427, 439, 440, 449, 454, 456, 458, 460, 461, 464, 465, 466, 471, 472, 473, 474, 475, 491, 492, 494, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 530, 540, 553, 575, 577, 581, 586, 588, 597, 598, 601, 602, 604, 615, 616, 617, 619, 626, 628, 629, 630, 633, 641, 643, 647, 652, 653, 654, 655, 657, 658, 660, 661, 662, 667, 668, 669, 676, 677, 678, 679, 680, 687, 688, 689, 690, 691, 720, 722, 723, 724, 725, 726, 729, 730, 731, 748, 749, 751, 762, 767, 770, 775, 786, 791, 796, 850, 853, 854, 855, 856, 857, 862, 865, 867, 870, 871, 873, 874, 878, 879, 882, 887, 888, 892, 895, 898, 899, 900, 901, 902, 907, 910, 912, 914, 916, 917, 921, 925, 928, 931, 934, 935, 936, 937, 938, 943, 946, 947, 948, 951, 952, 955, 956, 960, 963, 968, 973, 976, 979, 980, 981, 982, 983, 988, 991, 992, 993, 995, 998, 999, 1003, 1007, 1010, 1036, 1037, 1038, 1039, 1040, 1042, 1045, 1047, 1059, 1060, 1061, 1063, 1066, 1068, 1082, 1085, 1088, 1102, 1103, 1105, 1129, 1133, 1135, 1137, 1148, 1151, 1154, 1164, 1165, 1166, 1167, 1174, 1175, 1177, 1193, 1196, 1197, 1198, 1206, 1207, 1217, 1218, 1219, 1222, 1235, 1246, 1248, 1250, 1258, 1263, 1264, 1269, 1272, 1275, 1276, 1278, 1279, 1281, 1282, 1283, 1284, 1295, 1296, 1297, 1299, 1301, 1302, 1303, 1320, 1321, 1323, 1324, 1326, 1328, 1329, 1330, 1333, 1334, 1347, 1349, 1352, 1354, 1356, 1357, 1362, 1363, 1371, 1372, 1378, 1380, 1382, 1385, 1387, 1388, 1392, 1393, 1394, 1395, 1396, 1399, 1402, 1404, 1405, 1406, 1408, 1409, 1412, 1425, 1426], "more": [8, 43, 53, 67, 86, 92, 93, 94, 97, 99, 100, 101, 102, 103, 107, 109, 110, 111, 114, 115, 121, 127, 128, 143, 165, 172, 198, 199, 202, 204, 215, 216, 218, 219, 220, 221, 230, 231, 235, 256, 267, 277, 278, 281, 289, 299, 310, 314, 324, 325, 335, 338, 361, 378, 383, 385, 387, 389, 390, 392, 399, 405, 406, 407, 422, 427, 428, 432, 433, 437, 460, 464, 480, 520, 521, 559, 560, 581, 582, 583, 590, 593, 614, 619, 626, 631, 635, 653, 656, 660, 661, 662, 676, 679, 683, 691, 698, 699, 703, 711, 717, 718, 735, 737, 748, 760, 782, 786, 796, 862, 868, 886, 887, 890, 891, 907, 913, 924, 925, 926, 927, 943, 949, 967, 968, 971, 972, 988, 994, 1006, 1007, 1008, 1009, 1037, 1039, 1040, 1042, 1043, 1071, 1094, 1100, 1116, 1119, 1120, 1123, 1130, 1131, 1132, 1133, 1135, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1185, 1192, 1193, 1206, 1214, 1217, 1218, 1219, 1272, 1287, 1288, 1295, 1296, 1297, 1323, 1326, 1328, 1337, 1345, 1348, 1349, 1350, 1390, 1394, 1395, 1397, 1398, 1399, 1401, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426], "express": [8, 92, 110, 184, 315, 329, 330, 383, 384, 618, 619, 873, 916, 955, 998, 1199, 1287, 1326], "than": [8, 11, 34, 43, 55, 97, 99, 101, 102, 103, 115, 128, 142, 143, 144, 161, 199, 214, 215, 216, 218, 219, 221, 227, 231, 235, 241, 256, 277, 278, 281, 288, 289, 297, 298, 299, 304, 306, 307, 310, 311, 315, 316, 321, 324, 325, 326, 328, 329, 330, 341, 352, 358, 361, 374, 380, 381, 383, 384, 385, 387, 389, 390, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 425, 426, 429, 435, 464, 468, 469, 500, 527, 537, 559, 560, 581, 582, 583, 590, 625, 626, 635, 636, 652, 653, 656, 658, 659, 673, 676, 678, 679, 681, 683, 686, 690, 692, 693, 694, 698, 699, 711, 731, 735, 737, 748, 752, 761, 786, 887, 925, 947, 968, 992, 1007, 1038, 1042, 1043, 1060, 1102, 1135, 1146, 1154, 1162, 1165, 1167, 1172, 1174, 1185, 1187, 1194, 1198, 1226, 1230, 1231, 1236, 1237, 1238, 1239, 1275, 1276, 1296, 1297, 1326, 1328, 1345, 1348, 1349, 1350, 1353, 1354, 1358, 1365, 1366, 1379, 1382, 1395, 1402, 1404, 1405, 1408, 1413, 1423, 1425], "worst": [8, 210, 211, 212, 221, 228, 235, 264, 293, 294, 338, 345, 346, 347, 440, 513, 515, 516, 517, 518], "reus": [8, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 1131, 1132, 1138, 1139, 1140, 1141, 1142, 1328, 1402], "subcircuit": 8, "multipl": [8, 11, 25, 40, 45, 77, 93, 94, 99, 103, 107, 109, 143, 157, 158, 166, 175, 188, 195, 207, 287, 311, 357, 385, 386, 423, 443, 447, 458, 460, 464, 485, 486, 487, 594, 595, 597, 615, 616, 641, 643, 678, 690, 691, 697, 705, 738, 762, 786, 796, 856, 857, 863, 869, 877, 884, 892, 901, 902, 908, 923, 928, 937, 938, 944, 946, 950, 959, 960, 962, 963, 965, 973, 982, 983, 989, 991, 1002, 1003, 1005, 1010, 1037, 1039, 1040, 1045, 1046, 1102, 1103, 1105, 1127, 1135, 1137, 1216, 1217, 1219, 1285, 1291, 1296, 1298, 1326, 1352, 1378, 1393, 1405, 1406, 1412, 1413, 1417, 1425, 1426], "wherea": [8, 103, 682, 762, 786, 791, 1165, 1417], "cannot": [8, 101, 103, 127, 132, 199, 232, 300, 362, 394, 476, 581, 582, 583, 584, 632, 722, 887, 925, 934, 968, 979, 1007, 1043, 1165, 1208, 1209, 1296, 1298, 1302, 1303, 1326, 1345, 1347, 1348, 1349, 1350], "subformula": 8, "onc": [8, 38, 54, 55, 88, 93, 94, 99, 100, 112, 127, 199, 227, 230, 231, 232, 246, 247, 360, 374, 380, 388, 422, 423, 428, 488, 491, 492, 581, 582, 583, 652, 678, 679, 717, 718, 887, 925, 968, 1007, 1046, 1066, 1087, 1217, 1311, 1326, 1403, 1407], "thu": [8, 88, 101, 103, 115, 215, 216, 220, 256, 258, 331, 418, 419, 427, 428, 462, 477, 500, 512, 583, 679, 698, 699, 760, 762, 796, 1037, 1039, 1040, 1043, 1087, 1112, 1148, 1215, 1217, 1234, 1278, 1279, 1296, 1328, 1402, 1405, 1407], "wai": [8, 27, 52, 53, 55, 75, 86, 88, 93, 97, 99, 100, 101, 102, 103, 104, 107, 110, 115, 132, 152, 157, 158, 165, 184, 226, 281, 297, 298, 315, 330, 337, 356, 588, 598, 615, 618, 678, 691, 730, 760, 791, 796, 854, 856, 857, 862, 873, 899, 901, 902, 907, 915, 916, 935, 937, 938, 943, 955, 980, 982, 983, 988, 996, 998, 1037, 1039, 1040, 1041, 1097, 1165, 1213, 1215, 1217, 1239, 1262, 1269, 1272, 1326, 1328, 1330, 1393, 1394, 1404, 1406, 1411, 1426], "infeas": [8, 422], "circuit_to_formula": 8, "dag_to_branch": [8, 758, 1408], "transfer": [8, 202, 204, 230, 231, 469, 890, 891, 926, 927, 971, 972, 1008, 1009, 1420], "oper": [8, 30, 52, 95, 101, 112, 115, 168, 184, 189, 227, 374, 423, 460, 546, 547, 548, 552, 553, 554, 577, 595, 598, 601, 671, 672, 673, 674, 679, 680, 758, 786, 865, 873, 878, 910, 916, 946, 955, 960, 991, 998, 1036, 1068, 1088, 1103, 1164, 1218, 1219, 1295, 1302, 1319, 1323, 1325, 1326, 1393, 1394, 1400, 1404, 1405, 1406, 1407, 1408, 1411, 1412, 1413, 1414, 1417], "variabl": [8, 94, 132, 373, 530, 540, 618, 619, 732, 796, 1037, 1038, 1039, 1040, 1154, 1165, 1326, 1408, 1412, 1413, 1414, 1420], "formula_to_str": 8, "_to_str": 8, "root": [8, 67, 84, 293, 294, 338, 387, 389, 390, 394, 449, 460, 559, 577, 609, 671, 673, 678, 704, 728, 730, 739, 760, 791, 1119, 1120, 1125, 1126, 1145, 1147, 1235, 1271, 1272, 1323, 1365, 1366, 1393, 1406, 1407, 1408, 1412, 1413, 1423, 1425], "children": [8, 460, 577, 1145, 1155, 1272, 1365, 1366], "otherwis": [8, 92, 110, 146, 149, 171, 178, 184, 185, 198, 217, 230, 249, 250, 284, 297, 298, 303, 306, 307, 311, 315, 316, 322, 323, 324, 325, 326, 329, 330, 343, 353, 358, 393, 394, 395, 396, 397, 398, 410, 411, 412, 418, 419, 422, 425, 426, 462, 463, 464, 470, 479, 488, 490, 494, 495, 496, 498, 499, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 521, 555, 562, 563, 568, 572, 574, 584, 586, 588, 597, 601, 616, 618, 619, 633, 663, 673, 687, 688, 689, 696, 698, 699, 734, 735, 736, 737, 751, 848, 867, 873, 874, 886, 893, 912, 916, 917, 924, 929, 934, 948, 955, 956, 967, 974, 979, 993, 998, 999, 1006, 1068, 1091, 1135, 1137, 1165, 1185, 1197, 1217, 1270, 1282, 1283, 1284, 1307, 1309, 1312, 1342, 1356, 1357, 1376, 1409, 1413, 1426], "child": [8, 1147, 1272], "must": [8, 11, 93, 94, 95, 99, 100, 103, 110, 151, 152, 158, 161, 171, 204, 206, 207, 214, 215, 216, 219, 230, 231, 232, 252, 253, 257, 258, 259, 260, 261, 262, 264, 267, 268, 269, 271, 273, 276, 281, 285, 297, 298, 306, 307, 315, 316, 317, 318, 319, 324, 325, 327, 329, 330, 342, 361, 362, 363, 378, 382, 385, 391, 410, 411, 412, 413, 425, 429, 440, 471, 472, 473, 474, 475, 545, 546, 547, 548, 549, 550, 551, 553, 555, 556, 557, 558, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 577, 578, 579, 580, 584, 585, 586, 587, 588, 589, 593, 597, 599, 601, 602, 603, 604, 615, 626, 627, 632, 633, 635, 636, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 671, 672, 673, 674, 680, 690, 692, 698, 699, 707, 721, 734, 735, 736, 737, 789, 796, 853, 854, 857, 867, 891, 892, 898, 899, 902, 912, 928, 934, 938, 972, 973, 979, 983, 1010, 1037, 1038, 1039, 1040, 1063, 1071, 1085, 1102, 1133, 1137, 1146, 1162, 1165, 1173, 1176, 1186, 1188, 1190, 1193, 1197, 1199, 1209, 1213, 1217, 1219, 1235, 1239, 1240, 1270, 1275, 1276, 1277, 1278, 1279, 1295, 1296, 1298, 1307, 1309, 1310, 1311, 1312, 1315, 1333, 1337, 1338, 1339, 1340, 1359, 1361, 1362, 1363, 1364, 1365, 1366, 1376, 1393, 1394, 1395, 1407, 1426], "NOT": [8, 110, 199, 549, 550, 551, 748, 887, 925, 968, 1007], "util": [8, 14, 36, 44, 45, 93, 97, 102, 103, 229, 230, 231, 316, 374, 423, 425, 426, 429, 460, 496, 678, 679, 758, 1044, 1242, 1299, 1301, 1303, 1310, 1319, 1320, 1321, 1325, 1402, 1406, 1407, 1411, 1413, 1416, 1419], "arbitrary_el": [8, 1392, 1413], "nb": [8, 1331, 1334], "left": [8, 71, 115, 183, 311, 312, 322, 324, 325, 385, 559, 560, 584, 616, 688, 689, 739, 1106, 1134, 1136, 1146, 1179, 1206, 1280, 1355, 1358, 1404], "right": [8, 71, 110, 111, 115, 152, 206, 322, 385, 427, 428, 500, 559, 560, 584, 585, 587, 588, 615, 616, 688, 689, 739, 854, 935, 980, 1134, 1136, 1146, 1155, 1157, 1179, 1206, 1213, 1215, 1270, 1280], "littl": [8, 94, 298, 307], "mislead": 8, "That": [8, 97, 132, 165, 212, 221, 227, 295, 385, 436, 465, 525, 535, 555, 588, 657, 671, 672, 673, 674, 691, 704, 717, 791, 862, 907, 943, 988, 1046, 1162, 1210, 1296, 1388, 1404, 1409], "okai": 8, "becaus": [8, 11, 54, 69, 94, 99, 101, 102, 103, 112, 132, 161, 215, 216, 220, 255, 311, 378, 387, 389, 390, 394, 411, 412, 427, 494, 498, 499, 500, 510, 569, 585, 587, 615, 616, 632, 652, 934, 979, 1038, 1236, 1273, 1296, 1303, 1326, 1345, 1350, 1404, 1407, 1416], "AND": [8, 110, 598, 748, 762], "OR": [8, 110, 157, 175, 188, 856, 869, 877, 901, 937, 947, 950, 959, 982, 992], "symmetr": [8, 145, 148, 237, 545, 586, 593, 761, 1173, 1192, 1235, 1246, 1250, 1251, 1256, 1258, 1269, 1320, 1321, 1387], "It": [8, 52, 56, 58, 92, 93, 94, 97, 99, 101, 102, 104, 107, 110, 112, 115, 132, 172, 184, 207, 214, 215, 216, 229, 230, 231, 249, 260, 261, 262, 264, 278, 310, 316, 324, 325, 326, 343, 346, 347, 351, 353, 412, 414, 415, 416, 417, 418, 419, 429, 438, 440, 452, 457, 464, 480, 496, 500, 508, 530, 540, 545, 559, 560, 565, 566, 567, 582, 588, 594, 595, 598, 600, 601, 615, 619, 628, 629, 630, 652, 658, 659, 663, 671, 674, 692, 717, 718, 719, 760, 761, 762, 791, 796, 868, 873, 892, 913, 916, 928, 949, 955, 973, 994, 998, 1010, 1012, 1013, 1018, 1037, 1038, 1039, 1040, 1054, 1117, 1170, 1174, 1200, 1201, 1206, 1207, 1210, 1217, 1223, 1227, 1234, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1253, 1254, 1258, 1261, 1263, 1264, 1269, 1275, 1276, 1277, 1280, 1296, 1297, 1323, 1324, 1326, 1328, 1343, 1382, 1383, 1393, 1395, 1398, 1402, 1404, 1407, 1408, 1409, 1411, 1412, 1413, 1426], "just": [8, 99, 102, 103, 104, 184, 199, 338, 374, 439, 464, 559, 560, 577, 660, 661, 662, 692, 791, 873, 887, 916, 925, 946, 955, 960, 968, 991, 998, 1007, 1120, 1126, 1229, 1278, 1279, 1296, 1328, 1393, 1404, 1406], "operand": 8, "predict": [8, 567, 568, 569, 570, 571, 572, 573, 574, 591, 592, 758, 1325, 1402, 1406, 1412], "henc": [8, 168, 189, 521, 865, 878, 910, 946, 960, 991, 1059, 1202, 1383], "doe": [8, 77, 93, 94, 99, 101, 102, 103, 104, 114, 115, 132, 147, 153, 154, 165, 168, 189, 207, 208, 227, 228, 229, 230, 231, 232, 293, 308, 339, 340, 342, 343, 352, 357, 373, 382, 385, 410, 414, 426, 450, 469, 494, 495, 496, 497, 498, 499, 500, 502, 503, 506, 507, 509, 510, 511, 512, 534, 544, 549, 550, 551, 564, 566, 583, 584, 586, 589, 601, 612, 626, 627, 678, 691, 693, 694, 698, 699, 717, 718, 721, 722, 723, 724, 725, 726, 762, 862, 865, 878, 892, 907, 910, 928, 943, 946, 960, 973, 988, 991, 1010, 1038, 1043, 1066, 1070, 1072, 1081, 1102, 1103, 1105, 1106, 1107, 1109, 1114, 1173, 1175, 1177, 1192, 1207, 1222, 1223, 1227, 1229, 1234, 1241, 1296, 1300, 1303, 1326, 1333, 1334, 1341, 1342, 1344, 1351, 1353, 1354, 1355, 1356, 1357, 1358, 1371, 1379, 1380, 1381, 1383, 1393, 1404, 1405, 1406, 1410, 1417, 1426], "necessarili": [8, 99, 341, 451, 483, 559, 560, 641, 643, 1038, 1219], "behav": [8, 88, 103, 159, 190, 200, 220, 351, 858, 879, 888, 903, 939, 969, 984, 1229, 1296, 1395, 1404], "everi": [8, 11, 57, 88, 93, 109, 112, 120, 144, 157, 161, 177, 211, 212, 220, 221, 229, 230, 231, 235, 243, 264, 287, 295, 300, 324, 325, 343, 352, 380, 397, 437, 439, 440, 450, 462, 471, 472, 473, 474, 475, 477, 483, 484, 491, 512, 516, 565, 606, 614, 615, 619, 632, 633, 635, 636, 663, 685, 687, 688, 717, 718, 791, 856, 901, 937, 982, 1052, 1053, 1054, 1070, 1071, 1072, 1085, 1086, 1102, 1103, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1114, 1115, 1116, 1117, 1148, 1162, 1195, 1216, 1217, 1257, 1264, 1278, 1279, 1296, 1407], "left_subformula": 8, "right_subformula": 8, "in_degre": [8, 166, 188, 491, 678, 863, 877, 944, 959, 1177, 1207, 1208, 1404, 1406, 1407, 1426], "ha": [8, 11, 16, 44, 67, 88, 91, 93, 94, 95, 97, 99, 100, 101, 102, 103, 105, 107, 110, 112, 116, 120, 127, 152, 161, 165, 166, 173, 174, 175, 184, 188, 198, 207, 212, 214, 215, 219, 220, 226, 227, 229, 230, 231, 232, 235, 238, 239, 240, 241, 242, 243, 244, 247, 249, 252, 269, 271, 272, 273, 274, 275, 276, 282, 289, 291, 293, 294, 295, 300, 305, 310, 324, 331, 343, 352, 355, 356, 363, 364, 365, 373, 378, 380, 381, 383, 384, 385, 386, 391, 393, 394, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 424, 427, 428, 429, 439, 450, 458, 460, 466, 467, 468, 471, 472, 473, 474, 475, 476, 477, 480, 491, 492, 493, 494, 495, 496, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 522, 564, 566, 577, 578, 581, 590, 593, 605, 607, 610, 611, 622, 623, 624, 628, 629, 630, 632, 633, 634, 635, 636, 638, 646, 647, 649, 652, 657, 658, 682, 688, 690, 692, 697, 711, 717, 718, 729, 730, 731, 739, 749, 786, 791, 854, 862, 863, 869, 873, 877, 886, 892, 899, 907, 908, 916, 924, 928, 935, 943, 944, 948, 950, 955, 959, 967, 973, 980, 988, 989, 993, 998, 1006, 1010, 1040, 1043, 1045, 1066, 1068, 1070, 1072, 1075, 1080, 1084, 1098, 1099, 1101, 1102, 1103, 1105, 1122, 1130, 1145, 1154, 1160, 1162, 1165, 1176, 1180, 1185, 1193, 1195, 1196, 1197, 1198, 1199, 1207, 1210, 1211, 1215, 1217, 1222, 1234, 1239, 1243, 1244, 1248, 1249, 1254, 1259, 1261, 1264, 1267, 1269, 1270, 1272, 1275, 1276, 1277, 1278, 1279, 1281, 1282, 1283, 1284, 1285, 1286, 1289, 1291, 1293, 1296, 1300, 1326, 1328, 1330, 1333, 1334, 1353, 1354, 1371, 1372, 1379, 1382, 1393, 1394, 1395, 1398, 1403, 1404, 1405, 1406, 1407, 1409, 1413, 1414, 1416, 1423, 1425], "output": [8, 13, 16, 89, 93, 101, 102, 103, 109, 197, 287, 288, 345, 374, 380, 494, 498, 499, 509, 510, 575, 588, 677, 678, 691, 722, 1045, 1193, 1197, 1199, 1269, 1296, 1326, 1334, 1341, 1344, 1355, 1358, 1399, 1402, 1404, 1406, 1411, 1413, 1414, 1426], "two": [8, 11, 16, 27, 34, 38, 43, 54, 55, 57, 58, 65, 67, 71, 88, 93, 95, 99, 100, 102, 109, 112, 114, 115, 120, 132, 151, 171, 175, 184, 185, 188, 202, 207, 211, 212, 213, 214, 215, 216, 217, 220, 221, 226, 227, 230, 231, 232, 245, 249, 251, 252, 253, 257, 258, 260, 261, 262, 265, 269, 270, 271, 272, 273, 274, 275, 276, 282, 285, 286, 287, 289, 305, 311, 315, 316, 322, 326, 329, 330, 337, 341, 343, 345, 351, 352, 358, 359, 377, 380, 381, 383, 391, 411, 412, 419, 423, 428, 429, 430, 431, 442, 443, 444, 445, 447, 452, 453, 454, 457, 462, 471, 472, 473, 474, 475, 476, 480, 491, 494, 498, 499, 500, 502, 503, 506, 508, 509, 510, 511, 521, 545, 549, 550, 551, 555, 559, 560, 561, 562, 563, 564, 565, 566, 568, 569, 572, 574, 578, 584, 585, 586, 587, 588, 593, 598, 605, 607, 608, 610, 611, 615, 619, 626, 627, 629, 632, 633, 635, 636, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 671, 672, 673, 674, 675, 676, 680, 692, 694, 731, 732, 738, 739, 760, 761, 762, 780, 786, 791, 796, 853, 867, 869, 873, 874, 877, 890, 892, 898, 912, 916, 917, 926, 928, 934, 946, 948, 950, 955, 956, 959, 960, 971, 973, 979, 991, 993, 998, 999, 1008, 1010, 1019, 1020, 1021, 1022, 1036, 1037, 1039, 1040, 1056, 1084, 1088, 1098, 1100, 1101, 1106, 1107, 1108, 1109, 1114, 1116, 1134, 1146, 1147, 1149, 1151, 1152, 1156, 1174, 1185, 1186, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1204, 1207, 1210, 1211, 1215, 1217, 1218, 1243, 1244, 1253, 1271, 1272, 1275, 1276, 1294, 1295, 1296, 1323, 1324, 1326, 1328, 1359, 1360, 1363, 1393, 1394, 1395, 1397, 1402, 1404, 1405, 1406, 1407, 1410, 1411, 1413, 1425], "layer": [8, 36, 55, 61, 67, 103, 438, 705, 1038, 1109, 1420], "third": [8, 102, 114, 249, 422, 467, 585, 587, 734, 736, 1217, 1226, 1262, 1263, 1326, 1407], "appear": [8, 83, 93, 95, 99, 100, 102, 179, 204, 230, 231, 238, 243, 246, 247, 277, 363, 364, 365, 378, 451, 452, 453, 455, 466, 470, 584, 585, 587, 588, 675, 679, 707, 730, 734, 736, 891, 972, 1036, 1088, 1102, 1136, 1150, 1152, 1154, 1157, 1159, 1187, 1188, 1277, 1282, 1323, 1324, 1345, 1348, 1349, 1350, 1382, 1407, 1413, 1414], "both": [8, 52, 55, 92, 93, 94, 100, 101, 102, 103, 115, 161, 164, 204, 214, 215, 216, 217, 240, 257, 258, 259, 264, 282, 286, 287, 289, 337, 358, 379, 383, 415, 417, 418, 419, 423, 427, 440, 470, 502, 506, 545, 575, 581, 598, 600, 601, 602, 603, 604, 605, 606, 607, 610, 611, 615, 621, 635, 636, 653, 654, 655, 676, 711, 720, 760, 761, 762, 782, 891, 972, 1020, 1036, 1066, 1075, 1080, 1084, 1088, 1097, 1120, 1126, 1144, 1165, 1189, 1192, 1199, 1207, 1210, 1211, 1213, 1215, 1282, 1296, 1326, 1328, 1358, 1363, 1364, 1387, 1393, 1395, 1402, 1413, 1416, 1417, 1425, 1426], "negat": 8, "sole": [8, 786, 1278, 1279, 1326], "fourth": [8, 230, 231, 1326, 1404], "digraph": [8, 10, 11, 16, 21, 25, 41, 45, 56, 61, 67, 69, 70, 82, 88, 101, 102, 115, 132, 151, 152, 156, 157, 158, 160, 162, 163, 165, 166, 168, 170, 171, 172, 175, 176, 185, 186, 187, 188, 189, 192, 193, 194, 195, 196, 198, 199, 202, 204, 207, 208, 216, 227, 229, 230, 231, 240, 246, 247, 299, 308, 314, 318, 319, 321, 327, 328, 334, 335, 336, 337, 339, 340, 342, 343, 388, 391, 393, 396, 397, 398, 399, 401, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 430, 431, 437, 450, 452, 453, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 481, 482, 492, 494, 495, 496, 497, 498, 499, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 513, 514, 518, 519, 523, 555, 566, 575, 576, 577, 588, 590, 613, 615, 623, 630, 636, 643, 644, 652, 656, 657, 658, 659, 663, 678, 688, 690, 693, 696, 697, 698, 699, 700, 701, 702, 706, 707, 708, 709, 711, 716, 717, 718, 719, 721, 722, 723, 724, 725, 726, 740, 741, 744, 745, 746, 747, 748, 749, 750, 752, 760, 789, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 904, 905, 906, 907, 908, 911, 912, 913, 915, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 935, 936, 937, 938, 940, 941, 942, 943, 949, 957, 958, 963, 964, 965, 966, 967, 968, 972, 973, 974, 975, 977, 978, 980, 981, 982, 983, 985, 986, 987, 988, 989, 994, 996, 1000, 1001, 1003, 1004, 1005, 1006, 1007, 1010, 1035, 1037, 1038, 1039, 1040, 1041, 1052, 1062, 1066, 1070, 1072, 1075, 1080, 1083, 1084, 1098, 1099, 1101, 1118, 1135, 1150, 1154, 1168, 1169, 1170, 1173, 1177, 1178, 1180, 1182, 1183, 1184, 1185, 1189, 1217, 1270, 1272, 1273, 1274, 1283, 1284, 1287, 1290, 1292, 1298, 1323, 1326, 1333, 1337, 1342, 1356, 1357, 1362, 1365, 1366, 1371, 1393, 1399, 1401, 1402, 1404, 1405, 1406, 1407, 1408, 1409, 1411, 1412, 1413, 1414, 1416, 1417, 1424, 1425, 1426], "add_nod": [8, 11, 26, 34, 69, 74, 89, 102, 157, 184, 246, 339, 340, 398, 422, 491, 492, 496, 504, 505, 508, 522, 523, 605, 607, 610, 611, 691, 796, 856, 873, 901, 916, 937, 955, 982, 998, 1037, 1039, 1040, 1086, 1275, 1326, 1345, 1407, 1408, 1417, 1426], "get_node_attribut": [8, 39, 44, 71, 1213, 1404], "600": [8, 10, 12], "font_siz": [8, 16, 21, 25, 32, 35, 38, 45, 46, 1133, 1134, 1136], "22": [8, 35, 64, 66, 383, 384, 1271, 1323, 1403, 1408, 1412, 1422], "multipartite_layout": [8, 36, 61, 67, 1412, 1414, 1420], "subset_kei": [8, 36, 61, 67, 1109], "equal": [8, 36, 81, 144, 214, 215, 216, 230, 231, 238, 269, 271, 273, 276, 288, 297, 298, 300, 303, 306, 307, 310, 311, 312, 315, 316, 320, 323, 324, 325, 329, 330, 331, 373, 410, 411, 412, 413, 418, 419, 428, 471, 474, 476, 491, 494, 495, 496, 498, 499, 502, 503, 504, 505, 506, 507, 508, 509, 510, 525, 535, 545, 552, 553, 554, 555, 568, 572, 605, 623, 657, 671, 672, 673, 674, 687, 688, 689, 690, 721, 722, 740, 741, 753, 761, 791, 1112, 1116, 1162, 1165, 1198, 1204, 1230, 1239, 1271, 1280, 1291, 1307, 1309, 1312, 1398, 1399], "107": [8, 17, 241, 244, 1201], "plot_circuit": [8, 17], "southern": [9, 1265], "women": [9, 1265, 1398, 1406], "unipartit": [9, 115, 258, 259, 358], "properti": [9, 11, 18, 22, 33, 63, 86, 101, 102, 103, 112, 134, 159, 161, 166, 168, 175, 176, 179, 184, 188, 189, 190, 200, 284, 285, 286, 287, 288, 363, 364, 365, 388, 476, 500, 545, 569, 619, 685, 858, 863, 865, 869, 870, 873, 877, 878, 879, 888, 903, 908, 910, 916, 939, 944, 946, 950, 951, 955, 959, 960, 969, 984, 989, 991, 998, 1085, 1086, 1122, 1134, 1136, 1193, 1202, 1217, 1219, 1269, 1283, 1284, 1326, 1328, 1383, 1398, 1405, 1406, 1407, 1408, 1413, 1417, 1426], "These": [9, 52, 58, 73, 79, 86, 93, 94, 105, 112, 336, 385, 494, 512, 559, 671, 673, 732, 748, 779, 786, 1038, 1045, 1047, 1323, 1326, 1385, 1387, 1392, 1394, 1395, 1397, 1399, 1404, 1405, 1411, 1426], "were": [9, 65, 88, 99, 101, 104, 215, 216, 220, 289, 305, 410, 437, 460, 588, 962, 1002, 1199, 1393, 1395, 1399, 1402, 1405, 1406, 1407, 1413, 1416], "et": [9, 210, 226, 227, 315, 316, 322, 330, 334, 337, 345, 352, 358, 373, 380, 381, 423, 425, 426, 451, 569, 591, 592, 681, 682, 684, 693, 1202], "al": [9, 210, 226, 227, 315, 316, 322, 330, 334, 337, 345, 352, 358, 373, 380, 381, 423, 425, 426, 451, 569, 591, 592, 681, 682, 684, 693, 1202, 1407, 1413], "1930": [9, 1396], "thei": [9, 54, 58, 65, 71, 92, 93, 94, 97, 99, 100, 101, 102, 103, 104, 105, 107, 112, 132, 151, 165, 207, 213, 220, 249, 285, 287, 288, 296, 297, 298, 301, 302, 306, 307, 308, 309, 351, 362, 374, 391, 396, 427, 451, 452, 453, 454, 464, 465, 471, 472, 473, 474, 475, 496, 504, 505, 508, 512, 546, 547, 548, 559, 560, 576, 583, 586, 588, 600, 604, 675, 676, 704, 717, 750, 760, 786, 853, 862, 892, 898, 907, 928, 934, 943, 962, 973, 979, 988, 1002, 1010, 1036, 1038, 1066, 1085, 1088, 1109, 1120, 1126, 1133, 1135, 1137, 1151, 1159, 1165, 1193, 1197, 1198, 1217, 1271, 1272, 1323, 1328, 1353, 1354, 1356, 1357, 1359, 1363, 1394, 1396, 1402, 1404, 1406, 1409, 1414, 1426], "repres": [9, 11, 26, 43, 52, 54, 57, 67, 92, 99, 107, 115, 230, 231, 265, 281, 283, 286, 287, 288, 291, 292, 338, 350, 361, 362, 363, 377, 378, 380, 381, 382, 385, 386, 391, 448, 452, 453, 455, 457, 460, 465, 466, 494, 495, 498, 499, 500, 502, 503, 506, 507, 509, 510, 521, 565, 577, 578, 579, 580, 586, 588, 609, 615, 618, 619, 656, 660, 664, 667, 676, 679, 691, 692, 695, 697, 698, 699, 700, 702, 728, 730, 731, 734, 736, 739, 752, 786, 791, 796, 1019, 1020, 1021, 1022, 1037, 1038, 1039, 1040, 1045, 1081, 1102, 1140, 1151, 1185, 1193, 1194, 1196, 1197, 1198, 1199, 1209, 1217, 1240, 1243, 1246, 1250, 1258, 1267, 1269, 1272, 1273, 1278, 1279, 1323, 1324, 1326, 1329, 1330, 1346, 1347, 1388, 1393, 1406], "observ": [9, 13, 132, 223, 1414, 1426], "attend": 9, "14": [9, 11, 16, 19, 25, 38, 44, 59, 64, 66, 71, 229, 230, 231, 383, 384, 405, 406, 501, 619, 690, 1150, 1242, 1250, 1262, 1406, 1408, 1426], "event": [9, 25, 99, 100, 110, 1165, 1229, 1300], "18": [9, 44, 64, 66, 93, 324, 325, 345, 383, 384, 618, 1169, 1249, 1255, 1258, 1260, 1263, 1269, 1393, 1406, 1416, 1417, 1421, 1426], "bipartit": [9, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 350, 351, 358, 377, 439, 440, 443, 581, 588, 758, 1043, 1106, 1151, 1203, 1204, 1205, 1265, 1325, 1395, 1398, 1399, 1400, 1401, 1406, 1407, 1411, 1413, 1417, 1421, 1425], "biadjac": [9, 282, 283, 1400, 1406], "7": [9, 12, 14, 19, 25, 35, 44, 46, 63, 64, 65, 66, 68, 89, 99, 101, 102, 115, 125, 151, 158, 170, 171, 192, 207, 232, 268, 297, 299, 314, 322, 327, 332, 333, 339, 340, 342, 362, 374, 380, 391, 403, 410, 413, 414, 415, 423, 424, 425, 426, 441, 445, 446, 483, 496, 501, 508, 511, 512, 555, 581, 586, 618, 619, 630, 652, 658, 663, 671, 674, 680, 695, 703, 706, 707, 708, 730, 747, 750, 761, 796, 853, 857, 866, 867, 881, 892, 898, 902, 911, 912, 915, 920, 928, 934, 938, 947, 973, 979, 983, 992, 996, 1010, 1037, 1039, 1040, 1052, 1053, 1085, 1100, 1104, 1148, 1212, 1242, 1248, 1250, 1251, 1255, 1258, 1260, 1273, 1323, 1326, 1330, 1339, 1340, 1345, 1348, 1349, 1350, 1382, 1392, 1394, 1402, 1403, 1405, 1408, 1409, 1410, 1411, 1412, 1413, 1426], "12": [9, 11, 19, 25, 44, 50, 55, 58, 64, 65, 66, 89, 91, 93, 229, 230, 231, 265, 345, 380, 381, 392, 399, 405, 406, 407, 449, 486, 501, 516, 568, 572, 574, 606, 616, 1052, 1053, 1054, 1133, 1136, 1150, 1244, 1245, 1249, 1254, 1257, 1263, 1335, 1406, 1408, 1412, 1426], "9": [9, 11, 12, 19, 25, 35, 44, 46, 63, 64, 65, 66, 68, 82, 89, 101, 102, 111, 115, 125, 232, 293, 295, 339, 340, 342, 346, 347, 356, 374, 380, 405, 406, 424, 438, 449, 494, 496, 501, 504, 505, 508, 545, 566, 581, 586, 676, 706, 707, 708, 761, 1100, 1104, 1148, 1150, 1194, 1199, 1212, 1217, 1235, 1246, 1255, 1267, 1273, 1283, 1284, 1323, 1326, 1328, 1396, 1403, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426], "11": [9, 25, 33, 44, 64, 65, 66, 68, 89, 102, 110, 115, 157, 210, 239, 240, 297, 298, 303, 306, 307, 323, 392, 399, 405, 406, 407, 413, 415, 417, 422, 501, 514, 517, 606, 618, 680, 721, 738, 856, 901, 937, 982, 1052, 1053, 1054, 1100, 1150, 1287, 1403, 1410, 1413, 1414, 1419, 1424, 1425, 1426], "13": [9, 11, 38, 44, 64, 66, 89, 91, 156, 229, 230, 231, 343, 501, 703, 855, 900, 936, 981, 1150, 1192, 1406, 1420, 1426], "16": [9, 19, 31, 44, 45, 64, 66, 70, 229, 230, 231, 346, 347, 387, 389, 390, 394, 453, 508, 511, 512, 519, 571, 592, 606, 748, 749, 750, 1109, 1205, 1256, 1271, 1286, 1323, 1406, 1411, 1426], "17": [9, 21, 44, 64, 66, 103, 229, 230, 231, 297, 508, 680, 693, 1405, 1406, 1426], "friend": [9, 545, 1407, 1412], "member": [9, 92, 93, 94, 100, 112, 315, 317, 318, 319, 330, 391, 483, 484, 586, 691, 1222, 1267, 1403], "evelyn": 9, "jefferson": 9, "laura": 9, "mandevil": 9, "theresa": 9, "anderson": 9, "brenda": 9, "roger": 9, "charlott": 9, "mcdowd": 9, "franc": 9, "eleanor": 9, "nye": 9, "pearl": [9, 132], "oglethorp": 9, "ruth": 9, "desand": 9, "vern": 9, "sanderson": 9, "myra": 9, "liddel": 9, "katherina": 9, "sylvia": 9, "avondal": 9, "nora": 9, "fayett": 9, "helen": 9, "lloyd": 9, "dorothi": 9, "murchison": 9, "olivia": 9, "carleton": 9, "flora": 9, "price": 9, "meet": [9, 94, 1165, 1196, 1197, 1198], "50": [9, 25, 30, 34, 40, 50, 54, 55, 56, 57, 64, 65, 272, 312, 1117, 1193, 1197, 1198, 1251, 1297, 1302], "45": [9, 58, 64, 110, 226, 300, 409, 1175], "57": [9, 64], "46": [9, 64, 235, 564, 619, 1264], "24": [9, 19, 37, 64, 66, 68, 103, 383, 384, 496, 505, 508, 703, 1212, 1229, 1244, 1262, 1271, 1403], 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288, 559, 560], "projected_graph": [9, 115, 284, 285, 286, 288, 351], "keep": [9, 92, 93, 94, 115, 204, 345, 346, 347, 362, 377, 387, 389, 390, 394, 583, 598, 693, 694, 891, 972, 1117, 1207, 1210, 1278, 1279, 1296, 1376, 1394, 1411, 1414], "co": [9, 26, 94, 99, 144, 752, 1326], "occur": [9, 93, 95, 100, 230, 231, 277, 278, 280, 383, 581, 582, 583, 588, 1043, 1117, 1120, 1126, 1282, 1296], "count": [9, 185, 237, 238, 242, 243, 245, 297, 298, 310, 315, 330, 360, 386, 443, 568, 597, 619, 749, 753, 874, 917, 944, 950, 956, 959, 999, 1060, 1179, 1278, 1279, 1406, 1407, 1416], "share": [9, 54, 58, 92, 94, 112, 165, 199, 214, 215, 216, 221, 278, 285, 287, 288, 294, 358, 359, 376, 418, 419, 460, 462, 480, 569, 578, 691, 732, 862, 887, 907, 925, 943, 968, 988, 1007, 1217, 1328], "contact": [9, 92, 688, 1195, 1326], "weighted_projected_graph": [9, 284, 285, 286, 287, 1417], "648": 9, "077": [9, 17, 36, 47], "plot_davis_club": [9, 17], "retain": [10, 102, 110, 230, 284, 285, 286, 287, 288, 1100, 1187, 1295], "pattern": [10, 54, 93, 103, 236, 241, 244, 248, 385, 494, 519, 555, 671, 672, 673, 674, 690, 691, 693, 762, 786, 1036, 1088, 1388, 1413], "add": [10, 11, 26, 34, 41, 45, 49, 52, 61, 71, 88, 89, 91, 93, 94, 101, 102, 105, 106, 115, 151, 152, 153, 154, 156, 157, 158, 164, 207, 222, 223, 229, 282, 285, 341, 374, 411, 412, 423, 428, 430, 431, 450, 460, 581, 582, 583, 589, 614, 615, 618, 619, 654, 690, 701, 717, 718, 796, 850, 853, 854, 855, 856, 857, 892, 895, 898, 899, 900, 901, 902, 928, 931, 934, 935, 936, 937, 938, 973, 976, 979, 980, 981, 982, 983, 984, 1010, 1037, 1038, 1039, 1040, 1042, 1049, 1052, 1053, 1054, 1100, 1154, 1165, 1172, 1185, 1207, 1210, 1217, 1219, 1233, 1234, 1236, 1302, 1326, 1353, 1354, 1356, 1357, 1379, 1380, 1383, 1393, 1394, 1395, 1398, 1404, 1406, 1407, 1408, 1409, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426], "compressor": [10, 690, 786], "do": [10, 55, 75, 88, 92, 93, 94, 96, 99, 101, 102, 106, 107, 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69, 93, 598, 1133, 1134, 1136, 1412, 1413, 1414, 1416, 1426], "ax1": [10, 15, 27, 50, 82], "number_of_edg": [10, 15, 25, 28, 198, 690, 886, 924, 967, 1006, 1059, 1154, 1271, 1406, 1407, 1426], "nonexp_graph": 10, "compression_nod": 10, "summar": [10, 15, 100, 101, 690, 691, 758, 791, 1325, 1328, 1413], "dedensifi": [10, 758], "threshold": [10, 57, 83, 112, 220, 229, 231, 380, 381, 690, 692, 695, 696, 758, 786, 1117, 1193, 1194, 1196, 1197, 1198, 1325, 1398, 1406, 1407, 1408, 1412, 1414], "copi": [10, 16, 38, 44, 93, 95, 106, 167, 196, 199, 202, 203, 204, 205, 284, 285, 286, 287, 288, 341, 388, 390, 392, 406, 433, 434, 435, 436, 437, 453, 460, 469, 521, 584, 585, 587, 596, 599, 602, 603, 605, 606, 607, 610, 611, 613, 614, 633, 636, 690, 864, 885, 887, 890, 891, 909, 925, 926, 927, 945, 963, 966, 968, 971, 972, 990, 1003, 1007, 1008, 1009, 1035, 1038, 1057, 1061, 1063, 1066, 1082, 1083, 1122, 1183, 1189, 1217, 1223, 1227, 1251, 1270, 1294, 1295, 1296, 1403, 1404, 1406, 1407, 1408, 1409, 1412, 1413, 1422, 1425], "nonexp_node_color": 10, "nonexp_node_s": 10, "yellow": [10, 15, 598, 760, 1426], "nonexp_po": 10, "75": [10, 34, 239, 260, 299, 314, 355, 356, 386, 682, 1169, 1170, 1171, 1173, 1404, 1408, 1426], "c_node": [10, 690], "spot": 10, "255": [10, 17, 21], "plot_dedensif": [10, 17], "153": [11, 455], "curiou": 11, "let": [11, 55, 58, 93, 97, 101, 103, 217, 257, 280, 282, 299, 300, 313, 322, 371, 372, 383, 586, 619, 762, 1219, 1278, 1279, 1326, 1425], "defin": [11, 24, 52, 58, 69, 97, 112, 127, 213, 222, 223, 239, 240, 260, 261, 262, 263, 285, 289, 311, 316, 329, 334, 335, 345, 346, 347, 356, 385, 386, 390, 424, 425, 426, 429, 432, 433, 434, 435, 436, 437, 449, 464, 465, 466, 469, 494, 495, 498, 499, 500, 502, 503, 506, 507, 509, 510, 519, 567, 569, 570, 571, 573, 574, 575, 577, 586, 614, 615, 619, 621, 625, 652, 671, 673, 674, 676, 684, 685, 686, 687, 688, 689, 728, 730, 738, 751, 752, 753, 762, 791, 796, 1037, 1038, 1039, 1040, 1045, 1047, 1071, 1081, 1098, 1147, 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464, 465, 466, 592, 624, 691, 760, 786, 871, 914, 952, 995, 1045, 1100, 1175, 1177, 1180, 1216, 1219, 1222, 1225, 1245, 1280, 1290, 1295, 1296, 1299, 1301, 1383, 1395, 1407, 1408, 1412, 1413, 1414, 1419, 1426], "follow": [11, 25, 44, 49, 52, 53, 65, 67, 83, 86, 91, 92, 93, 94, 95, 97, 99, 100, 101, 102, 103, 108, 110, 111, 128, 132, 151, 161, 171, 183, 207, 213, 227, 229, 230, 231, 243, 280, 305, 338, 343, 351, 362, 373, 378, 380, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 440, 452, 453, 465, 466, 496, 502, 503, 504, 505, 506, 507, 508, 588, 598, 599, 602, 615, 636, 679, 748, 750, 760, 762, 791, 853, 867, 892, 898, 912, 928, 934, 948, 973, 979, 993, 1010, 1102, 1103, 1105, 1144, 1165, 1175, 1179, 1185, 1188, 1200, 1201, 1209, 1219, 1225, 1233, 1234, 1241, 1251, 1260, 1274, 1275, 1276, 1277, 1281, 1296, 1315, 1323, 1326, 1328, 1329, 1388, 1393, 1395, 1399, 1404, 1406, 1407, 1409, 1411, 1412, 1413, 1425, 1426], "given": [11, 38, 44, 62, 64, 67, 91, 99, 101, 103, 112, 116, 141, 142, 144, 152, 158, 193, 197, 208, 211, 212, 227, 229, 235, 236, 248, 249, 260, 264, 266, 269, 271, 273, 274, 276, 279, 281, 283, 284, 285, 286, 287, 288, 320, 329, 331, 338, 344, 351, 353, 357, 362, 363, 364, 365, 373, 378, 380, 381, 385, 439, 454, 455, 460, 462, 470, 477, 478, 480, 497, 511, 512, 513, 559, 560, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 576, 578, 579, 580, 588, 589, 590, 614, 615, 616, 622, 623, 659, 660, 661, 662, 676, 677, 678, 679, 681, 683, 684, 686, 690, 691, 693, 697, 698, 699, 700, 702, 703, 704, 706, 707, 708, 709, 728, 729, 730, 731, 732, 739, 748, 753, 761, 782, 786, 854, 857, 882, 899, 902, 921, 935, 938, 963, 980, 983, 1003, 1046, 1085, 1086, 1094, 1101, 1102, 1135, 1144, 1151, 1162, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1189, 1199, 1200, 1201, 1206, 1207, 1208, 1209, 1210, 1221, 1222, 1240, 1269, 1273, 1274, 1276, 1295, 1300, 1302, 1315, 1323, 1353, 1354, 1379, 1380, 1394, 1395, 1406], "digit": [11, 70, 99], "base": [11, 15, 38, 43, 55, 58, 69, 93, 94, 100, 101, 102, 103, 107, 128, 132, 199, 203, 205, 212, 216, 220, 229, 296, 297, 301, 302, 303, 308, 309, 310, 311, 312, 322, 323, 324, 325, 329, 330, 337, 343, 346, 347, 362, 371, 373, 374, 380, 381, 382, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 423, 425, 426, 427, 428, 430, 431, 449, 464, 466, 494, 498, 499, 500, 509, 510, 545, 555, 564, 566, 569, 574, 581, 614, 616, 660, 667, 680, 688, 691, 704, 706, 707, 708, 710, 711, 712, 713, 714, 715, 717, 732, 738, 758, 761, 762, 786, 791, 796, 887, 925, 934, 935, 968, 979, 980, 1007, 1036, 1037, 1038, 1041, 1043, 1082, 1088, 1182, 1229, 1235, 1253, 1267, 1296, 1320, 1321, 1323, 1326, 1383, 1387, 1392, 1395, 1402, 1403, 1404, 1406, 1407, 1408, 1409, 1411, 1412, 1421, 1425], "obtain": [11, 91, 165, 207, 282, 345, 346, 347, 380, 383, 387, 388, 389, 390, 394, 465, 511, 606, 618, 619, 656, 722, 742, 743, 760, 796, 862, 892, 907, 928, 943, 973, 988, 1010, 1037, 1039, 1040, 1164, 1253, 1272, 1278, 1279, 1323, 1326, 1356, 1357, 1402, 1426], "seri": [11, 444, 616, 680, 1215, 1286], "finit": [11, 462, 494, 495, 498, 499, 502, 503, 506, 507, 509, 510, 514, 518, 1177, 1179, 1192, 1222], "end": [11, 25, 36, 52, 95, 101, 106, 153, 154, 206, 215, 227, 267, 268, 300, 332, 333, 342, 371, 372, 427, 614, 618, 619, 626, 627, 631, 632, 634, 635, 636, 639, 640, 650, 651, 652, 653, 654, 655, 660, 664, 667, 677, 678, 680, 734, 736, 1038, 1061, 1066, 1075, 1080, 1082, 1084, 1117, 1133, 1135, 1152, 1165, 1206, 1229, 1326, 1333, 1334, 1337, 1338, 1339, 1340, 1342, 1344, 1350, 1353, 1357, 1358, 1368, 1371, 1372, 1375, 1376, 1379, 1404, 1413], "In": [11, 16, 27, 43, 54, 57, 58, 88, 92, 93, 94, 95, 97, 99, 100, 101, 103, 110, 115, 127, 132, 133, 175, 184, 199, 217, 229, 230, 231, 235, 240, 257, 258, 259, 278, 283, 286, 288, 289, 299, 311, 312, 324, 325, 329, 350, 357, 378, 379, 380, 410, 413, 414, 415, 422, 429, 443, 447, 450, 458, 460, 494, 498, 499, 501, 510, 565, 568, 572, 574, 590, 591, 615, 619, 621, 652, 653, 654, 657, 658, 663, 670, 675, 676, 690, 691, 701, 703, 717, 718, 719, 730, 732, 740, 741, 742, 743, 761, 762, 767, 770, 789, 791, 796, 869, 873, 887, 916, 925, 954, 955, 968, 997, 998, 1007, 1037, 1038, 1039, 1040, 1042, 1043, 1066, 1100, 1101, 1117, 1154, 1168, 1199, 1203, 1206, 1207, 1208, 1210, 1216, 1217, 1222, 1226, 1231, 1233, 1241, 1295, 1296, 1300, 1320, 1321, 1326, 1328, 1350, 1394, 1398, 1399, 1404, 1405, 1406, 1407, 1408, 1409, 1413, 1414, 1426], "languag": [11, 92, 99, 110, 1042, 1324, 1341, 1342, 1344, 1381, 1382, 1383, 1411], "discret": [11, 104, 235, 249, 362, 409, 513, 517, 518, 618, 1164, 1165, 1178, 1180, 1186, 1190, 1204, 1278, 1279, 1282, 1314, 1315, 1323, 1406], "global": [11, 103, 314, 341, 410, 477, 486, 487, 509, 592, 1045, 1269, 1296, 1301, 1304, 1305, 1328, 1407, 1409, 1411], "attractor": [11, 388], "map": [11, 34, 38, 52, 67, 101, 102, 103, 115, 125, 144, 145, 148, 166, 169, 197, 238, 243, 264, 350, 369, 391, 412, 416, 417, 418, 419, 423, 424, 425, 426, 431, 440, 460, 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1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426], "108": [11, 39, 47, 1216], "513": [11, 1398, 1406], "reach": [11, 99, 100, 314, 324, 327, 376, 383, 387, 389, 390, 394, 410, 411, 412, 418, 419, 494, 498, 499, 510, 564, 566, 626, 627, 632, 640, 643, 652, 693, 711, 758, 1188, 1207, 1210, 1407], "orbit": 11, "up": [11, 70, 80, 93, 94, 97, 99, 100, 101, 104, 107, 132, 133, 346, 347, 377, 423, 427, 509, 530, 540, 577, 619, 652, 653, 657, 748, 1036, 1038, 1061, 1066, 1082, 1088, 1102, 1144, 1148, 1173, 1213, 1215, 1272, 1326, 1328, 1355, 1358, 1395, 1396, 1402, 1404, 1406, 1410, 1411, 1413, 1414, 1416, 1417, 1420, 1426], "reveal": [11, 711, 786], "maximum": [11, 112, 115, 209, 210, 211, 212, 214, 215, 217, 222, 224, 227, 257, 259, 264, 277, 278, 279, 281, 288, 296, 304, 311, 312, 315, 316, 317, 318, 319, 321, 324, 328, 330, 339, 341, 342, 343, 346, 347, 352, 356, 361, 373, 377, 380, 382, 383, 385, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 428, 440, 472, 473, 494, 498, 499, 500, 501, 502, 503, 506, 507, 509, 510, 520, 521, 564, 566, 581, 583, 589, 591, 592, 670, 671, 672, 673, 674, 676, 691, 693, 694, 704, 706, 707, 708, 710, 711, 712, 713, 714, 715, 716, 720, 723, 724, 732, 734, 735, 736, 737, 740, 741, 749, 758, 768, 791, 1117, 1133, 1135, 1137, 1165, 1181, 1198, 1199, 1200, 1201, 1208, 1225, 1237, 1238, 1302, 1323, 1395, 1402, 1406, 1407, 1412, 1413], "cycl": [11, 38, 44, 95, 120, 214, 227, 228, 229, 230, 231, 232, 263, 293, 294, 295, 338, 341, 343, 358, 449, 450, 451, 452, 453, 457, 462, 463, 464, 466, 467, 468, 480, 496, 501, 504, 505, 508, 519, 584, 585, 587, 608, 628, 629, 630, 632, 652, 657, 658, 663, 697, 727, 742, 743, 758, 791, 1043, 1052, 1135, 1137, 1148, 1149, 1152, 1163, 1186, 1190, 1242, 1244, 1260, 1264, 1325, 1395, 1397, 1398, 1401, 1403, 1404, 1406, 1407, 1408, 1411, 1412, 1414, 1424, 1425], "requir": [11, 38, 65, 93, 94, 95, 99, 100, 101, 102, 104, 106, 107, 109, 111, 115, 165, 207, 291, 292, 293, 296, 301, 302, 308, 309, 316, 437, 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218, 221, 226, 233, 236, 241, 244, 248, 249, 267, 275, 278, 280, 282, 284, 288, 289, 290, 293, 295, 300, 301, 302, 305, 306, 307, 308, 309, 311, 312, 313, 322, 324, 325, 326, 331, 332, 333, 339, 340, 341, 343, 345, 355, 356, 358, 361, 371, 372, 374, 378, 383, 385, 398, 405, 406, 429, 434, 449, 452, 453, 455, 467, 468, 469, 471, 472, 474, 475, 476, 479, 488, 490, 491, 492, 494, 496, 498, 499, 502, 503, 504, 505, 506, 507, 508, 509, 510, 517, 518, 565, 566, 575, 577, 582, 586, 588, 590, 593, 598, 602, 615, 616, 618, 619, 625, 626, 675, 677, 678, 686, 688, 691, 692, 693, 732, 734, 736, 762, 796, 850, 853, 854, 856, 857, 865, 866, 867, 878, 881, 884, 892, 895, 898, 899, 901, 902, 910, 911, 912, 920, 923, 928, 931, 934, 935, 937, 938, 946, 947, 948, 960, 962, 965, 973, 976, 979, 980, 982, 983, 984, 991, 992, 993, 1002, 1005, 1010, 1037, 1038, 1039, 1040, 1047, 1097, 1100, 1104, 1133, 1134, 1135, 1136, 1146, 1154, 1165, 1175, 1177, 1179, 1180, 1182, 1183, 1184, 1187, 1192, 1193, 1194, 1203, 1204, 1205, 1207, 1210, 1219, 1222, 1226, 1230, 1233, 1234, 1260, 1266, 1268, 1278, 1279, 1280, 1287, 1288, 1292, 1295, 1302, 1303, 1310, 1320, 1321, 1323, 1326, 1329, 1333, 1337, 1338, 1341, 1344, 1356, 1388, 1393, 1396, 1402, 1403, 1405, 1406, 1407, 1409, 1411, 1413, 1414, 1417], "687": [11, 59], "1071": 11, "345": 11, "216": [11, 1193], "225": [11, 89, 207, 278, 892, 928, 973, 1010, 1155], "141": [11, 226], "66": [11, 34, 58, 64, 566], "432": 11, "99": [11, 65, 592, 1201, 1233, 1323, 1403], "1458": 11, "702": 11, "351": 11, "test": [11, 52, 88, 94, 95, 96, 97, 99, 103, 106, 109, 132, 180, 267, 268, 310, 338, 343, 397, 398, 420, 421, 454, 520, 525, 535, 555, 616, 671, 740, 741, 742, 743, 755, 757, 760, 762, 871, 914, 952, 995, 1042, 1070, 1072, 1165, 1326, 1333, 1334, 1337, 1339, 1340, 1344, 1349, 1350, 1371, 1372, 1375, 1376, 1393, 1395, 1396, 1398, 1401, 1405, 1406, 1407, 1408, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1423, 1424, 1425, 1426], "softwar": [11, 91, 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1217], "confus": [15, 101, 102, 165, 691, 862, 907, 943, 988, 1196, 1197, 1198, 1398, 1406, 1412], "stanford": [15, 34, 65, 69, 71, 566, 691, 1268], "analysi": [15, 23, 47, 50, 52, 55, 86, 100, 101, 103, 105, 107, 110, 228, 232, 257, 258, 259, 260, 261, 262, 286, 288, 289, 299, 305, 379, 383, 412, 431, 437, 462, 494, 500, 619, 691, 751, 758, 760, 762, 1042, 1201, 1233, 1325, 1405, 1409, 1410, 1412, 1414], "uniqu": [15, 27, 238, 255, 278, 311, 312, 378, 460, 464, 469, 559, 560, 565, 585, 587, 600, 604, 618, 619, 641, 643, 691, 732, 748, 934, 979, 1047, 1244, 1250, 1251, 1296, 1326, 1343, 1359, 1360, 1363, 1364, 1426], "combin": [15, 61, 102, 204, 207, 379, 380, 385, 411, 412, 416, 418, 423, 575, 598, 600, 604, 678, 691, 891, 892, 928, 973, 1010, 1387, 1408], "type": [15, 70, 93, 95, 97, 100, 101, 102, 103, 104, 110, 165, 208, 241, 242, 243, 244, 247, 266, 267, 269, 270, 271, 273, 274, 276, 282, 283, 296, 301, 302, 303, 308, 309, 315, 323, 350, 351, 429, 496, 549, 550, 551, 555, 584, 585, 587, 588, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 652, 658, 671, 672, 673, 674, 690, 691, 693, 695, 711, 722, 748, 749, 750, 786, 862, 907, 943, 988, 1041, 1043, 1047, 1087, 1091, 1092, 1093, 1094, 1097, 1098, 1099, 1100, 1101, 1102, 1104, 1105, 1110, 1118, 1145, 1146, 1147, 1148, 1150, 1152, 1154, 1155, 1157, 1159, 1160, 1163, 1175, 1177, 1178, 1180, 1182, 1183, 1184, 1190, 1191, 1192, 1200, 1201, 1202, 1211, 1213, 1215, 1217, 1222, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1252, 1253, 1254, 1255, 1256, 1257, 1259, 1260, 1261, 1262, 1263, 1264, 1273, 1278, 1279, 1281, 1298, 1325, 1326, 1332, 1333, 1336, 1337, 1338, 1342, 1345, 1348, 1349, 1350, 1356, 1357, 1358, 1370, 1371, 1382, 1386, 1390, 1393, 1395, 1404, 1406, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1417, 1425, 1426], "other": [15, 16, 24, 41, 43, 50, 52, 56, 57, 58, 83, 88, 91, 92, 93, 94, 97, 99, 100, 101, 102, 103, 104, 105, 107, 109, 110, 115, 132, 134, 165, 208, 214, 215, 216, 226, 230, 231, 232, 235, 256, 258, 264, 267, 268, 282, 288, 289, 294, 297, 298, 305, 316, 320, 322, 324, 325, 327, 352, 358, 366, 373, 396, 397, 428, 452, 453, 460, 462, 473, 491, 502, 503, 506, 507, 527, 537, 559, 560, 565, 588, 602, 632, 633, 635, 636, 641, 653, 660, 661, 662, 665, 666, 667, 668, 669, 675, 676, 688, 691, 701, 723, 724, 725, 726, 734, 735, 736, 737, 751, 752, 762, 789, 791, 796, 862, 907, 943, 948, 988, 993, 1037, 1038, 1039, 1040, 1042, 1054, 1102, 1103, 1114, 1116, 1133, 1145, 1147, 1151, 1154, 1165, 1174, 1180, 1186, 1194, 1195, 1197, 1198, 1222, 1229, 1269, 1278, 1279, 1281, 1286, 1289, 1291, 1293, 1296, 1302, 1324, 1325, 1326, 1328, 1337, 1338, 1339, 1345, 1348, 1349, 1350, 1382, 1383, 1394, 1396, 1398, 1403, 1404, 1405, 1406, 1407, 1408, 1410, 1411, 1412, 1413, 1414, 1417, 1425, 1426], "produc": [15, 44, 49, 103, 115, 226, 246, 247, 272, 280, 297, 298, 306, 307, 315, 316, 329, 330, 422, 460, 565, 601, 612, 629, 632, 633, 635, 636, 677, 678, 680, 691, 786, 1097, 1102, 1103, 1105, 1165, 1179, 1181, 1189, 1212, 1236, 1280, 1281, 1282, 1283, 1284, 1285, 1286, 1287, 1288, 1392, 1399, 1406, 1408, 1416, 1417], "infer": [15, 695, 1104, 1118, 1358, 1412], "differ": [15, 25, 27, 28, 33, 41, 53, 54, 57, 63, 71, 86, 92, 93, 94, 95, 99, 103, 112, 161, 164, 165, 204, 207, 215, 216, 223, 280, 282, 297, 298, 314, 315, 326, 330, 334, 335, 337, 341, 358, 361, 371, 372, 373, 374, 378, 410, 413, 414, 415, 435, 437, 509, 511, 512, 593, 602, 615, 704, 717, 718, 738, 750, 758, 772, 786, 862, 891, 892, 907, 928, 943, 972, 973, 988, 1010, 1102, 1105, 1133, 1165, 1169, 1170, 1171, 1193, 1198, 1207, 1255, 1269, 1287, 1296, 1326, 1365, 1366, 1382, 1394, 1404, 1405, 1406, 1413, 1414, 1425, 1426], "relat": [15, 34, 67, 92, 93, 95, 99, 100, 115, 129, 132, 220, 230, 297, 366, 370, 586, 588, 619, 688, 762, 767, 795, 1202, 1205, 1269, 1323, 1395, 1402, 1406, 1413, 1416, 1425], "strong": [15, 397, 511, 512, 517, 610, 619, 691, 699, 758, 1408], "weak": [15, 398, 691, 758, 1425], "number_of_nod": [15, 25, 80, 156, 187, 311, 324, 337, 383, 564, 581, 852, 855, 876, 897, 900, 919, 933, 936, 958, 978, 981, 1001, 1154, 1271, 1426], "7482934": 15, "_": [15, 16, 26, 38, 93, 105, 300, 333, 356, 372, 405, 406, 425, 426, 502, 503, 506, 507, 569, 588, 630, 1352, 1354, 1378, 1380, 1411], "edge_type_visual_weight_lookup": 15, "edge_weight": [15, 382, 583], "node_attribut": [15, 691], "edge_attribut": [15, 283, 691, 1101], "summary_graph": [15, 691], "snap_aggreg": [15, 758, 1413], "prefix": [15, 67, 512, 690, 691, 1272, 1326, 1347, 1413, 1421], "aggreg": [15, 511, 512, 691, 786], "summary_po": 15, "8375428": 15, "edge_typ": 15, "get_edge_data": [15, 25, 1411], "plot_snap": [15, 17], "support": [16, 52, 77, 92, 93, 96, 100, 101, 102, 103, 226, 308, 322, 339, 340, 342, 343, 356, 373, 410, 411, 412, 418, 419, 464, 494, 496, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 597, 626, 627, 632, 633, 635, 636, 690, 738, 762, 775, 786, 796, 1037, 1038, 1039, 1040, 1114, 1116, 1146, 1302, 1326, 1341, 1342, 1344, 1353, 1354, 1355, 1356, 1357, 1358, 1379, 1380, 1381, 1383, 1387, 1394, 1395, 1396, 1398, 1402, 1404, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426], "unsupport": 16, "contain": [16, 25, 34, 45, 65, 69, 71, 88, 99, 102, 104, 114, 115, 151, 152, 157, 158, 165, 166, 167, 168, 172, 175, 176, 177, 180, 188, 189, 193, 195, 199, 207, 212, 214, 220, 226, 236, 237, 238, 240, 241, 243, 245, 248, 249, 252, 253, 255, 256, 257, 258, 259, 260, 264, 266, 267, 270, 277, 278, 280, 281, 290, 293, 294, 299, 315, 320, 322, 338, 344, 346, 347, 350, 352, 353, 355, 356, 357, 358, 360, 373, 377, 379, 380, 381, 388, 400, 408, 414, 415, 427, 432, 433, 437, 440, 457, 481, 482, 494, 495, 498, 499, 500, 502, 503, 506, 507, 509, 510, 512, 513, 514, 516, 549, 550, 564, 568, 572, 574, 589, 593, 596, 599, 602, 621, 624, 631, 632, 652, 656, 658, 660, 661, 662, 687, 688, 689, 695, 723, 724, 725, 726, 749, 786, 796, 853, 854, 856, 857, 862, 863, 864, 865, 868, 869, 870, 871, 877, 878, 882, 884, 887, 892, 898, 899, 901, 902, 907, 908, 909, 910, 913, 914, 921, 923, 925, 928, 934, 935, 937, 938, 943, 944, 945, 946, 949, 950, 951, 952, 959, 960, 963, 965, 968, 973, 979, 980, 982, 983, 988, 989, 990, 991, 994, 995, 1003, 1005, 1007, 1010, 1037, 1038, 1039, 1040, 1041, 1052, 1053, 1054, 1061, 1066, 1085, 1086, 1087, 1094, 1097, 1100, 1102, 1103, 1105, 1106, 1118, 1127, 1140, 1150, 1151, 1152, 1154, 1157, 1164, 1173, 1200, 1201, 1206, 1207, 1208, 1211, 1251, 1286, 1296, 1297, 1298, 1302, 1322, 1323, 1324, 1326, 1331, 1334, 1352, 1356, 1359, 1360, 1363, 1364, 1371, 1378, 1390, 1395, 1403, 1404, 1406, 1407, 1409, 1411, 1412, 1414, 1423, 1425, 1426], "entir": [16, 95, 101, 165, 179, 184, 260, 360, 375, 577, 862, 873, 907, 916, 943, 955, 988, 998, 1038, 1085, 1100, 1225, 1406, 1409], "adopt": [16, 96, 98, 101, 102, 107, 1405, 1414], "lobpcg": [16, 91, 1275, 1276, 1277], "python_exampl": 16, "graph_partit": 16, "categor": [16, 546, 547, 548, 611], "node_typ": [16, 1342, 1356, 1357], "supported_nod": 16, "unsupported_nod": 16, "remove_edges_from": [16, 89, 192, 453, 602, 881, 920, 962, 1002, 1175, 1177, 1222, 1393, 1394, 1412, 1420, 1426], "nbr": [16, 88, 159, 190, 199, 200, 207, 229, 230, 231, 285, 500, 506, 796, 858, 879, 887, 888, 892, 903, 925, 928, 939, 968, 969, 973, 984, 1007, 1010, 1037, 1039, 1040, 1094, 1326, 1404, 1426], "adj": [16, 88, 199, 200, 207, 324, 325, 796, 849, 887, 888, 892, 894, 915, 925, 928, 930, 968, 969, 973, 975, 996, 1007, 1010, 1037, 1039, 1040, 1094, 1326, 1404, 1411, 1417, 1425, 1426], "g_minus_h": 16, "strip": [16, 25, 69, 1215], "_node_color": 16, "_po": 16, "draw_networkx_edg": [16, 25, 26, 27, 28, 33, 35, 38, 39, 40, 41, 44, 46, 68, 83, 1130, 1133, 1134, 1136, 1137, 1411, 1413], "draw_networkx_label": [16, 25, 35, 38, 46, 71, 1130, 1133, 1134, 1135, 1137], "ncl": 16, "undirect": [16, 25, 34, 71, 93, 112, 177, 185, 204, 205, 209, 211, 212, 214, 215, 216, 217, 218, 219, 220, 221, 224, 227, 228, 229, 230, 231, 232, 237, 239, 240, 246, 247, 264, 267, 275, 277, 278, 280, 281, 293, 294, 295, 297, 298, 300, 313, 315, 318, 319, 321, 322, 328, 330, 331, 332, 333, 337, 338, 341, 345, 346, 347, 348, 349, 350, 352, 353, 371, 372, 379, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 428, 430, 431, 437, 439, 440, 450, 463, 464, 465, 466, 467, 478, 479, 480, 481, 482, 485, 486, 487, 488, 490, 491, 492, 500, 559, 560, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 581, 582, 583, 590, 594, 595, 598, 600, 601, 605, 606, 607, 610, 611, 613, 615, 618, 619, 624, 625, 652, 658, 681, 682, 683, 684, 686, 687, 688, 689, 692, 694, 717, 718, 727, 730, 731, 732, 734, 735, 736, 737, 738, 742, 743, 753, 760, 761, 762, 767, 779, 791, 874, 891, 917, 927, 956, 972, 999, 1009, 1036, 1038, 1056, 1060, 1088, 1090, 1098, 1101, 1115, 1133, 1135, 1146, 1166, 1167, 1173, 1175, 1182, 1184, 1187, 1189, 1190, 1191, 1193, 1196, 1197, 1198, 1199, 1202, 1206, 1207, 1217, 1219, 1230, 1243, 1244, 1247, 1250, 1251, 1252, 1254, 1259, 1273, 1275, 1276, 1278, 1279, 1282, 1298, 1323, 1326, 1327, 1333, 1341, 1342, 1344, 1351, 1352, 1353, 1354, 1371, 1377, 1378, 1379, 1380, 1381, 1383, 1389, 1390, 1395, 1401, 1402, 1404, 1406, 1408, 1411, 1414, 1417, 1426], "And": [16, 23, 47, 86, 93, 101, 107, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 467, 502, 503, 506, 507, 688, 1296, 1297, 1328, 1408, 1409, 1411, 1416, 1425], "specifi": [16, 24, 25, 62, 102, 151, 152, 157, 158, 167, 184, 185, 193, 207, 222, 223, 226, 232, 236, 238, 240, 241, 243, 244, 246, 247, 248, 260, 264, 266, 267, 268, 269, 271, 273, 276, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 299, 305, 310, 311, 320, 324, 326, 329, 338, 348, 349, 353, 356, 357, 374, 377, 410, 411, 412, 413, 414, 415, 418, 419, 433, 435, 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1402, 1403, 1404, 1406, 1407, 1412, 1416, 1426], "to_undirect": [16, 25, 69, 796, 1037, 1039, 1040, 1182, 1184, 1404, 1413, 1426], "magenta": 16, "six": 16, "classifi": [16, 512, 684, 750], "four": [16, 23, 47, 86, 99, 102, 165, 263, 585, 587, 692, 862, 907, 943, 988, 1039, 1040, 1164, 1193, 1199, 1211, 1323, 1407, 1408, 1414, 1426], "green": [16, 32, 38, 70, 93, 115, 464, 598, 760, 1302, 1330, 1394, 1412, 1426], "goal": [16, 88, 92, 99, 105, 107, 127, 383, 626, 627, 717, 718, 1042], "g_ex": 16, "m": [16, 25, 28, 30, 31, 63, 65, 67, 91, 93, 96, 102, 106, 110, 112, 128, 181, 191, 201, 209, 211, 212, 219, 227, 231, 235, 236, 238, 239, 240, 241, 243, 244, 248, 257, 258, 259, 263, 272, 274, 275, 278, 280, 282, 284, 293, 294, 296, 300, 301, 302, 308, 309, 315, 316, 317, 330, 338, 341, 343, 345, 352, 355, 356, 361, 362, 370, 380, 383, 385, 412, 429, 431, 432, 433, 451, 462, 479, 494, 498, 499, 509, 510, 511, 512, 519, 545, 555, 569, 582, 584, 585, 587, 588, 606, 614, 619, 625, 652, 658, 659, 684, 686, 691, 692, 706, 748, 749, 761, 762, 775, 872, 880, 889, 953, 961, 970, 1060, 1151, 1155, 1157, 1169, 1175, 1177, 1179, 1181, 1199, 1201, 1202, 1203, 1204, 1205, 1207, 1208, 1209, 1210, 1211, 1213, 1215, 1216, 1218, 1219, 1220, 1222, 1223, 1226, 1229, 1230, 1231, 1233, 1234, 1235, 1240, 1256, 1265, 1269, 1271, 1278, 1279, 1280, 1287, 1288, 1292, 1323, 1387, 1406, 1409, 1426], "node_color_list": 16, "nc": [16, 56], "spectral_layout": [16, 43, 1141, 1399, 1406], "subgraphs_of_g_ex": 16, "removed_edg": 16, "node_color_list_c": 16, "One": [16, 52, 55, 101, 102, 103, 115, 545, 559, 560, 679, 684, 761, 1177, 1186, 1272, 1315, 1326, 1404, 1426], "g_ex_r": 16, "compos": [16, 269, 270, 271, 272, 273, 274, 275, 276, 600, 604, 758, 1400, 1406, 1407, 1417, 1423, 1425], "previous": [16, 91, 108, 112, 322, 614, 1182, 1183, 1184, 1395, 1407, 1417], "store": [16, 25, 39, 53, 54, 55, 57, 67, 86, 93, 97, 101, 102, 110, 158, 219, 220, 283, 290, 345, 346, 347, 431, 470, 471, 472, 473, 474, 475, 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202, 204, 237, 242, 245, 246, 247, 265, 266, 268, 282, 283, 327, 512, 555, 629, 728, 730, 762, 786, 890, 891, 926, 971, 972, 1008, 1091, 1092, 1094, 1095, 1098, 1099, 1100, 1101, 1117, 1120, 1126, 1130, 1270, 1281, 1326, 1332, 1335, 1336, 1339, 1341, 1347, 1370, 1383, 1393, 1399, 1405, 1406, 1413], "primal": [52, 55, 508, 581], "dual": [52, 54, 55, 581, 1227, 1410, 1413], "sens": [52, 97, 99, 104, 199, 310, 460, 586, 791, 887, 925, 968, 1007, 1217, 1234, 1269, 1326, 1403, 1404], "approach": [52, 55, 99, 101, 103, 104, 107, 115, 341, 345, 462, 464, 466, 500, 519, 616, 678, 1094, 1175, 1188, 1202, 1222, 1407, 1413], "segment": [52, 55, 338], "major": [52, 95, 98, 99, 100, 102, 103, 104, 106, 107, 1393, 1394, 1403, 1404, 1407], "studi": [52, 91, 110, 606, 1192, 1196, 1323, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425], "topologi": [52, 55, 435, 436, 512, 681, 683, 748, 1202, 1217, 1225, 1229, 1233, 1241, 1326], "encod": [52, 55, 58, 67, 99, 141, 249, 267, 268, 619, 758, 775, 1326, 1333, 1334, 1337, 1338, 1339, 1340, 1341, 1344, 1345, 1348, 1349, 1350, 1354, 1355, 1358, 1363, 1368, 1371, 1372, 1375, 1376, 1382, 1406, 1407, 1412], "angular": [52, 55], "inform": [52, 66, 92, 93, 99, 100, 101, 102, 103, 107, 111, 112, 121, 132, 159, 165, 200, 202, 204, 220, 226, 230, 231, 249, 301, 302, 303, 308, 309, 314, 323, 324, 325, 338, 405, 406, 438, 453, 455, 480, 488, 500, 512, 564, 566, 568, 572, 573, 574, 583, 592, 614, 619, 624, 691, 775, 782, 786, 796, 858, 862, 888, 890, 891, 903, 907, 926, 927, 939, 943, 969, 971, 972, 984, 988, 1008, 1009, 1037, 1039, 1040, 1042, 1112, 1141, 1143, 1185, 1206, 1214, 1216, 1217, 1218, 1219, 1267, 1280, 1290, 1296, 1356, 1373, 1375, 1376, 1381, 1383, 1389, 1390, 1393, 1394, 1404, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426], "angl": [52, 55, 1114, 1116], "instead": [52, 93, 94, 101, 102, 103, 106, 141, 165, 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297, 298, 299, 303, 306, 307, 313, 322, 323, 338, 346, 347, 455, 1233], "compenvurbsi": 52, "2017": [52, 227, 512, 1207, 1208, 1406, 1407], "004": [52, 341], "scienc": [52, 91, 101, 105, 107, 109, 110, 112, 219, 228, 249, 296, 301, 302, 303, 308, 309, 323, 346, 347, 409, 412, 431, 441, 445, 446, 453, 476, 498, 618, 619, 680, 681, 683, 1203, 1223, 1255], "pydata": [52, 1413, 1423, 1424, 1425], "stack": [52, 111, 346, 693, 1045, 1046], "showcas": [53, 86, 93, 109], "analys": [53, 70, 86, 310], "ecosystem": [53, 86, 99, 100, 104, 107, 110, 1425], "descript": [53, 86, 93, 97, 464, 466, 704, 717, 786, 1130, 1131, 1132, 1133, 1138, 1139, 1140, 1141, 1142, 1207, 1222, 1242, 1407, 1411, 1413, 1421, 1422, 1425], "plu": [54, 386, 583, 1036, 1088, 1148, 1253], "voronoi": [54, 752, 758, 1325, 1407], "cholera": [54, 57], "broad": [54, 57, 1296], "pump": [54, 57], "record": [54, 57, 94, 99, 691, 1426], "john": [54, 57, 91, 278, 568, 572, 685, 1205, 1250, 1408, 1413], "snow": [54, 57], "1853": [54, 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992, 1275, 1276, 1277, 1300, 1349, 1404], "centroid": [54, 57, 58], "libpys": [54, 55, 57, 58], "cg": [54, 102, 296, 301, 302, 303, 308, 309, 323, 588], "voronoi_fram": 54, "contextili": [54, 55, 57], "add_basemap": [54, 55, 57], "geopackag": [54, 55, 56, 57], "sqlite": [54, 57], "reli": [54, 57, 99, 103, 362, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 502, 503, 506, 507, 1393, 1407, 1411, 1425], "fiona": [54, 57], "level": [54, 57, 101, 103, 104, 106, 111, 112, 115, 125, 165, 220, 322, 334, 336, 374, 380, 381, 387, 389, 390, 394, 423, 427, 640, 691, 770, 786, 862, 907, 943, 988, 1012, 1013, 1018, 1019, 1020, 1021, 1022, 1094, 1108, 1155, 1202, 1207, 1208, 1236, 1296, 1323, 1328, 1396, 1399, 1407, 1412, 1413, 1414], "interfac": [54, 57, 58, 75, 76, 96, 98, 99, 101, 102, 107, 109, 110, 184, 429, 496, 673, 758, 761, 762, 780, 873, 916, 955, 998, 1042, 1044, 1326, 1328, 1393, 1396, 1398, 1402, 1404, 1405, 1406, 1409, 1413, 1414, 1426], "kind": [54, 57, 58, 92, 93, 94, 99, 208, 466, 722, 1202, 1326, 1383], "read_fil": [54, 55, 57, 58], "cholera_cas": [54, 57], "gpkg": [54, 56, 57], "correctli": [54, 164, 324, 325, 1393, 1404, 1406, 1411, 1412, 1419], "construct": [54, 55, 56, 57, 58, 67, 94, 102, 227, 229, 230, 231, 232, 269, 273, 276, 352, 423, 450, 460, 513, 545, 546, 547, 548, 552, 553, 554, 556, 557, 558, 609, 685, 695, 708, 716, 732, 1046, 1047, 1052, 1053, 1101, 1102, 1103, 1104, 1105, 1153, 1154, 1175, 1177, 1178, 1180, 1186, 1190, 1191, 1192, 1195, 1203, 1207, 1208, 1209, 1210, 1217, 1219, 1222, 1229, 1236, 1251, 1259, 1263, 1269, 1272, 1278, 1279, 1296, 1323, 1327, 1395, 1399, 1406, 1409, 1415], "column_stack": [54, 57, 58], "could": [54, 93, 101, 102, 103, 165, 215, 216, 224, 581, 679, 862, 907, 943, 988, 1066, 1094, 1102, 1103, 1120, 1126, 1174, 1296, 1300, 1326, 1393, 1404, 1414, 1426], "present": [54, 58, 93, 107, 110, 132, 184, 220, 226, 315, 316, 330, 357, 359, 429, 494, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 567, 581, 594, 595, 597, 600, 601, 604, 632, 633, 635, 636, 659, 670, 749, 786, 873, 916, 955, 998, 1043, 1045, 1061, 1082, 1150, 1152, 1157, 1159, 1160, 1163, 1165, 1278, 1279, 1353, 1354, 1357, 1381, 1383, 1407, 1411, 1426], "alongsid": [54, 438], "diagram": [54, 132, 381, 752], "intrins": 54, "put": [54, 92, 95, 102, 226, 1326, 1404, 1406], "underli": [54, 101, 102, 132, 152, 157, 158, 161, 195, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 427, 428, 490, 491, 500, 615, 742, 743, 791, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005, 1038, 1225, 1233, 1241, 1326, 1393, 1394, 1402], "quickli": [54, 1239], "Be": [54, 92, 1038, 1135, 1404], "care": [54, 92, 100, 102, 106, 107, 109, 115, 156, 855, 900, 936, 981, 1038, 1326, 1404, 1406], "bound": [54, 112, 214, 215, 216, 217, 220, 224, 227, 264, 300, 342, 352, 437, 440, 675, 1043, 1165, 1235, 1319, 1413, 1414, 1416], "box": [54, 107, 1134, 1136, 1271, 1323], "control": [54, 168, 179, 189, 204, 230, 231, 324, 325, 450, 467, 865, 878, 891, 910, 946, 960, 991, 1328, 1402, 1408, 1409, 1413], "cell": [54, 58, 752, 758, 1271, 1323, 1325, 1407], "convex": 54, "hull": 54, "contigu": [54, 58, 438, 1102, 1277, 1278], "being": [54, 92, 94, 95, 99, 101, 102, 109, 217, 227, 464, 465, 466, 559, 560, 711, 1038, 1045, 1144, 1175, 1236, 1296, 1393, 1394, 1407, 1412, 1413, 1416, 1425], "face": [54, 101, 102, 115, 183, 206, 615, 1043, 1262, 1263], "analogu": [54, 58, 230], "von": 54, "neuman": 54, "neighborhood": [54, 58, 114, 213, 240, 249, 285, 286, 324, 325, 512, 690, 786, 1189], "cardin": [54, 115, 218, 221, 264, 277, 278, 279, 280, 339, 341, 343, 345, 414, 415, 416, 417, 428, 440, 441, 444, 446, 581, 583, 611, 691, 1395], "regular": [54, 58, 65, 88, 99, 477, 478, 479, 480, 622, 623, 624, 758, 1038, 1185, 1190, 1191, 1192, 1239, 1245, 1250, 1251, 1254, 1258, 1261, 1262, 1263, 1264, 1280, 1290, 1323, 1325, 1394, 1395, 1398, 1406, 1412, 1413], "come": [54, 93, 100, 101, 102, 517, 577, 588, 598, 608, 677, 698, 699, 1046, 1243, 1326, 1402, 1413], "piec": [54, 374], "move": [54, 94, 95, 100, 101, 230, 231, 377, 380, 1117, 1207, 1210, 1393, 1395, 1404, 1405, 1406, 1407, 1411, 1413, 1416, 1419, 1421, 1425], "chessboard": 54, "from_datafram": [54, 55, 57, 58], "built": [54, 69, 93, 102, 103, 106, 230, 231, 362, 464, 1102, 1103, 1105, 1182, 1183, 1184, 1296, 1328, 1396, 1426], "relev": [54, 93, 99, 101, 103, 104, 106, 132, 168, 176, 184, 189, 497, 501, 504, 505, 508, 657, 865, 870, 873, 878, 910, 916, 946, 951, 955, 960, 991, 998, 1084, 1307, 1312, 1323, 1411, 1417], "delaunay_graph": 54, "merg": [54, 57, 58, 93, 99, 100, 106, 383, 584, 585, 587, 1322, 1403], "nice": [54, 57, 58, 101, 214, 494, 1061, 1328, 1410], "basemap": [54, 57, 58], "lightblu": [54, 58], "cornsilk": 54, "808": [54, 59], "plot_delaunai": [54, 59], "sometim": [55, 63, 92, 94, 99, 102, 109, 199, 346, 347, 611, 729, 731, 887, 925, 968, 1007, 1043, 1117, 1155, 1247, 1328, 1404], "linestr": 55, "altern": [55, 58, 76, 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498, 499, 501, 502, 503, 506, 507, 509, 510, 565], "pick": [141, 217, 331, 657, 1188, 1207, 1210, 1407], "st": [141, 415, 417], "cut": [141, 222, 223, 293, 377, 382, 387, 389, 390, 394, 411, 412, 414, 415, 416, 417, 419, 427, 428, 429, 442, 443, 444, 445, 447, 494, 495, 498, 499, 500, 502, 503, 506, 507, 509, 510, 619, 758, 760, 1038, 1066, 1115, 1262, 1325, 1395, 1402, 1406, 1413], "refin": [143, 215, 423, 438], "auxgraph": [143, 423], "node_partit": 144, "permut": [144, 368, 452, 453, 455, 466, 748, 1285, 1320, 1321], "containin": 144, "frozenset": [144, 267, 339, 383, 586, 588, 752, 1165, 1333, 1337, 1338, 1412], "abc": [144, 545, 1154, 1206, 1303, 1412, 1413], "interchang": [144, 362], "bool": [145, 146, 148, 149, 165, 168, 171, 176, 184, 189, 196, 204, 208, 232, 237, 238, 242, 243, 245, 249, 250, 258, 265, 266, 267, 268, 272, 275, 286, 287, 288, 291, 294, 295, 296, 297, 298, 299, 301, 302, 305, 306, 307, 308, 309, 310, 314, 315, 322, 324, 325, 326, 327, 330, 343, 350, 355, 362, 393, 394, 395, 396, 397, 398, 439, 454, 462, 463, 467, 479, 480, 488, 489, 491, 494, 498, 499, 509, 510, 513, 514, 515, 516, 517, 518, 520, 521, 522, 545, 562, 564, 578, 579, 580, 581, 588, 613, 614, 616, 617, 622, 623, 625, 640, 652, 663, 673, 679, 685, 690, 696, 698, 699, 700, 704, 708, 719, 723, 724, 725, 726, 728, 730, 733, 734, 735, 736, 737, 738, 740, 741, 742, 743, 862, 865, 867, 870, 873, 878, 885, 891, 907, 910, 912, 916, 927, 931, 943, 946, 948, 951, 955, 960, 966, 972, 976, 988, 991, 993, 998, 1039, 1040, 1045, 1057, 1068, 1070, 1071, 1072, 1084, 1091, 1097, 1116, 1133, 1134, 1135, 1136, 1169, 1179, 1185, 1189, 1209, 1211, 1212, 1213, 1215, 1224, 1228, 1230, 1231, 1232, 1275, 1276, 1277, 1278, 1279, 1282, 1295, 1296, 1307, 1309, 1312, 1335, 1336, 1337, 1339, 1341, 1342, 1344, 1353, 1354, 1355, 1356, 1357, 1358, 1360, 1364, 1379, 1380], "account": [145, 148, 398, 448, 749, 761, 1270, 1393, 1413], "graph_nod": [145, 148], "subgraph_nod": [145, 148], "find_isomorph": [147, 150], "induc": [148, 167, 199, 211, 226, 342, 388, 392, 406, 427, 436, 437, 470, 487, 494, 495, 498, 499, 502, 503, 506, 507, 509, 510, 512, 586, 589, 752, 761, 762, 864, 887, 909, 925, 945, 968, 990, 1007, 1038, 1061, 1066, 1087, 1102, 1103, 1105, 1189, 1283, 1284, 1393], "u_of_edg": [151, 853, 898], "v_of_edg": [151, 853, 898], "capac": [151, 265, 296, 301, 302, 303, 308, 309, 323, 411, 412, 415, 416, 417, 418, 419, 430, 431, 494, 495, 496, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 758, 853, 898, 934, 979, 1335, 1402], "342": [151, 853, 898, 934, 979, 1255], "ebunch_to_add": [152, 158, 854, 857, 899, 902, 935, 938, 980, 983], "add_weighted_edges_from": [152, 229, 230, 231, 508, 581, 630, 657, 659, 721, 854, 899, 935, 980, 1070, 1326, 1404, 1407, 1426], "runtimeerror": [152, 157, 158, 195, 464, 465, 466, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005], "happen": [152, 157, 158, 195, 380, 584, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005, 1403, 1404, 1425], "iterator_of_edg": [152, 158, 854, 857, 899, 902, 935, 938, 980, 983], "wn2898": [152, 854, 899, 935, 980], "wrong": [152, 157, 158, 722, 854, 856, 857, 899, 901, 902, 935, 937, 938, 980, 982, 983, 1406, 1411, 1416, 1425], "start_nod": [153, 154, 155], "end_nod": [153, 154, 155], "reference_neighbor": [153, 154], "half": [153, 154, 155, 164, 177, 183, 206, 297, 298, 615, 653], "clockwis": [153, 154, 169, 182, 197, 615], "networkxexcept": [153, 154, 161, 331, 588, 593, 724, 726, 1043, 1110, 1138, 1180, 1325], "add_half_edge_cw": [153, 155, 164, 615], "connect_compon": [153, 154, 155, 615], "add_half_edge_first": [153, 154, 164, 615], "add_half_edge_ccw": [154, 155, 164, 615], "node_for_ad": [156, 855, 900, 936, 981], "mutabl": [156, 855, 900, 936, 981, 1061, 1066, 1082, 1085, 1086], "hash": [156, 511, 512, 758, 855, 900, 936, 981, 1324, 1325, 1414, 1426], "hello": [156, 157, 855, 856, 900, 901, 936, 937, 981, 982, 1303], "k3": [156, 157, 855, 856, 900, 901, 936, 937, 981, 982, 1217], "utm": [156, 855, 900, 936, 981], "382871": [156, 855, 900, 936, 981], "3972649": [156, 855, 900, 936, 981], "nodes_for_ad": [157, 856, 901, 937, 982], "iterator_of_nod": [157, 195, 856, 884, 901, 923, 937, 965, 982, 1005], "datadict": [159, 190, 200, 207, 734, 736, 858, 879, 888, 892, 903, 928, 939, 969, 973, 1010, 1084, 1312, 1326], "foovalu": [159, 190, 200, 858, 879, 888, 903, 939, 969], "nbrdict": [160, 859, 904, 940, 985, 1019, 1094], "fulfil": [161, 615], "cw": [161, 615], "ccw": [161, 615], "planar": [161, 614, 616, 617, 758, 1110, 1138, 1243, 1246, 1247, 1249, 1325, 1409, 1410], "first_nbr": [161, 615], "invalid": [161, 615, 1413], "alter": [163, 861, 906, 942, 987], "afterward": 164, "as_view": [165, 202, 204, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1089, 1090], "shallow": [165, 202, 204, 284, 285, 286, 287, 288, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1394], "deepcopi": [165, 202, 204, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1409], "__class__": [165, 199, 862, 887, 907, 925, 943, 968, 988, 1007, 1404, 1407, 1409, 1410, 1411], "fresh": [165, 862, 907, 943, 988, 1404], "inspir": [165, 230, 231, 342, 681, 862, 907, 943, 988, 1226, 1323, 1404], "deep": [165, 202, 204, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1265, 1394], "degreeview": [166, 863, 908, 944, 950, 989, 1404, 1426], "didegreeview": [166, 863], "outedgeview": [168, 189, 467, 468, 613, 747, 750, 865, 878, 1035, 1083, 1404, 1418], "ddict": [168, 176, 184, 189, 865, 870, 873, 878, 910, 916, 946, 951, 955, 960, 991, 998], "in_edg": [168, 189, 865, 878, 946, 960, 1404, 1406, 1407], "out_edg": [168, 865, 946, 1062, 1404, 1406, 1407, 1426], "quietli": [168, 189, 865, 878, 910, 946, 960, 991, 1087, 1426], "outedgedataview": [168, 189, 865, 878, 1404, 1411], "set_data": 169, "edge_dict": [170, 866, 911, 947, 992], "safe": [170, 866, 911, 1404, 1412], "edge_ind": [171, 867, 912, 948, 993], "data_dictionari": [171, 867, 912], "simpler": [172, 184, 868, 873, 913, 916, 949, 955, 994, 998, 1406, 1407, 1417], "indegreeview": [175, 869, 1404], "deg": [175, 188, 243, 259, 356, 361, 685, 869, 877, 950, 959, 1165, 1179, 1222, 1404], "inedgeview": [176, 870, 1404], "inedgedataview": [176, 870], "silent": [180, 193, 195, 320, 871, 882, 884, 914, 921, 923, 952, 963, 965, 995, 1003, 1005, 1085, 1086, 1127, 1353, 1354, 1359, 1363, 1406, 1413], "niter": [180, 681, 682, 683, 684, 851, 871, 896, 914, 932, 952, 977, 995, 1414], "__iter__": [180, 871, 914, 952, 995, 1303], "nodedata": [184, 873, 916, 955, 998], "5pm": [184, 796, 873, 916, 955, 998, 1037, 1039, 1040, 1394, 1426], "Not": [184, 379, 432, 433, 434, 435, 436, 437, 438, 476, 873, 916, 955, 998, 1117, 1216], "nedg": [185, 588, 874, 917, 956, 999], "__len__": [186, 187, 875, 876, 918, 919, 957, 958, 1000, 1001], "outdegreeview": [188, 877], "Will": [193, 362, 605, 607, 610, 882, 921, 963, 1003, 1404, 1414], "get_data": [197, 616], "inplac": [199, 690, 887, 925, 968, 1007, 1066, 1393], "reduct": [199, 469, 618, 786, 887, 925, 968, 1007, 1066, 1320, 1321, 1413, 1414], "sg": [199, 887, 925, 968, 1007], "largest_wcc": [199, 887, 925, 968, 1007], "is_multigraph": [199, 758, 887, 925, 968, 1007, 1154, 1412], "keydict": [199, 207, 887, 892, 925, 928, 968, 973, 1007, 1010, 1039, 1040], "contrast": [202, 204, 301, 302, 308, 309, 890, 891, 926, 927, 971, 972, 1008, 1009, 1066, 1233, 1241, 1426], "reciproc": [204, 299, 320, 322, 356, 411, 430, 447, 476, 620, 758, 891, 972, 1325, 1416, 1425], "mark_half_edg": 206, "li": [206, 619, 670, 675, 685, 775, 1207, 1210, 1425], "straightforward": [207, 892, 928, 973, 1010], "slightli": [207, 326, 437, 520, 521, 581, 892, 928, 973, 1010, 1165, 1326, 1404, 1407, 1412, 1414, 1425], "singleton": [207, 588, 892, 928, 973, 1010, 1218, 1251, 1407], "preserve_attr": [208, 723, 724, 725, 726], "optimum": [208, 231, 583, 720, 722, 791, 1395, 1406], "arboresc": [208, 460, 719, 720, 722, 724, 726, 740, 743, 758, 1272, 1395, 1406], "span": [208, 226, 227, 228, 295, 508, 618, 619, 624, 719, 720, 722, 724, 726, 732, 733, 734, 735, 736, 737, 738, 758, 1394, 1397, 1406, 1407, 1420], "max_ind_cliqu": 209, "networkxnotimpl": [209, 210, 211, 212, 220, 224, 227, 293, 294, 295, 318, 319, 321, 328, 343, 379, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 403, 404, 405, 406, 407, 422, 424, 425, 426, 427, 429, 455, 457, 458, 459, 460, 468, 481, 482, 500, 589, 590, 608, 680, 732, 1043, 1216, 1275, 1276, 1298, 1325, 1353, 1354, 1379, 1407, 1408], "boppana": [209, 211, 212], "halld\u00f3rsson": [209, 211, 212], "1992": [209, 211, 212, 517, 518, 1407], "exclud": [209, 211, 212, 215, 216, 261, 262, 453, 688, 719, 723, 724, 725, 726, 733, 751, 1036, 1038, 1088, 1217, 1412], "180": [209, 211, 212, 238], "196": [209, 211, 212], "heurist": [210, 220, 228, 233, 234, 377, 380, 381, 427, 494, 509, 626, 627, 652, 663, 703, 758, 1173, 1320, 1321, 1325, 1395, 1408, 1412, 1413], "max_cliqu": 210, "rigor": 210, "pattabiraman": 210, "bharath": 210, "massiv": [210, 217], "421": 210, "448": 210, "1080": [210, 297, 298, 306, 307, 329], "15427951": 210, "986778": 210, "apx": [211, 212], "subseteq": [211, 280, 289, 618, 675], "omega": [211, 758, 782, 1414], "maximum_cliqu": 211, "1007": [211, 226, 296, 301, 302, 303, 308, 309, 323, 324, 325, 341, 431, 451, 498, 574, 1144, 1181], "bf01994876": 211, "iset": 212, "trial": [213, 230, 231, 1195, 1237, 1238], "estim": [213, 224, 297, 306, 313, 564, 625, 626, 627, 782, 1280, 1407], "coeffici": [213, 248, 260, 261, 262, 263, 289, 355, 356, 358, 570, 618, 619, 625, 682, 684, 778, 782, 1397, 1398, 1399, 1406, 1413], "fraction": [213, 257, 259, 286, 289, 297, 299, 304, 306, 315, 317, 318, 319, 321, 322, 326, 328, 330, 356, 358, 359, 519, 1165, 1234], "schank": 213, "thoma": [213, 751, 1407, 1409, 1413], "dorothea": [213, 1168], "wagner": [213, 429, 758, 1168, 1402, 1406], "universit\u00e4t": 213, "karlsruh": 213, "fakult\u00e4t": 213, "f\u00fcr": 213, "informatik": [213, 412], "5445": 213, "ir": [213, 606], "1000001239": 213, "erdos_renyi_graph": [213, 1224, 1232, 1326, 1406, 1426], "214": 213, "cutoff": [214, 215, 310, 326, 383, 410, 411, 412, 418, 419, 494, 495, 498, 499, 510, 637, 638, 640, 641, 642, 643, 644, 647, 648, 649, 656, 660, 661, 662, 667, 668, 669, 677, 678, 1234, 1398, 1402, 1406, 1413, 1416, 1424, 1425], "distinct": [214, 215, 255, 281, 288, 352, 391, 452, 453, 460, 578, 595, 608, 618, 700, 701, 734, 735, 736, 737, 789, 1150, 1244, 1271, 1323, 1326, 1328, 1395, 1417], "nonadjac": [214, 215, 480, 584, 585, 587], "cutset": [214, 215, 414, 415, 416, 417, 427, 428, 500, 506, 758], "menger": [214, 215, 216], "theorem": [214, 215, 216, 220, 235, 281, 311, 312, 322, 411, 506, 507, 514, 517, 518, 618, 1190, 1205], "local_node_connect": [214, 216, 408, 409, 410, 411, 413], "node_connect": [214, 215, 409, 410, 411, 412, 414, 415, 416, 417, 419, 427, 428, 1402], "dougla": [214, 215, 216, 220, 1413, 1425], "035": [214, 215, 216, 220], "eclect": [214, 215, 216], "ss": [214, 215, 216], "uci": [214, 215, 216, 467, 704, 706, 707, 708, 710, 734, 736], "drwhite": [214, 215, 216], "pprint": [214, 577, 711], "all_pairs_node_connect": [215, 216, 1402, 1424, 1425], "bf": [215, 216, 217, 363, 588, 704, 706, 707, 708, 717, 1397, 1401, 1406, 1409, 1412, 1413], "lose": [215, 796, 1037, 1039, 1040], "accuraci": [215, 312, 786], "platon": [215, 216, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 1245, 1248, 1254, 1257, 1261, 1263], "octahedr": [215, 216, 1257], "approx": [215, 216, 227, 229, 230, 231, 1413], "octahedral_graph": [215, 216], "vari": [217, 238, 243, 373, 378, 569, 695], "sweep": [217, 1412], "dsweep": 217, "a_1": [217, 477], "a_2": 217, "magnien": [217, 260, 261, 262, 289], "cl\u00e9menc": [217, 260, 261, 262, 289], "matthieu": [217, 260, 261, 262, 274, 289], "latapi": [217, 260, 261, 262, 274, 289], "michel": 217, "habib": 217, "empir": 217, "tight": 217, "jea": 217, "0904": 217, "2728": 217, "crescenzi": 217, "pierluigi": 217, "roberto": 217, "grossi": 217, "leonardo": 217, "lanzi": 217, "andrea": [217, 1165, 1413], "marino": 217, "symposium": [217, 619, 1186, 1195, 1239], "berlin": [217, 520, 521, 1413], "heidelberg": [217, 520, 521], "ut": 217, "ee": [217, 313], "mtat": 217, "238": 217, "2014_fall": 217, "domin": [218, 219, 311, 410, 414, 481, 482, 483, 484, 758, 1325, 1395, 1400, 1406, 1407], "opt": [218, 221, 1425], "min_weight_dominating_set": 219, "vazirani": [219, 221], "vijai": [219, 221, 517], "min_dens": 220, "95": [220, 590, 1283, 1284, 1382], "nest": [220, 427, 728, 730, 791, 1038, 1045, 1061, 1094, 1296, 1308, 1348, 1355, 1356, 1357, 1358, 1383, 1406], "forth": [220, 427], "relax": [220, 227, 1171, 1413], "narrow": [220, 1165], "whitnei": 220, "bicompon": [220, 387, 389, 390, 394], "ferraro": [220, 427], "cohes": [220, 427, 437], "1503": [220, 427], "04476v1": [220, 427], "santaf": 220, 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"license"]], "Bibliography": [[110, "bibliography"]], "Install": [[111, "install"]], "Install the released version": [[111, "install-the-released-version"]], "Install the development version": [[111, "install-the-development-version"]], "Extra packages": [[111, "extra-packages"]], "Test a source distribution": [[111, "test-a-source-distribution"]], "Test an installed package": [[111, "test-an-installed-package"]], "Approximations and Heuristics": [[112, "module-networkx.algorithms.approximation"]], "Connectivity": [[112, "module-networkx.algorithms.approximation.connectivity"], [126, "connectivity"], [127, "module-networkx.algorithms.connectivity"]], "K-components": [[112, "module-networkx.algorithms.approximation.kcomponents"]], "Clique": [[112, "module-networkx.algorithms.approximation.clique"], [121, "module-networkx.algorithms.clique"]], "Clustering": [[112, "module-networkx.algorithms.approximation.clustering_coefficient"], [115, "module-networkx.algorithms.bipartite.cluster"], 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"effective_size": [[688, "effective-size"]], "local_constraint": [[689, "local-constraint"]], "dedensify": [[690, "dedensify"]], "snap_aggregation": [[691, "snap-aggregation"]], "connected_double_edge_swap": [[692, "connected-double-edge-swap"]], "directed_edge_swap": [[693, "directed-edge-swap"]], "double_edge_swap": [[694, "double-edge-swap"]], "find_threshold_graph": [[695, "find-threshold-graph"]], "is_threshold_graph": [[696, "is-threshold-graph"]], "hamiltonian_path": [[697, "hamiltonian-path"]], "is_reachable": [[698, "is-reachable"]], "is_tournament": [[700, "is-tournament"]], "random_tournament": [[701, "random-tournament"]], "score_sequence": [[702, "score-sequence"]], "bfs_beam_edges": [[703, "bfs-beam-edges"]], "bfs_edges": [[704, "bfs-edges"]], "bfs_layers": [[705, "bfs-layers"]], "bfs_predecessors": [[706, "bfs-predecessors"]], "bfs_successors": [[707, "bfs-successors"]], "bfs_tree": [[708, "bfs-tree"]], "descendants_at_distance": [[709, "descendants-at-distance"]], "dfs_edges": [[710, "dfs-edges"]], "dfs_labeled_edges": [[711, "dfs-labeled-edges"]], "dfs_postorder_nodes": [[712, "dfs-postorder-nodes"]], "dfs_predecessors": [[713, "dfs-predecessors"]], "dfs_preorder_nodes": [[714, "dfs-preorder-nodes"]], "dfs_successors": [[715, "dfs-successors"]], "dfs_tree": [[716, "dfs-tree"]], "edge_bfs": [[717, "edge-bfs"]], "edge_dfs": [[718, "edge-dfs"]], "networkx.algorithms.tree.branchings.ArborescenceIterator": [[719, "networkx-algorithms-tree-branchings-arborescenceiterator"]], "networkx.algorithms.tree.branchings.Edmonds": [[720, "networkx-algorithms-tree-branchings-edmonds"]], "branching_weight": [[721, "branching-weight"]], "greedy_branching": [[722, "greedy-branching"]], "maximum_branching": [[723, "maximum-branching"]], "maximum_spanning_arborescence": [[724, "maximum-spanning-arborescence"]], "minimum_branching": [[725, "minimum-branching"]], "minimum_spanning_arborescence": [[726, "minimum-spanning-arborescence"]], "NotATree": [[727, "notatree"]], "from_nested_tuple": [[728, "from-nested-tuple"]], "from_prufer_sequence": [[729, "from-prufer-sequence"]], "to_nested_tuple": [[730, "to-nested-tuple"]], "to_prufer_sequence": [[731, "to-prufer-sequence"]], "junction_tree": [[732, "junction-tree"]], "networkx.algorithms.tree.mst.SpanningTreeIterator": [[733, "networkx-algorithms-tree-mst-spanningtreeiterator"]], "maximum_spanning_edges": [[734, "maximum-spanning-edges"]], "maximum_spanning_tree": [[735, "maximum-spanning-tree"]], "minimum_spanning_edges": [[736, "minimum-spanning-edges"]], "minimum_spanning_tree": [[737, "minimum-spanning-tree"]], "random_spanning_tree": [[738, "random-spanning-tree"]], "join": [[739, "join"]], "is_arborescence": [[740, "is-arborescence"]], "is_branching": [[741, "is-branching"]], "is_forest": [[742, "is-forest"]], "is_tree": [[743, "is-tree"]], "all_triads": [[744, "all-triads"]], "all_triplets": [[745, "all-triplets"]], "is_triad": [[746, "is-triad"]], "random_triad": [[747, "random-triad"]], "triad_type": [[748, "triad-type"]], "triadic_census": [[749, "triadic-census"]], "triads_by_type": [[750, "triads-by-type"]], "closeness_vitality": [[751, "closeness-vitality"]], "voronoi_cells": [[752, "voronoi-cells"]], "wiener_index": [[753, "wiener-index"]], "Graph Hashing": [[754, "module-networkx.algorithms.graph_hashing"]], "Graphical degree sequence": [[755, "module-networkx.algorithms.graphical"]], "Hierarchy": [[756, "module-networkx.algorithms.hierarchy"]], "Hybrid": [[757, "module-networkx.algorithms.hybrid"]], "Isolates": [[759, "module-networkx.algorithms.isolate"]], "Isomorphism": [[760, "isomorphism"]], "VF2++": [[760, "module-networkx.algorithms.isomorphism.vf2pp"]], "VF2++ Algorithm": [[760, "vf2-algorithm"]], "Tree Isomorphism": [[760, "module-networkx.algorithms.isomorphism.tree_isomorphism"]], "Advanced Interfaces": [[760, "advanced-interfaces"]], "ISMAGS Algorithm": [[761, "module-networkx.algorithms.isomorphism.ismags"]], "Notes": [[761, "notes"], [762, "notes"]], "ISMAGS object": [[761, "ismags-object"]], "VF2 Algorithm": [[762, "module-networkx.algorithms.isomorphism.isomorphvf2"]], "Subgraph Isomorphism": [[762, "subgraph-isomorphism"]], "Graph Matcher": [[762, "graph-matcher"]], "DiGraph Matcher": [[762, "digraph-matcher"]], "Match helpers": [[762, "match-helpers"]], "Link Analysis": [[763, "link-analysis"]], "PageRank": [[763, "module-networkx.algorithms.link_analysis.pagerank_alg"]], "Hits": [[763, "module-networkx.algorithms.link_analysis.hits_alg"]], "Link Prediction": [[764, "module-networkx.algorithms.link_prediction"]], "Lowest Common Ancestor": [[765, "module-networkx.algorithms.lowest_common_ancestors"]], "Minors": [[767, "module-networkx.algorithms.minors"]], "Maximal independent set": [[768, "module-networkx.algorithms.mis"]], "Moral": [[769, "module-networkx.algorithms.moral"]], "Node Classification": [[770, "module-networkx.algorithms.node_classification"]], "non-randomness": [[771, "module-networkx.algorithms.non_randomness"]], "Operators": [[772, "operators"]], "Planar Drawing": [[773, "module-networkx.algorithms.planar_drawing"]], "Planarity": [[774, "module-networkx.algorithms.planarity"]], "Graph Polynomials": [[775, "module-networkx.algorithms.polynomials"]], "Reciprocity": [[776, "module-networkx.algorithms.reciprocity"]], "Regular": [[777, "module-networkx.algorithms.regular"]], "Rich Club": [[778, "module-networkx.algorithms.richclub"]], "Shortest Paths": [[779, "module-networkx.algorithms.shortest_paths.generic"]], "Advanced Interface": [[779, "module-networkx.algorithms.shortest_paths.unweighted"]], "Dense Graphs": [[779, "module-networkx.algorithms.shortest_paths.dense"]], "A* Algorithm": [[779, "module-networkx.algorithms.shortest_paths.astar"]], "Similarity Measures": [[780, "module-networkx.algorithms.similarity"]], "Simple Paths": [[781, "module-networkx.algorithms.simple_paths"]], "Small-world": [[782, "module-networkx.algorithms.smallworld"]], "s metric": [[783, "module-networkx.algorithms.smetric"]], "Sparsifiers": [[784, "module-networkx.algorithms.sparsifiers"]], "Structural holes": [[785, "module-networkx.algorithms.structuralholes"]], "Summarization": [[786, "module-networkx.algorithms.summarization"]], "Swap": [[787, "module-networkx.algorithms.swap"]], "Threshold Graphs": [[788, "module-networkx.algorithms.threshold"]], "Tournament": [[789, "module-networkx.algorithms.tournament"]], "Traversal": [[790, "traversal"]], "Depth First Search": [[790, "module-networkx.algorithms.traversal.depth_first_search"]], "Breadth First Search": [[790, "module-networkx.algorithms.traversal.breadth_first_search"]], "Beam search": [[790, "module-networkx.algorithms.traversal.beamsearch"]], "Depth First Search on Edges": [[790, "module-networkx.algorithms.traversal.edgedfs"]], "Breadth First Search on Edges": [[790, "module-networkx.algorithms.traversal.edgebfs"]], "Tree": [[791, "tree"]], "Recognition": [[791, 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"AdjacencyView.values": [[801, "adjacencyview-values"]], "AtlasView.copy": [[802, "atlasview-copy"]], "AtlasView.get": [[803, "atlasview-get"]], "AtlasView.items": [[804, "atlasview-items"]], "AtlasView.keys": [[805, "atlasview-keys"]], "AtlasView.values": [[806, "atlasview-values"]], "FilterAdjacency.get": [[807, "filteradjacency-get"]], "FilterAdjacency.items": [[808, "filteradjacency-items"]], "FilterAdjacency.keys": [[809, "filteradjacency-keys"]], "FilterAdjacency.values": [[810, "filteradjacency-values"]], "FilterAtlas.get": [[811, "filteratlas-get"]], "FilterAtlas.items": [[812, "filteratlas-items"]], "FilterAtlas.keys": [[813, "filteratlas-keys"]], "FilterAtlas.values": [[814, "filteratlas-values"]], "FilterMultiAdjacency.get": [[815, "filtermultiadjacency-get"]], "FilterMultiAdjacency.items": [[816, "filtermultiadjacency-items"]], "FilterMultiAdjacency.keys": [[817, "filtermultiadjacency-keys"]], "FilterMultiAdjacency.values": [[818, "filtermultiadjacency-values"]], "FilterMultiInner.get": [[819, "filtermultiinner-get"]], "FilterMultiInner.items": [[820, "filtermultiinner-items"]], "FilterMultiInner.keys": [[821, "filtermultiinner-keys"]], "FilterMultiInner.values": [[822, "filtermultiinner-values"]], "MultiAdjacencyView.copy": [[823, "multiadjacencyview-copy"]], "MultiAdjacencyView.get": [[824, "multiadjacencyview-get"]], "MultiAdjacencyView.items": [[825, "multiadjacencyview-items"]], "MultiAdjacencyView.keys": [[826, "multiadjacencyview-keys"]], "MultiAdjacencyView.values": [[827, "multiadjacencyview-values"]], "UnionAdjacency.copy": [[828, "unionadjacency-copy"]], "UnionAdjacency.get": [[829, "unionadjacency-get"]], "UnionAdjacency.items": [[830, "unionadjacency-items"]], "UnionAdjacency.keys": [[831, "unionadjacency-keys"]], "UnionAdjacency.values": [[832, "unionadjacency-values"]], "UnionAtlas.copy": [[833, "unionatlas-copy"]], "UnionAtlas.get": [[834, "unionatlas-get"]], "UnionAtlas.items": [[835, "unionatlas-items"]], "UnionAtlas.keys": 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"DiGraph.add_edges_from": [[854, "digraph-add-edges-from"]], "DiGraph.add_node": [[855, "digraph-add-node"]], "DiGraph.add_nodes_from": [[856, "digraph-add-nodes-from"]], "DiGraph.add_weighted_edges_from": [[857, "digraph-add-weighted-edges-from"]], "DiGraph.adj": [[858, "digraph-adj"]], "DiGraph.adjacency": [[859, "digraph-adjacency"]], "DiGraph.clear": [[860, "digraph-clear"]], "DiGraph.clear_edges": [[861, "digraph-clear-edges"]], "DiGraph.copy": [[862, "digraph-copy"]], "DiGraph.degree": [[863, "digraph-degree"]], "DiGraph.edge_subgraph": [[864, "digraph-edge-subgraph"]], "DiGraph.edges": [[865, "digraph-edges"]], "DiGraph.get_edge_data": [[866, "digraph-get-edge-data"]], "DiGraph.has_edge": [[867, "digraph-has-edge"]], "DiGraph.has_node": [[868, "digraph-has-node"]], "DiGraph.in_degree": [[869, "digraph-in-degree"]], "DiGraph.in_edges": [[870, "digraph-in-edges"]], "DiGraph.nbunch_iter": [[871, "digraph-nbunch-iter"]], "DiGraph.neighbors": [[872, "digraph-neighbors"]], "DiGraph.nodes": [[873, "digraph-nodes"]], "DiGraph.number_of_edges": [[874, "digraph-number-of-edges"]], "DiGraph.number_of_nodes": [[875, "digraph-number-of-nodes"]], "DiGraph.order": [[876, "digraph-order"]], "DiGraph.out_degree": [[877, "digraph-out-degree"]], "DiGraph.out_edges": [[878, "digraph-out-edges"]], "DiGraph.pred": [[879, "digraph-pred"]], "DiGraph.predecessors": [[880, "digraph-predecessors"]], "DiGraph.remove_edge": [[881, "digraph-remove-edge"]], "DiGraph.remove_edges_from": [[882, "digraph-remove-edges-from"]], "DiGraph.remove_node": [[883, "digraph-remove-node"]], "DiGraph.remove_nodes_from": [[884, "digraph-remove-nodes-from"]], "DiGraph.reverse": [[885, "digraph-reverse"]], "DiGraph.size": [[886, "digraph-size"]], "DiGraph.subgraph": [[887, "digraph-subgraph"]], "DiGraph.succ": [[888, "digraph-succ"]], "DiGraph.successors": [[889, "digraph-successors"]], "DiGraph.to_directed": [[890, "digraph-to-directed"]], "DiGraph.to_undirected": [[891, 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"graph-has-edge"]], "Graph.has_node": [[913, "graph-has-node"]], "Graph.nbunch_iter": [[914, "graph-nbunch-iter"]], "Graph.neighbors": [[915, "graph-neighbors"]], "Graph.nodes": [[916, "graph-nodes"]], "Graph.number_of_edges": [[917, "graph-number-of-edges"]], "Graph.number_of_nodes": [[918, "graph-number-of-nodes"]], "Graph.order": [[919, "graph-order"]], "Graph.remove_edge": [[920, "graph-remove-edge"]], "Graph.remove_edges_from": [[921, "graph-remove-edges-from"]], "Graph.remove_node": [[922, "graph-remove-node"]], "Graph.remove_nodes_from": [[923, "graph-remove-nodes-from"]], "Graph.size": [[924, "graph-size"]], "Graph.subgraph": [[925, "graph-subgraph"]], "Graph.to_directed": [[926, "graph-to-directed"]], "Graph.to_undirected": [[927, "graph-to-undirected"]], "Graph.update": [[928, "graph-update"]], "MultiDiGraph.__contains__": [[929, "multidigraph-contains"]], "MultiDiGraph.__getitem__": [[930, "multidigraph-getitem"]], "MultiDiGraph.__init__": [[931, "multidigraph-init"]], "MultiDiGraph.__iter__": [[932, "multidigraph-iter"]], "MultiDiGraph.__len__": [[933, "multidigraph-len"]], "MultiDiGraph.add_edge": [[934, "multidigraph-add-edge"]], "MultiDiGraph.add_edges_from": [[935, "multidigraph-add-edges-from"]], "MultiDiGraph.add_node": [[936, "multidigraph-add-node"]], "MultiDiGraph.add_nodes_from": [[937, "multidigraph-add-nodes-from"]], "MultiDiGraph.add_weighted_edges_from": [[938, "multidigraph-add-weighted-edges-from"]], "MultiDiGraph.adj": [[939, "multidigraph-adj"]], "MultiDiGraph.adjacency": [[940, "multidigraph-adjacency"]], "MultiDiGraph.clear": [[941, "multidigraph-clear"]], "MultiDiGraph.clear_edges": [[942, "multidigraph-clear-edges"]], "MultiDiGraph.copy": [[943, "multidigraph-copy"]], "MultiDiGraph.degree": [[944, "multidigraph-degree"]], "MultiDiGraph.edge_subgraph": [[945, "multidigraph-edge-subgraph"]], "MultiDiGraph.edges": [[946, "multidigraph-edges"]], "MultiDiGraph.get_edge_data": [[947, "multidigraph-get-edge-data"]], "MultiDiGraph.has_edge": [[948, "multidigraph-has-edge"]], "MultiDiGraph.has_node": [[949, "multidigraph-has-node"]], "MultiDiGraph.in_degree": [[950, "multidigraph-in-degree"]], "MultiDiGraph.in_edges": [[951, "multidigraph-in-edges"]], "MultiDiGraph.nbunch_iter": [[952, "multidigraph-nbunch-iter"]], "MultiDiGraph.neighbors": [[953, "multidigraph-neighbors"]], "MultiDiGraph.new_edge_key": [[954, "multidigraph-new-edge-key"]], "MultiDiGraph.nodes": [[955, "multidigraph-nodes"]], "MultiDiGraph.number_of_edges": [[956, "multidigraph-number-of-edges"]], "MultiDiGraph.number_of_nodes": [[957, "multidigraph-number-of-nodes"]], "MultiDiGraph.order": [[958, "multidigraph-order"]], "MultiDiGraph.out_degree": [[959, "multidigraph-out-degree"]], "MultiDiGraph.out_edges": [[960, "multidigraph-out-edges"]], "MultiDiGraph.predecessors": [[961, "multidigraph-predecessors"]], "MultiDiGraph.remove_edge": [[962, "multidigraph-remove-edge"]], "MultiDiGraph.remove_edges_from": [[963, 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"multigraph-new-edge-key"]], "MultiGraph.nodes": [[998, "multigraph-nodes"]], "MultiGraph.number_of_edges": [[999, "multigraph-number-of-edges"]], "MultiGraph.number_of_nodes": [[1000, "multigraph-number-of-nodes"]], "MultiGraph.order": [[1001, "multigraph-order"]], "MultiGraph.remove_edge": [[1002, "multigraph-remove-edge"]], "MultiGraph.remove_edges_from": [[1003, "multigraph-remove-edges-from"]], "MultiGraph.remove_node": [[1004, "multigraph-remove-node"]], "MultiGraph.remove_nodes_from": [[1005, "multigraph-remove-nodes-from"]], "MultiGraph.size": [[1006, "multigraph-size"]], "MultiGraph.subgraph": [[1007, "multigraph-subgraph"]], "MultiGraph.to_directed": [[1008, "multigraph-to-directed"]], "MultiGraph.to_undirected": [[1009, "multigraph-to-undirected"]], "MultiGraph.update": [[1010, "multigraph-update"]], "_dispatch": [[1011, "dispatch"]], "networkx.classes.coreviews.AdjacencyView": [[1012, "networkx-classes-coreviews-adjacencyview"]], "networkx.classes.coreviews.AtlasView": 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Applying classic graph operations, such as:": [[1426, "applying-classic-graph-operations-such-as"]], "2. Using a call to one of the classic small graphs, e.g.,": [[1426, "using-a-call-to-one-of-the-classic-small-graphs-e-g"]], "3. Using a (constructive) generator for a classic graph, e.g.,": [[1426, "using-a-constructive-generator-for-a-classic-graph-e-g"]], "4. Using a stochastic graph generator, e.g,": [[1426, "using-a-stochastic-graph-generator-e-g"]], "5. Reading a graph stored in a file using common graph formats": [[1426, "reading-a-graph-stored-in-a-file-using-common-graph-formats"]], "Analyzing graphs": [[1426, "analyzing-graphs"]], "Drawing graphs": [[1426, "drawing-graphs"]]}, "indexentries": {"module": [[112, "module-networkx.algorithms.approximation"], [112, "module-networkx.algorithms.approximation.clique"], [112, "module-networkx.algorithms.approximation.clustering_coefficient"], [112, "module-networkx.algorithms.approximation.connectivity"], [112, "module-networkx.algorithms.approximation.distance_measures"], [112, "module-networkx.algorithms.approximation.dominating_set"], [112, "module-networkx.algorithms.approximation.kcomponents"], [112, "module-networkx.algorithms.approximation.matching"], [112, "module-networkx.algorithms.approximation.maxcut"], [112, "module-networkx.algorithms.approximation.ramsey"], [112, "module-networkx.algorithms.approximation.steinertree"], [112, 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"module-networkx.algorithms.approximation.clustering_coefficient"]], "networkx.algorithms.approximation.connectivity": [[112, "module-networkx.algorithms.approximation.connectivity"]], "networkx.algorithms.approximation.distance_measures": [[112, "module-networkx.algorithms.approximation.distance_measures"]], "networkx.algorithms.approximation.dominating_set": [[112, "module-networkx.algorithms.approximation.dominating_set"]], "networkx.algorithms.approximation.kcomponents": [[112, "module-networkx.algorithms.approximation.kcomponents"]], "networkx.algorithms.approximation.matching": [[112, "module-networkx.algorithms.approximation.matching"]], "networkx.algorithms.approximation.maxcut": [[112, "module-networkx.algorithms.approximation.maxcut"]], "networkx.algorithms.approximation.ramsey": [[112, "module-networkx.algorithms.approximation.ramsey"]], "networkx.algorithms.approximation.steinertree": [[112, "module-networkx.algorithms.approximation.steinertree"]], 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"module-networkx.algorithms.bipartite.covering"]], "networkx.algorithms.bipartite.edgelist": [[115, "module-networkx.algorithms.bipartite.edgelist"]], "networkx.algorithms.bipartite.generators": [[115, "module-networkx.algorithms.bipartite.generators"]], "networkx.algorithms.bipartite.matching": [[115, "module-networkx.algorithms.bipartite.matching"]], "networkx.algorithms.bipartite.matrix": [[115, "module-networkx.algorithms.bipartite.matrix"]], "networkx.algorithms.bipartite.projection": [[115, "module-networkx.algorithms.bipartite.projection"]], "networkx.algorithms.bipartite.redundancy": [[115, "module-networkx.algorithms.bipartite.redundancy"]], "networkx.algorithms.bipartite.spectral": [[115, "module-networkx.algorithms.bipartite.spectral"]], "networkx.algorithms.boundary": [[116, "module-networkx.algorithms.boundary"]], "networkx.algorithms.bridges": [[117, "module-networkx.algorithms.bridges"]], "networkx.algorithms.centrality": [[118, "module-networkx.algorithms.centrality"]], "networkx.algorithms.chains": [[119, "module-networkx.algorithms.chains"]], "networkx.algorithms.chordal": [[120, "module-networkx.algorithms.chordal"]], "networkx.algorithms.clique": [[121, "module-networkx.algorithms.clique"]], "networkx.algorithms.cluster": [[122, "module-networkx.algorithms.cluster"]], "networkx.algorithms.coloring": [[123, "module-networkx.algorithms.coloring"]], "networkx.algorithms.communicability_alg": [[124, "module-networkx.algorithms.communicability_alg"]], "networkx.algorithms.community": [[125, "module-networkx.algorithms.community"]], "networkx.algorithms.community.asyn_fluid": [[125, "module-networkx.algorithms.community.asyn_fluid"]], "networkx.algorithms.community.centrality": [[125, "module-networkx.algorithms.community.centrality"]], "networkx.algorithms.community.community_utils": [[125, "module-networkx.algorithms.community.community_utils"]], "networkx.algorithms.community.kclique": [[125, "module-networkx.algorithms.community.kclique"]], "networkx.algorithms.community.kernighan_lin": [[125, "module-networkx.algorithms.community.kernighan_lin"]], "networkx.algorithms.community.label_propagation": [[125, "module-networkx.algorithms.community.label_propagation"]], "networkx.algorithms.community.louvain": [[125, "module-networkx.algorithms.community.louvain"]], "networkx.algorithms.community.lukes": [[125, "module-networkx.algorithms.community.lukes"]], "networkx.algorithms.community.modularity_max": [[125, "module-networkx.algorithms.community.modularity_max"]], "networkx.algorithms.community.quality": [[125, "module-networkx.algorithms.community.quality"]], "networkx.algorithms.components": [[126, "module-networkx.algorithms.components"]], "networkx.algorithms.connectivity": [[127, "module-networkx.algorithms.connectivity"]], "networkx.algorithms.connectivity.connectivity": [[127, "module-networkx.algorithms.connectivity.connectivity"]], "networkx.algorithms.connectivity.cuts": [[127, "module-networkx.algorithms.connectivity.cuts"]], "networkx.algorithms.connectivity.disjoint_paths": [[127, "module-networkx.algorithms.connectivity.disjoint_paths"]], "networkx.algorithms.connectivity.edge_augmentation": [[127, "module-networkx.algorithms.connectivity.edge_augmentation"]], "networkx.algorithms.connectivity.edge_kcomponents": [[127, "module-networkx.algorithms.connectivity.edge_kcomponents"]], "networkx.algorithms.connectivity.kcomponents": [[127, "module-networkx.algorithms.connectivity.kcomponents"]], "networkx.algorithms.connectivity.kcutsets": [[127, "module-networkx.algorithms.connectivity.kcutsets"]], "networkx.algorithms.connectivity.stoerwagner": [[127, "module-networkx.algorithms.connectivity.stoerwagner"]], "networkx.algorithms.connectivity.utils": [[127, "module-networkx.algorithms.connectivity.utils"]], "networkx.algorithms.core": [[128, "module-networkx.algorithms.core"]], "networkx.algorithms.covering": [[129, "module-networkx.algorithms.covering"]], "networkx.algorithms.cuts": [[130, "module-networkx.algorithms.cuts"]], "networkx.algorithms.cycles": [[131, "module-networkx.algorithms.cycles"]], "networkx.algorithms.d_separation": [[132, "module-networkx.algorithms.d_separation"]], "networkx.algorithms.dag": [[133, "module-networkx.algorithms.dag"]], "networkx.algorithms.distance_measures": [[134, "module-networkx.algorithms.distance_measures"]], "networkx.algorithms.distance_regular": [[135, "module-networkx.algorithms.distance_regular"]], "networkx.algorithms.dominance": [[136, "module-networkx.algorithms.dominance"]], "networkx.algorithms.dominating": [[137, "module-networkx.algorithms.dominating"]], "networkx.algorithms.efficiency_measures": [[138, "module-networkx.algorithms.efficiency_measures"]], "networkx.algorithms.euler": [[139, "module-networkx.algorithms.euler"]], "networkx.algorithms.flow": [[140, "module-networkx.algorithms.flow"]], "construct() (edgecomponentauxgraph class method)": [[141, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.construct"]], "k_edge_components() (edgecomponentauxgraph method)": [[142, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.k_edge_components"]], "k_edge_subgraphs() (edgecomponentauxgraph method)": [[143, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.k_edge_subgraphs"]], "analyze_symmetry() (ismags method)": [[144, "networkx.algorithms.isomorphism.ISMAGS.analyze_symmetry"]], "find_isomorphisms() (ismags method)": [[145, "networkx.algorithms.isomorphism.ISMAGS.find_isomorphisms"]], "is_isomorphic() (ismags method)": [[146, "networkx.algorithms.isomorphism.ISMAGS.is_isomorphic"]], "isomorphisms_iter() (ismags method)": [[147, "networkx.algorithms.isomorphism.ISMAGS.isomorphisms_iter"]], "largest_common_subgraph() (ismags method)": [[148, "networkx.algorithms.isomorphism.ISMAGS.largest_common_subgraph"]], "subgraph_is_isomorphic() (ismags method)": [[149, "networkx.algorithms.isomorphism.ISMAGS.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (ismags method)": [[150, "networkx.algorithms.isomorphism.ISMAGS.subgraph_isomorphisms_iter"]], "add_edge() (planarembedding method)": [[151, "networkx.algorithms.planarity.PlanarEmbedding.add_edge"]], "add_edges_from() (planarembedding method)": [[152, "networkx.algorithms.planarity.PlanarEmbedding.add_edges_from"]], "add_half_edge_ccw() (planarembedding method)": [[153, "networkx.algorithms.planarity.PlanarEmbedding.add_half_edge_ccw"]], "add_half_edge_cw() (planarembedding method)": [[154, "networkx.algorithms.planarity.PlanarEmbedding.add_half_edge_cw"]], "add_half_edge_first() (planarembedding method)": [[155, "networkx.algorithms.planarity.PlanarEmbedding.add_half_edge_first"]], "add_node() (planarembedding method)": [[156, "networkx.algorithms.planarity.PlanarEmbedding.add_node"]], "add_nodes_from() (planarembedding method)": [[157, "networkx.algorithms.planarity.PlanarEmbedding.add_nodes_from"]], "add_weighted_edges_from() (planarembedding method)": [[158, "networkx.algorithms.planarity.PlanarEmbedding.add_weighted_edges_from"]], "adj (planarembedding property)": [[159, "networkx.algorithms.planarity.PlanarEmbedding.adj"]], "adjacency() (planarembedding method)": [[160, "networkx.algorithms.planarity.PlanarEmbedding.adjacency"]], "check_structure() (planarembedding method)": [[161, "networkx.algorithms.planarity.PlanarEmbedding.check_structure"]], "clear() (planarembedding method)": [[162, "networkx.algorithms.planarity.PlanarEmbedding.clear"]], "clear_edges() (planarembedding method)": [[163, "networkx.algorithms.planarity.PlanarEmbedding.clear_edges"]], "connect_components() (planarembedding method)": [[164, "networkx.algorithms.planarity.PlanarEmbedding.connect_components"]], "copy() (planarembedding method)": [[165, "networkx.algorithms.planarity.PlanarEmbedding.copy"]], "degree (planarembedding property)": 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"networkx.algorithms.planarity.PlanarEmbedding.in_degree"]], "in_edges (planarembedding property)": [[176, "networkx.algorithms.planarity.PlanarEmbedding.in_edges"]], "is_directed() (planarembedding method)": [[177, "networkx.algorithms.planarity.PlanarEmbedding.is_directed"]], "is_multigraph() (planarembedding method)": [[178, "networkx.algorithms.planarity.PlanarEmbedding.is_multigraph"]], "name (planarembedding property)": [[179, "networkx.algorithms.planarity.PlanarEmbedding.name"]], "nbunch_iter() (planarembedding method)": [[180, "networkx.algorithms.planarity.PlanarEmbedding.nbunch_iter"]], "neighbors() (planarembedding method)": [[181, "networkx.algorithms.planarity.PlanarEmbedding.neighbors"]], "neighbors_cw_order() (planarembedding method)": [[182, "networkx.algorithms.planarity.PlanarEmbedding.neighbors_cw_order"]], "next_face_half_edge() (planarembedding method)": [[183, "networkx.algorithms.planarity.PlanarEmbedding.next_face_half_edge"]], "nodes (planarembedding property)": [[184, "networkx.algorithms.planarity.PlanarEmbedding.nodes"]], "number_of_edges() (planarembedding method)": [[185, "networkx.algorithms.planarity.PlanarEmbedding.number_of_edges"]], "number_of_nodes() (planarembedding method)": [[186, "networkx.algorithms.planarity.PlanarEmbedding.number_of_nodes"]], "order() (planarembedding method)": [[187, "networkx.algorithms.planarity.PlanarEmbedding.order"]], "out_degree (planarembedding property)": [[188, "networkx.algorithms.planarity.PlanarEmbedding.out_degree"]], "out_edges (planarembedding property)": [[189, "networkx.algorithms.planarity.PlanarEmbedding.out_edges"]], "pred (planarembedding property)": [[190, "networkx.algorithms.planarity.PlanarEmbedding.pred"]], "predecessors() (planarembedding method)": [[191, "networkx.algorithms.planarity.PlanarEmbedding.predecessors"]], "remove_edge() (planarembedding method)": [[192, "networkx.algorithms.planarity.PlanarEmbedding.remove_edge"]], "remove_edges_from() (planarembedding method)": [[193, "networkx.algorithms.planarity.PlanarEmbedding.remove_edges_from"]], "remove_node() (planarembedding method)": [[194, "networkx.algorithms.planarity.PlanarEmbedding.remove_node"]], "remove_nodes_from() (planarembedding method)": [[195, "networkx.algorithms.planarity.PlanarEmbedding.remove_nodes_from"]], "reverse() (planarembedding method)": [[196, "networkx.algorithms.planarity.PlanarEmbedding.reverse"]], "set_data() (planarembedding method)": [[197, "networkx.algorithms.planarity.PlanarEmbedding.set_data"]], "size() (planarembedding method)": [[198, "networkx.algorithms.planarity.PlanarEmbedding.size"]], "subgraph() (planarembedding method)": [[199, "networkx.algorithms.planarity.PlanarEmbedding.subgraph"]], "succ (planarembedding property)": [[200, "networkx.algorithms.planarity.PlanarEmbedding.succ"]], "successors() (planarembedding method)": [[201, "networkx.algorithms.planarity.PlanarEmbedding.successors"]], "to_directed() (planarembedding method)": [[202, "networkx.algorithms.planarity.PlanarEmbedding.to_directed"]], "to_directed_class() (planarembedding method)": [[203, "networkx.algorithms.planarity.PlanarEmbedding.to_directed_class"]], "to_undirected() (planarembedding method)": [[204, "networkx.algorithms.planarity.PlanarEmbedding.to_undirected"]], "to_undirected_class() (planarembedding method)": [[205, "networkx.algorithms.planarity.PlanarEmbedding.to_undirected_class"]], "traverse_face() (planarembedding method)": [[206, "networkx.algorithms.planarity.PlanarEmbedding.traverse_face"]], "update() (planarembedding method)": [[207, "networkx.algorithms.planarity.PlanarEmbedding.update"]], "find_optimum() (edmonds method)": [[208, "networkx.algorithms.tree.branchings.Edmonds.find_optimum"]], "clique_removal() (in module networkx.algorithms.approximation.clique)": [[209, "networkx.algorithms.approximation.clique.clique_removal"]], "large_clique_size() (in module networkx.algorithms.approximation.clique)": [[210, "networkx.algorithms.approximation.clique.large_clique_size"]], "max_clique() (in module networkx.algorithms.approximation.clique)": [[211, "networkx.algorithms.approximation.clique.max_clique"]], "maximum_independent_set() (in module networkx.algorithms.approximation.clique)": [[212, "networkx.algorithms.approximation.clique.maximum_independent_set"]], "average_clustering() (in module networkx.algorithms.approximation.clustering_coefficient)": [[213, "networkx.algorithms.approximation.clustering_coefficient.average_clustering"]], "all_pairs_node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[214, "networkx.algorithms.approximation.connectivity.all_pairs_node_connectivity"]], "local_node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[215, "networkx.algorithms.approximation.connectivity.local_node_connectivity"]], "node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[216, "networkx.algorithms.approximation.connectivity.node_connectivity"]], "diameter() (in module networkx.algorithms.approximation.distance_measures)": [[217, "networkx.algorithms.approximation.distance_measures.diameter"]], "min_edge_dominating_set() (in module networkx.algorithms.approximation.dominating_set)": [[218, "networkx.algorithms.approximation.dominating_set.min_edge_dominating_set"]], "min_weighted_dominating_set() (in module networkx.algorithms.approximation.dominating_set)": [[219, "networkx.algorithms.approximation.dominating_set.min_weighted_dominating_set"]], "k_components() (in module networkx.algorithms.approximation.kcomponents)": [[220, "networkx.algorithms.approximation.kcomponents.k_components"]], "min_maximal_matching() (in module networkx.algorithms.approximation.matching)": [[221, "networkx.algorithms.approximation.matching.min_maximal_matching"]], "one_exchange() (in module networkx.algorithms.approximation.maxcut)": [[222, "networkx.algorithms.approximation.maxcut.one_exchange"]], "randomized_partitioning() (in module networkx.algorithms.approximation.maxcut)": [[223, "networkx.algorithms.approximation.maxcut.randomized_partitioning"]], "ramsey_r2() (in module networkx.algorithms.approximation.ramsey)": [[224, "networkx.algorithms.approximation.ramsey.ramsey_R2"]], "metric_closure() (in module networkx.algorithms.approximation.steinertree)": [[225, "networkx.algorithms.approximation.steinertree.metric_closure"]], "steiner_tree() (in module networkx.algorithms.approximation.steinertree)": [[226, "networkx.algorithms.approximation.steinertree.steiner_tree"]], "asadpour_atsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[227, "networkx.algorithms.approximation.traveling_salesman.asadpour_atsp"]], "christofides() (in module networkx.algorithms.approximation.traveling_salesman)": [[228, "networkx.algorithms.approximation.traveling_salesman.christofides"]], "greedy_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[229, "networkx.algorithms.approximation.traveling_salesman.greedy_tsp"]], "simulated_annealing_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[230, "networkx.algorithms.approximation.traveling_salesman.simulated_annealing_tsp"]], "threshold_accepting_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[231, "networkx.algorithms.approximation.traveling_salesman.threshold_accepting_tsp"]], "traveling_salesman_problem() (in module networkx.algorithms.approximation.traveling_salesman)": [[232, "networkx.algorithms.approximation.traveling_salesman.traveling_salesman_problem"]], "treewidth_min_degree() (in module networkx.algorithms.approximation.treewidth)": [[233, "networkx.algorithms.approximation.treewidth.treewidth_min_degree"]], "treewidth_min_fill_in() (in module networkx.algorithms.approximation.treewidth)": [[234, "networkx.algorithms.approximation.treewidth.treewidth_min_fill_in"]], "min_weighted_vertex_cover() (in module networkx.algorithms.approximation.vertex_cover)": [[235, "networkx.algorithms.approximation.vertex_cover.min_weighted_vertex_cover"]], "attribute_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[236, "networkx.algorithms.assortativity.attribute_assortativity_coefficient"]], "attribute_mixing_dict() (in module networkx.algorithms.assortativity)": [[237, "networkx.algorithms.assortativity.attribute_mixing_dict"]], "attribute_mixing_matrix() (in module networkx.algorithms.assortativity)": [[238, "networkx.algorithms.assortativity.attribute_mixing_matrix"]], "average_degree_connectivity() (in module networkx.algorithms.assortativity)": [[239, "networkx.algorithms.assortativity.average_degree_connectivity"]], "average_neighbor_degree() (in module networkx.algorithms.assortativity)": [[240, "networkx.algorithms.assortativity.average_neighbor_degree"]], "degree_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[241, "networkx.algorithms.assortativity.degree_assortativity_coefficient"]], "degree_mixing_dict() (in module networkx.algorithms.assortativity)": [[242, "networkx.algorithms.assortativity.degree_mixing_dict"]], "degree_mixing_matrix() (in module networkx.algorithms.assortativity)": [[243, "networkx.algorithms.assortativity.degree_mixing_matrix"]], "degree_pearson_correlation_coefficient() (in module networkx.algorithms.assortativity)": [[244, "networkx.algorithms.assortativity.degree_pearson_correlation_coefficient"]], "mixing_dict() (in module networkx.algorithms.assortativity)": [[245, "networkx.algorithms.assortativity.mixing_dict"]], "node_attribute_xy() (in module networkx.algorithms.assortativity)": [[246, "networkx.algorithms.assortativity.node_attribute_xy"]], "node_degree_xy() (in module networkx.algorithms.assortativity)": [[247, "networkx.algorithms.assortativity.node_degree_xy"]], "numeric_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[248, "networkx.algorithms.assortativity.numeric_assortativity_coefficient"]], "find_asteroidal_triple() (in module networkx.algorithms.asteroidal)": [[249, "networkx.algorithms.asteroidal.find_asteroidal_triple"]], "is_at_free() (in module networkx.algorithms.asteroidal)": [[250, "networkx.algorithms.asteroidal.is_at_free"]], "color() (in module networkx.algorithms.bipartite.basic)": [[251, "networkx.algorithms.bipartite.basic.color"]], "degrees() (in module networkx.algorithms.bipartite.basic)": [[252, "networkx.algorithms.bipartite.basic.degrees"]], "density() (in module networkx.algorithms.bipartite.basic)": [[253, "networkx.algorithms.bipartite.basic.density"]], "is_bipartite() (in module networkx.algorithms.bipartite.basic)": [[254, "networkx.algorithms.bipartite.basic.is_bipartite"]], "is_bipartite_node_set() (in module networkx.algorithms.bipartite.basic)": [[255, "networkx.algorithms.bipartite.basic.is_bipartite_node_set"]], "sets() (in module networkx.algorithms.bipartite.basic)": [[256, "networkx.algorithms.bipartite.basic.sets"]], "betweenness_centrality() (in module networkx.algorithms.bipartite.centrality)": [[257, "networkx.algorithms.bipartite.centrality.betweenness_centrality"]], "closeness_centrality() (in module networkx.algorithms.bipartite.centrality)": [[258, "networkx.algorithms.bipartite.centrality.closeness_centrality"]], "degree_centrality() (in module networkx.algorithms.bipartite.centrality)": [[259, "networkx.algorithms.bipartite.centrality.degree_centrality"]], "average_clustering() (in module networkx.algorithms.bipartite.cluster)": [[260, "networkx.algorithms.bipartite.cluster.average_clustering"]], "clustering() (in module networkx.algorithms.bipartite.cluster)": [[261, "networkx.algorithms.bipartite.cluster.clustering"]], "latapy_clustering() (in module networkx.algorithms.bipartite.cluster)": [[262, "networkx.algorithms.bipartite.cluster.latapy_clustering"]], "robins_alexander_clustering() (in module networkx.algorithms.bipartite.cluster)": [[263, "networkx.algorithms.bipartite.cluster.robins_alexander_clustering"]], "min_edge_cover() (in module networkx.algorithms.bipartite.covering)": [[264, "networkx.algorithms.bipartite.covering.min_edge_cover"]], "generate_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[265, "networkx.algorithms.bipartite.edgelist.generate_edgelist"]], "parse_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[266, "networkx.algorithms.bipartite.edgelist.parse_edgelist"]], "read_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[267, "networkx.algorithms.bipartite.edgelist.read_edgelist"]], "write_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[268, "networkx.algorithms.bipartite.edgelist.write_edgelist"]], "alternating_havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[269, "networkx.algorithms.bipartite.generators.alternating_havel_hakimi_graph"]], "complete_bipartite_graph() (in module networkx.algorithms.bipartite.generators)": [[270, "networkx.algorithms.bipartite.generators.complete_bipartite_graph"]], "configuration_model() (in module networkx.algorithms.bipartite.generators)": [[271, "networkx.algorithms.bipartite.generators.configuration_model"]], "gnmk_random_graph() (in module networkx.algorithms.bipartite.generators)": [[272, "networkx.algorithms.bipartite.generators.gnmk_random_graph"]], "havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[273, "networkx.algorithms.bipartite.generators.havel_hakimi_graph"]], "preferential_attachment_graph() (in module networkx.algorithms.bipartite.generators)": [[274, "networkx.algorithms.bipartite.generators.preferential_attachment_graph"]], "random_graph() (in module networkx.algorithms.bipartite.generators)": [[275, "networkx.algorithms.bipartite.generators.random_graph"]], "reverse_havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[276, "networkx.algorithms.bipartite.generators.reverse_havel_hakimi_graph"]], "eppstein_matching() (in module networkx.algorithms.bipartite.matching)": [[277, "networkx.algorithms.bipartite.matching.eppstein_matching"]], "hopcroft_karp_matching() (in module networkx.algorithms.bipartite.matching)": [[278, "networkx.algorithms.bipartite.matching.hopcroft_karp_matching"]], "maximum_matching() (in module networkx.algorithms.bipartite.matching)": [[279, "networkx.algorithms.bipartite.matching.maximum_matching"]], "minimum_weight_full_matching() (in module networkx.algorithms.bipartite.matching)": [[280, "networkx.algorithms.bipartite.matching.minimum_weight_full_matching"]], "to_vertex_cover() (in module networkx.algorithms.bipartite.matching)": [[281, "networkx.algorithms.bipartite.matching.to_vertex_cover"]], "biadjacency_matrix() (in module networkx.algorithms.bipartite.matrix)": [[282, "networkx.algorithms.bipartite.matrix.biadjacency_matrix"]], "from_biadjacency_matrix() (in module networkx.algorithms.bipartite.matrix)": [[283, "networkx.algorithms.bipartite.matrix.from_biadjacency_matrix"]], "collaboration_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[284, "networkx.algorithms.bipartite.projection.collaboration_weighted_projected_graph"]], "generic_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[285, "networkx.algorithms.bipartite.projection.generic_weighted_projected_graph"]], "overlap_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[286, "networkx.algorithms.bipartite.projection.overlap_weighted_projected_graph"]], "projected_graph() (in module networkx.algorithms.bipartite.projection)": [[287, "networkx.algorithms.bipartite.projection.projected_graph"]], "weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[288, "networkx.algorithms.bipartite.projection.weighted_projected_graph"]], "node_redundancy() (in module networkx.algorithms.bipartite.redundancy)": [[289, "networkx.algorithms.bipartite.redundancy.node_redundancy"]], "spectral_bipartivity() (in module networkx.algorithms.bipartite.spectral)": [[290, "networkx.algorithms.bipartite.spectral.spectral_bipartivity"]], "edge_boundary() (in module networkx.algorithms.boundary)": [[291, "networkx.algorithms.boundary.edge_boundary"]], "node_boundary() (in module networkx.algorithms.boundary)": [[292, "networkx.algorithms.boundary.node_boundary"]], "bridges() (in module networkx.algorithms.bridges)": [[293, "networkx.algorithms.bridges.bridges"]], "has_bridges() (in module networkx.algorithms.bridges)": [[294, "networkx.algorithms.bridges.has_bridges"]], "local_bridges() (in module networkx.algorithms.bridges)": [[295, "networkx.algorithms.bridges.local_bridges"]], "approximate_current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[296, "networkx.algorithms.centrality.approximate_current_flow_betweenness_centrality"]], "betweenness_centrality() (in module networkx.algorithms.centrality)": [[297, "networkx.algorithms.centrality.betweenness_centrality"]], "betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[298, "networkx.algorithms.centrality.betweenness_centrality_subset"]], "closeness_centrality() (in module networkx.algorithms.centrality)": [[299, "networkx.algorithms.centrality.closeness_centrality"]], "communicability_betweenness_centrality() (in module networkx.algorithms.centrality)": [[300, "networkx.algorithms.centrality.communicability_betweenness_centrality"]], "current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[301, "networkx.algorithms.centrality.current_flow_betweenness_centrality"]], "current_flow_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[302, "networkx.algorithms.centrality.current_flow_betweenness_centrality_subset"]], "current_flow_closeness_centrality() (in module networkx.algorithms.centrality)": [[303, "networkx.algorithms.centrality.current_flow_closeness_centrality"]], "degree_centrality() (in module networkx.algorithms.centrality)": [[304, "networkx.algorithms.centrality.degree_centrality"]], "dispersion() (in module networkx.algorithms.centrality)": [[305, "networkx.algorithms.centrality.dispersion"]], "edge_betweenness_centrality() (in module networkx.algorithms.centrality)": [[306, "networkx.algorithms.centrality.edge_betweenness_centrality"]], "edge_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[307, "networkx.algorithms.centrality.edge_betweenness_centrality_subset"]], "edge_current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[308, "networkx.algorithms.centrality.edge_current_flow_betweenness_centrality"]], "edge_current_flow_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[309, "networkx.algorithms.centrality.edge_current_flow_betweenness_centrality_subset"]], "edge_load_centrality() (in module networkx.algorithms.centrality)": [[310, "networkx.algorithms.centrality.edge_load_centrality"]], "eigenvector_centrality() (in module networkx.algorithms.centrality)": [[311, "networkx.algorithms.centrality.eigenvector_centrality"]], "eigenvector_centrality_numpy() (in module networkx.algorithms.centrality)": [[312, "networkx.algorithms.centrality.eigenvector_centrality_numpy"]], "estrada_index() (in module networkx.algorithms.centrality)": [[313, "networkx.algorithms.centrality.estrada_index"]], "global_reaching_centrality() (in module networkx.algorithms.centrality)": [[314, "networkx.algorithms.centrality.global_reaching_centrality"]], "group_betweenness_centrality() (in module networkx.algorithms.centrality)": [[315, "networkx.algorithms.centrality.group_betweenness_centrality"]], "group_closeness_centrality() (in module networkx.algorithms.centrality)": [[316, "networkx.algorithms.centrality.group_closeness_centrality"]], "group_degree_centrality() (in module networkx.algorithms.centrality)": [[317, "networkx.algorithms.centrality.group_degree_centrality"]], "group_in_degree_centrality() (in module networkx.algorithms.centrality)": [[318, "networkx.algorithms.centrality.group_in_degree_centrality"]], "group_out_degree_centrality() (in module networkx.algorithms.centrality)": [[319, "networkx.algorithms.centrality.group_out_degree_centrality"]], "harmonic_centrality() (in module networkx.algorithms.centrality)": [[320, "networkx.algorithms.centrality.harmonic_centrality"]], "in_degree_centrality() (in module networkx.algorithms.centrality)": [[321, "networkx.algorithms.centrality.in_degree_centrality"]], "incremental_closeness_centrality() (in module networkx.algorithms.centrality)": [[322, "networkx.algorithms.centrality.incremental_closeness_centrality"]], "information_centrality() (in module networkx.algorithms.centrality)": [[323, "networkx.algorithms.centrality.information_centrality"]], "katz_centrality() (in module networkx.algorithms.centrality)": [[324, "networkx.algorithms.centrality.katz_centrality"]], "katz_centrality_numpy() (in module networkx.algorithms.centrality)": [[325, "networkx.algorithms.centrality.katz_centrality_numpy"]], "load_centrality() (in module networkx.algorithms.centrality)": [[326, "networkx.algorithms.centrality.load_centrality"]], "local_reaching_centrality() (in module networkx.algorithms.centrality)": [[327, "networkx.algorithms.centrality.local_reaching_centrality"]], "out_degree_centrality() (in module networkx.algorithms.centrality)": [[328, "networkx.algorithms.centrality.out_degree_centrality"]], "percolation_centrality() (in module networkx.algorithms.centrality)": [[329, "networkx.algorithms.centrality.percolation_centrality"]], "prominent_group() (in module networkx.algorithms.centrality)": [[330, "networkx.algorithms.centrality.prominent_group"]], "second_order_centrality() (in module networkx.algorithms.centrality)": [[331, "networkx.algorithms.centrality.second_order_centrality"]], "subgraph_centrality() (in module networkx.algorithms.centrality)": [[332, "networkx.algorithms.centrality.subgraph_centrality"]], "subgraph_centrality_exp() (in module networkx.algorithms.centrality)": [[333, "networkx.algorithms.centrality.subgraph_centrality_exp"]], "trophic_differences() (in module networkx.algorithms.centrality)": [[334, "networkx.algorithms.centrality.trophic_differences"]], "trophic_incoherence_parameter() (in module networkx.algorithms.centrality)": [[335, "networkx.algorithms.centrality.trophic_incoherence_parameter"]], "trophic_levels() (in module networkx.algorithms.centrality)": [[336, "networkx.algorithms.centrality.trophic_levels"]], "voterank() (in module networkx.algorithms.centrality)": [[337, "networkx.algorithms.centrality.voterank"]], "chain_decomposition() (in module networkx.algorithms.chains)": [[338, "networkx.algorithms.chains.chain_decomposition"]], "chordal_graph_cliques() (in module networkx.algorithms.chordal)": [[339, "networkx.algorithms.chordal.chordal_graph_cliques"]], "chordal_graph_treewidth() (in module networkx.algorithms.chordal)": [[340, "networkx.algorithms.chordal.chordal_graph_treewidth"]], "complete_to_chordal_graph() (in module networkx.algorithms.chordal)": [[341, "networkx.algorithms.chordal.complete_to_chordal_graph"]], "find_induced_nodes() (in module networkx.algorithms.chordal)": [[342, "networkx.algorithms.chordal.find_induced_nodes"]], "is_chordal() (in module networkx.algorithms.chordal)": [[343, "networkx.algorithms.chordal.is_chordal"]], "cliques_containing_node() (in module networkx.algorithms.clique)": [[344, "networkx.algorithms.clique.cliques_containing_node"]], "enumerate_all_cliques() (in module networkx.algorithms.clique)": [[345, "networkx.algorithms.clique.enumerate_all_cliques"]], "find_cliques() (in module networkx.algorithms.clique)": [[346, "networkx.algorithms.clique.find_cliques"]], "find_cliques_recursive() (in module networkx.algorithms.clique)": [[347, "networkx.algorithms.clique.find_cliques_recursive"]], "graph_clique_number() (in module networkx.algorithms.clique)": [[348, "networkx.algorithms.clique.graph_clique_number"]], "graph_number_of_cliques() (in module networkx.algorithms.clique)": [[349, "networkx.algorithms.clique.graph_number_of_cliques"]], "make_clique_bipartite() (in module networkx.algorithms.clique)": [[350, "networkx.algorithms.clique.make_clique_bipartite"]], "make_max_clique_graph() (in module networkx.algorithms.clique)": [[351, "networkx.algorithms.clique.make_max_clique_graph"]], "max_weight_clique() (in module networkx.algorithms.clique)": [[352, "networkx.algorithms.clique.max_weight_clique"]], "node_clique_number() (in module networkx.algorithms.clique)": [[353, "networkx.algorithms.clique.node_clique_number"]], "number_of_cliques() (in module networkx.algorithms.clique)": [[354, "networkx.algorithms.clique.number_of_cliques"]], "average_clustering() (in module networkx.algorithms.cluster)": [[355, "networkx.algorithms.cluster.average_clustering"]], "clustering() (in module networkx.algorithms.cluster)": [[356, "networkx.algorithms.cluster.clustering"]], "generalized_degree() (in module networkx.algorithms.cluster)": [[357, "networkx.algorithms.cluster.generalized_degree"]], "square_clustering() (in module networkx.algorithms.cluster)": [[358, "networkx.algorithms.cluster.square_clustering"]], "transitivity() (in module networkx.algorithms.cluster)": [[359, "networkx.algorithms.cluster.transitivity"]], "triangles() (in module networkx.algorithms.cluster)": [[360, "networkx.algorithms.cluster.triangles"]], "equitable_color() (in module networkx.algorithms.coloring)": [[361, "networkx.algorithms.coloring.equitable_color"]], "greedy_color() (in module networkx.algorithms.coloring)": [[362, "networkx.algorithms.coloring.greedy_color"]], "strategy_connected_sequential() (in module networkx.algorithms.coloring)": [[363, "networkx.algorithms.coloring.strategy_connected_sequential"]], "strategy_connected_sequential_bfs() (in module networkx.algorithms.coloring)": [[364, "networkx.algorithms.coloring.strategy_connected_sequential_bfs"]], "strategy_connected_sequential_dfs() (in module networkx.algorithms.coloring)": [[365, "networkx.algorithms.coloring.strategy_connected_sequential_dfs"]], "strategy_independent_set() (in module networkx.algorithms.coloring)": [[366, "networkx.algorithms.coloring.strategy_independent_set"]], "strategy_largest_first() (in module networkx.algorithms.coloring)": [[367, "networkx.algorithms.coloring.strategy_largest_first"]], "strategy_random_sequential() (in module networkx.algorithms.coloring)": [[368, "networkx.algorithms.coloring.strategy_random_sequential"]], "strategy_saturation_largest_first() (in module networkx.algorithms.coloring)": [[369, "networkx.algorithms.coloring.strategy_saturation_largest_first"]], "strategy_smallest_last() (in module networkx.algorithms.coloring)": [[370, "networkx.algorithms.coloring.strategy_smallest_last"]], "communicability() (in module networkx.algorithms.communicability_alg)": [[371, "networkx.algorithms.communicability_alg.communicability"]], "communicability_exp() (in module networkx.algorithms.communicability_alg)": [[372, "networkx.algorithms.communicability_alg.communicability_exp"]], "asyn_fluidc() (in module networkx.algorithms.community.asyn_fluid)": [[373, "networkx.algorithms.community.asyn_fluid.asyn_fluidc"]], "girvan_newman() (in module networkx.algorithms.community.centrality)": [[374, "networkx.algorithms.community.centrality.girvan_newman"]], "is_partition() (in module networkx.algorithms.community.community_utils)": [[375, "networkx.algorithms.community.community_utils.is_partition"]], "k_clique_communities() (in module networkx.algorithms.community.kclique)": [[376, "networkx.algorithms.community.kclique.k_clique_communities"]], "kernighan_lin_bisection() (in module networkx.algorithms.community.kernighan_lin)": [[377, "networkx.algorithms.community.kernighan_lin.kernighan_lin_bisection"]], "asyn_lpa_communities() (in module networkx.algorithms.community.label_propagation)": [[378, "networkx.algorithms.community.label_propagation.asyn_lpa_communities"]], "label_propagation_communities() (in module networkx.algorithms.community.label_propagation)": [[379, "networkx.algorithms.community.label_propagation.label_propagation_communities"]], "louvain_communities() (in module networkx.algorithms.community.louvain)": [[380, "networkx.algorithms.community.louvain.louvain_communities"]], "louvain_partitions() (in module networkx.algorithms.community.louvain)": [[381, "networkx.algorithms.community.louvain.louvain_partitions"]], "lukes_partitioning() (in module networkx.algorithms.community.lukes)": [[382, "networkx.algorithms.community.lukes.lukes_partitioning"]], "greedy_modularity_communities() (in module networkx.algorithms.community.modularity_max)": [[383, "networkx.algorithms.community.modularity_max.greedy_modularity_communities"]], "naive_greedy_modularity_communities() (in module networkx.algorithms.community.modularity_max)": [[384, "networkx.algorithms.community.modularity_max.naive_greedy_modularity_communities"]], "modularity() (in module networkx.algorithms.community.quality)": [[385, "networkx.algorithms.community.quality.modularity"]], "partition_quality() (in module networkx.algorithms.community.quality)": [[386, "networkx.algorithms.community.quality.partition_quality"]], "articulation_points() (in module networkx.algorithms.components)": [[387, "networkx.algorithms.components.articulation_points"]], "attracting_components() (in module networkx.algorithms.components)": [[388, "networkx.algorithms.components.attracting_components"]], "biconnected_component_edges() (in module networkx.algorithms.components)": [[389, "networkx.algorithms.components.biconnected_component_edges"]], "biconnected_components() (in module networkx.algorithms.components)": [[390, "networkx.algorithms.components.biconnected_components"]], "condensation() (in module networkx.algorithms.components)": [[391, "networkx.algorithms.components.condensation"]], "connected_components() (in module networkx.algorithms.components)": [[392, "networkx.algorithms.components.connected_components"]], "is_attracting_component() (in module networkx.algorithms.components)": [[393, "networkx.algorithms.components.is_attracting_component"]], "is_biconnected() (in module networkx.algorithms.components)": [[394, "networkx.algorithms.components.is_biconnected"]], "is_connected() (in module networkx.algorithms.components)": [[395, "networkx.algorithms.components.is_connected"]], "is_semiconnected() (in module networkx.algorithms.components)": [[396, "networkx.algorithms.components.is_semiconnected"]], "is_strongly_connected() (in module networkx.algorithms.components)": [[397, "networkx.algorithms.components.is_strongly_connected"]], "is_weakly_connected() (in module networkx.algorithms.components)": [[398, "networkx.algorithms.components.is_weakly_connected"]], "kosaraju_strongly_connected_components() (in module networkx.algorithms.components)": [[399, "networkx.algorithms.components.kosaraju_strongly_connected_components"]], "node_connected_component() (in module networkx.algorithms.components)": [[400, "networkx.algorithms.components.node_connected_component"]], "number_attracting_components() (in module networkx.algorithms.components)": [[401, "networkx.algorithms.components.number_attracting_components"]], "number_connected_components() (in module networkx.algorithms.components)": [[402, "networkx.algorithms.components.number_connected_components"]], "number_strongly_connected_components() (in module networkx.algorithms.components)": [[403, "networkx.algorithms.components.number_strongly_connected_components"]], "number_weakly_connected_components() (in module networkx.algorithms.components)": [[404, "networkx.algorithms.components.number_weakly_connected_components"]], "strongly_connected_components() (in module networkx.algorithms.components)": [[405, "networkx.algorithms.components.strongly_connected_components"]], "strongly_connected_components_recursive() (in module networkx.algorithms.components)": [[406, "networkx.algorithms.components.strongly_connected_components_recursive"]], "weakly_connected_components() (in module networkx.algorithms.components)": [[407, "networkx.algorithms.components.weakly_connected_components"]], "all_pairs_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[408, "networkx.algorithms.connectivity.connectivity.all_pairs_node_connectivity"]], "average_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[409, "networkx.algorithms.connectivity.connectivity.average_node_connectivity"]], "edge_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[410, "networkx.algorithms.connectivity.connectivity.edge_connectivity"]], "local_edge_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[411, "networkx.algorithms.connectivity.connectivity.local_edge_connectivity"]], "local_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[412, "networkx.algorithms.connectivity.connectivity.local_node_connectivity"]], "node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[413, "networkx.algorithms.connectivity.connectivity.node_connectivity"]], "minimum_edge_cut() (in module networkx.algorithms.connectivity.cuts)": [[414, "networkx.algorithms.connectivity.cuts.minimum_edge_cut"]], "minimum_node_cut() (in module networkx.algorithms.connectivity.cuts)": [[415, "networkx.algorithms.connectivity.cuts.minimum_node_cut"]], "minimum_st_edge_cut() (in module networkx.algorithms.connectivity.cuts)": [[416, "networkx.algorithms.connectivity.cuts.minimum_st_edge_cut"]], "minimum_st_node_cut() (in module networkx.algorithms.connectivity.cuts)": [[417, "networkx.algorithms.connectivity.cuts.minimum_st_node_cut"]], "edge_disjoint_paths() (in module networkx.algorithms.connectivity.disjoint_paths)": [[418, "networkx.algorithms.connectivity.disjoint_paths.edge_disjoint_paths"]], "node_disjoint_paths() (in module networkx.algorithms.connectivity.disjoint_paths)": [[419, "networkx.algorithms.connectivity.disjoint_paths.node_disjoint_paths"]], "is_k_edge_connected() (in module networkx.algorithms.connectivity.edge_augmentation)": [[420, "networkx.algorithms.connectivity.edge_augmentation.is_k_edge_connected"]], "is_locally_k_edge_connected() (in module networkx.algorithms.connectivity.edge_augmentation)": [[421, "networkx.algorithms.connectivity.edge_augmentation.is_locally_k_edge_connected"]], "k_edge_augmentation() (in module networkx.algorithms.connectivity.edge_augmentation)": [[422, "networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation"]], "edgecomponentauxgraph (class in networkx.algorithms.connectivity.edge_kcomponents)": [[423, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph"]], "__init__() (edgecomponentauxgraph method)": [[423, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.__init__"]], "bridge_components() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[424, "networkx.algorithms.connectivity.edge_kcomponents.bridge_components"]], "k_edge_components() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[425, "networkx.algorithms.connectivity.edge_kcomponents.k_edge_components"]], "k_edge_subgraphs() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[426, "networkx.algorithms.connectivity.edge_kcomponents.k_edge_subgraphs"]], "k_components() (in module networkx.algorithms.connectivity.kcomponents)": [[427, "networkx.algorithms.connectivity.kcomponents.k_components"]], "all_node_cuts() (in module networkx.algorithms.connectivity.kcutsets)": [[428, "networkx.algorithms.connectivity.kcutsets.all_node_cuts"]], "stoer_wagner() (in module networkx.algorithms.connectivity.stoerwagner)": [[429, "networkx.algorithms.connectivity.stoerwagner.stoer_wagner"]], "build_auxiliary_edge_connectivity() (in module networkx.algorithms.connectivity.utils)": [[430, "networkx.algorithms.connectivity.utils.build_auxiliary_edge_connectivity"]], "build_auxiliary_node_connectivity() (in module networkx.algorithms.connectivity.utils)": [[431, "networkx.algorithms.connectivity.utils.build_auxiliary_node_connectivity"]], "core_number() (in module networkx.algorithms.core)": [[432, "networkx.algorithms.core.core_number"]], "k_core() (in module networkx.algorithms.core)": [[433, "networkx.algorithms.core.k_core"]], "k_corona() (in module networkx.algorithms.core)": [[434, "networkx.algorithms.core.k_corona"]], "k_crust() (in module networkx.algorithms.core)": [[435, "networkx.algorithms.core.k_crust"]], "k_shell() (in module networkx.algorithms.core)": [[436, "networkx.algorithms.core.k_shell"]], "k_truss() (in module networkx.algorithms.core)": [[437, "networkx.algorithms.core.k_truss"]], "onion_layers() (in module networkx.algorithms.core)": [[438, "networkx.algorithms.core.onion_layers"]], "is_edge_cover() (in module networkx.algorithms.covering)": [[439, "networkx.algorithms.covering.is_edge_cover"]], "min_edge_cover() (in module networkx.algorithms.covering)": [[440, "networkx.algorithms.covering.min_edge_cover"]], "boundary_expansion() (in module networkx.algorithms.cuts)": [[441, "networkx.algorithms.cuts.boundary_expansion"]], "conductance() (in module networkx.algorithms.cuts)": [[442, "networkx.algorithms.cuts.conductance"]], "cut_size() (in module networkx.algorithms.cuts)": [[443, "networkx.algorithms.cuts.cut_size"]], "edge_expansion() (in module networkx.algorithms.cuts)": [[444, "networkx.algorithms.cuts.edge_expansion"]], "mixing_expansion() (in module networkx.algorithms.cuts)": [[445, "networkx.algorithms.cuts.mixing_expansion"]], "node_expansion() (in module networkx.algorithms.cuts)": [[446, "networkx.algorithms.cuts.node_expansion"]], "normalized_cut_size() (in module networkx.algorithms.cuts)": [[447, "networkx.algorithms.cuts.normalized_cut_size"]], "volume() (in module networkx.algorithms.cuts)": [[448, "networkx.algorithms.cuts.volume"]], "cycle_basis() (in module networkx.algorithms.cycles)": [[449, "networkx.algorithms.cycles.cycle_basis"]], "find_cycle() (in module networkx.algorithms.cycles)": [[450, "networkx.algorithms.cycles.find_cycle"]], "minimum_cycle_basis() (in module networkx.algorithms.cycles)": [[451, "networkx.algorithms.cycles.minimum_cycle_basis"]], "recursive_simple_cycles() (in module networkx.algorithms.cycles)": [[452, "networkx.algorithms.cycles.recursive_simple_cycles"]], "simple_cycles() (in module networkx.algorithms.cycles)": [[453, "networkx.algorithms.cycles.simple_cycles"]], "d_separated() (in module networkx.algorithms.d_separation)": [[454, "networkx.algorithms.d_separation.d_separated"]], "all_topological_sorts() (in module networkx.algorithms.dag)": [[455, "networkx.algorithms.dag.all_topological_sorts"]], "ancestors() (in module networkx.algorithms.dag)": [[456, "networkx.algorithms.dag.ancestors"]], "antichains() (in module networkx.algorithms.dag)": [[457, "networkx.algorithms.dag.antichains"]], "dag_longest_path() (in module networkx.algorithms.dag)": [[458, "networkx.algorithms.dag.dag_longest_path"]], "dag_longest_path_length() (in module networkx.algorithms.dag)": [[459, "networkx.algorithms.dag.dag_longest_path_length"]], "dag_to_branching() (in module networkx.algorithms.dag)": [[460, "networkx.algorithms.dag.dag_to_branching"]], "descendants() (in module networkx.algorithms.dag)": [[461, "networkx.algorithms.dag.descendants"]], "is_aperiodic() (in module networkx.algorithms.dag)": [[462, "networkx.algorithms.dag.is_aperiodic"]], "is_directed_acyclic_graph() (in module networkx.algorithms.dag)": [[463, "networkx.algorithms.dag.is_directed_acyclic_graph"]], "lexicographical_topological_sort() (in module networkx.algorithms.dag)": [[464, "networkx.algorithms.dag.lexicographical_topological_sort"]], "topological_generations() (in module networkx.algorithms.dag)": [[465, "networkx.algorithms.dag.topological_generations"]], "topological_sort() (in module networkx.algorithms.dag)": [[466, "networkx.algorithms.dag.topological_sort"]], "transitive_closure() (in module networkx.algorithms.dag)": [[467, "networkx.algorithms.dag.transitive_closure"]], "transitive_closure_dag() (in module networkx.algorithms.dag)": [[468, "networkx.algorithms.dag.transitive_closure_dag"]], "transitive_reduction() (in module networkx.algorithms.dag)": [[469, "networkx.algorithms.dag.transitive_reduction"]], "barycenter() (in module networkx.algorithms.distance_measures)": [[470, "networkx.algorithms.distance_measures.barycenter"]], "center() (in module networkx.algorithms.distance_measures)": [[471, "networkx.algorithms.distance_measures.center"]], "diameter() (in module networkx.algorithms.distance_measures)": [[472, "networkx.algorithms.distance_measures.diameter"]], "eccentricity() (in module networkx.algorithms.distance_measures)": [[473, "networkx.algorithms.distance_measures.eccentricity"]], "periphery() (in module networkx.algorithms.distance_measures)": [[474, "networkx.algorithms.distance_measures.periphery"]], "radius() (in module networkx.algorithms.distance_measures)": [[475, "networkx.algorithms.distance_measures.radius"]], "resistance_distance() (in module networkx.algorithms.distance_measures)": [[476, "networkx.algorithms.distance_measures.resistance_distance"]], "global_parameters() (in module networkx.algorithms.distance_regular)": [[477, "networkx.algorithms.distance_regular.global_parameters"]], "intersection_array() (in module networkx.algorithms.distance_regular)": [[478, "networkx.algorithms.distance_regular.intersection_array"]], "is_distance_regular() (in module networkx.algorithms.distance_regular)": [[479, "networkx.algorithms.distance_regular.is_distance_regular"]], "is_strongly_regular() (in module networkx.algorithms.distance_regular)": [[480, "networkx.algorithms.distance_regular.is_strongly_regular"]], "dominance_frontiers() (in module networkx.algorithms.dominance)": [[481, "networkx.algorithms.dominance.dominance_frontiers"]], "immediate_dominators() (in module networkx.algorithms.dominance)": [[482, "networkx.algorithms.dominance.immediate_dominators"]], "dominating_set() (in module networkx.algorithms.dominating)": [[483, "networkx.algorithms.dominating.dominating_set"]], "is_dominating_set() (in module networkx.algorithms.dominating)": [[484, "networkx.algorithms.dominating.is_dominating_set"]], "efficiency() (in module networkx.algorithms.efficiency_measures)": [[485, "networkx.algorithms.efficiency_measures.efficiency"]], "global_efficiency() (in module networkx.algorithms.efficiency_measures)": [[486, "networkx.algorithms.efficiency_measures.global_efficiency"]], "local_efficiency() (in module networkx.algorithms.efficiency_measures)": [[487, "networkx.algorithms.efficiency_measures.local_efficiency"]], "eulerian_circuit() (in module networkx.algorithms.euler)": [[488, "networkx.algorithms.euler.eulerian_circuit"]], "eulerian_path() (in module networkx.algorithms.euler)": [[489, "networkx.algorithms.euler.eulerian_path"]], "eulerize() (in module networkx.algorithms.euler)": [[490, "networkx.algorithms.euler.eulerize"]], "has_eulerian_path() (in module networkx.algorithms.euler)": [[491, "networkx.algorithms.euler.has_eulerian_path"]], "is_eulerian() (in module networkx.algorithms.euler)": [[492, "networkx.algorithms.euler.is_eulerian"]], "is_semieulerian() (in module networkx.algorithms.euler)": [[493, "networkx.algorithms.euler.is_semieulerian"]], "boykov_kolmogorov() (in module networkx.algorithms.flow)": [[494, "networkx.algorithms.flow.boykov_kolmogorov"]], "build_residual_network() (in module networkx.algorithms.flow)": [[495, "networkx.algorithms.flow.build_residual_network"]], "capacity_scaling() (in module networkx.algorithms.flow)": [[496, "networkx.algorithms.flow.capacity_scaling"]], "cost_of_flow() (in module networkx.algorithms.flow)": [[497, "networkx.algorithms.flow.cost_of_flow"]], "dinitz() (in module networkx.algorithms.flow)": [[498, "networkx.algorithms.flow.dinitz"]], "edmonds_karp() (in module networkx.algorithms.flow)": [[499, "networkx.algorithms.flow.edmonds_karp"]], "gomory_hu_tree() (in module networkx.algorithms.flow)": [[500, "networkx.algorithms.flow.gomory_hu_tree"]], "max_flow_min_cost() (in module networkx.algorithms.flow)": [[501, "networkx.algorithms.flow.max_flow_min_cost"]], "maximum_flow() (in module networkx.algorithms.flow)": [[502, "networkx.algorithms.flow.maximum_flow"]], "maximum_flow_value() (in module networkx.algorithms.flow)": [[503, "networkx.algorithms.flow.maximum_flow_value"]], "min_cost_flow() (in module networkx.algorithms.flow)": [[504, "networkx.algorithms.flow.min_cost_flow"]], "min_cost_flow_cost() (in module networkx.algorithms.flow)": [[505, "networkx.algorithms.flow.min_cost_flow_cost"]], "minimum_cut() (in module networkx.algorithms.flow)": [[506, "networkx.algorithms.flow.minimum_cut"]], "minimum_cut_value() (in module networkx.algorithms.flow)": [[507, "networkx.algorithms.flow.minimum_cut_value"]], "network_simplex() (in module networkx.algorithms.flow)": [[508, "networkx.algorithms.flow.network_simplex"]], "preflow_push() (in module networkx.algorithms.flow)": [[509, "networkx.algorithms.flow.preflow_push"]], "shortest_augmenting_path() (in module networkx.algorithms.flow)": [[510, "networkx.algorithms.flow.shortest_augmenting_path"]], "weisfeiler_lehman_graph_hash() (in module networkx.algorithms.graph_hashing)": [[511, "networkx.algorithms.graph_hashing.weisfeiler_lehman_graph_hash"]], "weisfeiler_lehman_subgraph_hashes() (in module networkx.algorithms.graph_hashing)": [[512, "networkx.algorithms.graph_hashing.weisfeiler_lehman_subgraph_hashes"]], "is_digraphical() (in module networkx.algorithms.graphical)": [[513, "networkx.algorithms.graphical.is_digraphical"]], "is_graphical() (in module networkx.algorithms.graphical)": [[514, "networkx.algorithms.graphical.is_graphical"]], "is_multigraphical() (in module networkx.algorithms.graphical)": [[515, "networkx.algorithms.graphical.is_multigraphical"]], "is_pseudographical() (in module networkx.algorithms.graphical)": [[516, "networkx.algorithms.graphical.is_pseudographical"]], "is_valid_degree_sequence_erdos_gallai() (in module networkx.algorithms.graphical)": [[517, "networkx.algorithms.graphical.is_valid_degree_sequence_erdos_gallai"]], "is_valid_degree_sequence_havel_hakimi() (in module networkx.algorithms.graphical)": [[518, "networkx.algorithms.graphical.is_valid_degree_sequence_havel_hakimi"]], "flow_hierarchy() (in module networkx.algorithms.hierarchy)": [[519, "networkx.algorithms.hierarchy.flow_hierarchy"]], "is_kl_connected() (in module networkx.algorithms.hybrid)": [[520, "networkx.algorithms.hybrid.is_kl_connected"]], "kl_connected_subgraph() (in module networkx.algorithms.hybrid)": [[521, "networkx.algorithms.hybrid.kl_connected_subgraph"]], "is_isolate() (in module networkx.algorithms.isolate)": [[522, "networkx.algorithms.isolate.is_isolate"]], "isolates() (in module networkx.algorithms.isolate)": [[523, "networkx.algorithms.isolate.isolates"]], "number_of_isolates() (in module networkx.algorithms.isolate)": [[524, "networkx.algorithms.isolate.number_of_isolates"]], "__init__() (digraphmatcher method)": [[525, "networkx.algorithms.isomorphism.DiGraphMatcher.__init__"]], "candidate_pairs_iter() (digraphmatcher method)": [[526, "networkx.algorithms.isomorphism.DiGraphMatcher.candidate_pairs_iter"]], "initialize() (digraphmatcher method)": [[527, "networkx.algorithms.isomorphism.DiGraphMatcher.initialize"]], "is_isomorphic() (digraphmatcher method)": [[528, "networkx.algorithms.isomorphism.DiGraphMatcher.is_isomorphic"]], "isomorphisms_iter() (digraphmatcher method)": [[529, "networkx.algorithms.isomorphism.DiGraphMatcher.isomorphisms_iter"]], "match() (digraphmatcher method)": [[530, "networkx.algorithms.isomorphism.DiGraphMatcher.match"]], "semantic_feasibility() (digraphmatcher method)": [[531, "networkx.algorithms.isomorphism.DiGraphMatcher.semantic_feasibility"]], "subgraph_is_isomorphic() (digraphmatcher method)": [[532, "networkx.algorithms.isomorphism.DiGraphMatcher.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (digraphmatcher method)": [[533, "networkx.algorithms.isomorphism.DiGraphMatcher.subgraph_isomorphisms_iter"]], "syntactic_feasibility() (digraphmatcher method)": [[534, "networkx.algorithms.isomorphism.DiGraphMatcher.syntactic_feasibility"]], "__init__() (graphmatcher method)": [[535, "networkx.algorithms.isomorphism.GraphMatcher.__init__"]], "candidate_pairs_iter() (graphmatcher method)": [[536, "networkx.algorithms.isomorphism.GraphMatcher.candidate_pairs_iter"]], "initialize() (graphmatcher method)": [[537, "networkx.algorithms.isomorphism.GraphMatcher.initialize"]], "is_isomorphic() (graphmatcher method)": [[538, "networkx.algorithms.isomorphism.GraphMatcher.is_isomorphic"]], "isomorphisms_iter() (graphmatcher method)": [[539, "networkx.algorithms.isomorphism.GraphMatcher.isomorphisms_iter"]], "match() (graphmatcher method)": [[540, "networkx.algorithms.isomorphism.GraphMatcher.match"]], "semantic_feasibility() (graphmatcher method)": [[541, "networkx.algorithms.isomorphism.GraphMatcher.semantic_feasibility"]], "subgraph_is_isomorphic() (graphmatcher method)": [[542, "networkx.algorithms.isomorphism.GraphMatcher.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (graphmatcher method)": [[543, "networkx.algorithms.isomorphism.GraphMatcher.subgraph_isomorphisms_iter"]], "syntactic_feasibility() (graphmatcher method)": [[544, "networkx.algorithms.isomorphism.GraphMatcher.syntactic_feasibility"]], "ismags (class in networkx.algorithms.isomorphism)": [[545, "networkx.algorithms.isomorphism.ISMAGS"]], "__init__() (ismags method)": [[545, "networkx.algorithms.isomorphism.ISMAGS.__init__"]], "categorical_edge_match() (in module networkx.algorithms.isomorphism)": [[546, "networkx.algorithms.isomorphism.categorical_edge_match"]], "categorical_multiedge_match() (in module networkx.algorithms.isomorphism)": [[547, "networkx.algorithms.isomorphism.categorical_multiedge_match"]], "categorical_node_match() (in module networkx.algorithms.isomorphism)": [[548, "networkx.algorithms.isomorphism.categorical_node_match"]], "could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[549, "networkx.algorithms.isomorphism.could_be_isomorphic"]], "fast_could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[550, "networkx.algorithms.isomorphism.fast_could_be_isomorphic"]], "faster_could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[551, "networkx.algorithms.isomorphism.faster_could_be_isomorphic"]], "generic_edge_match() (in module networkx.algorithms.isomorphism)": [[552, "networkx.algorithms.isomorphism.generic_edge_match"]], "generic_multiedge_match() (in module networkx.algorithms.isomorphism)": [[553, "networkx.algorithms.isomorphism.generic_multiedge_match"]], "generic_node_match() (in module networkx.algorithms.isomorphism)": [[554, "networkx.algorithms.isomorphism.generic_node_match"]], "is_isomorphic() (in module networkx.algorithms.isomorphism)": [[555, "networkx.algorithms.isomorphism.is_isomorphic"]], "numerical_edge_match() (in module networkx.algorithms.isomorphism)": [[556, "networkx.algorithms.isomorphism.numerical_edge_match"]], "numerical_multiedge_match() (in module networkx.algorithms.isomorphism)": [[557, "networkx.algorithms.isomorphism.numerical_multiedge_match"]], "numerical_node_match() (in module networkx.algorithms.isomorphism)": [[558, "networkx.algorithms.isomorphism.numerical_node_match"]], "rooted_tree_isomorphism() (in module networkx.algorithms.isomorphism.tree_isomorphism)": [[559, "networkx.algorithms.isomorphism.tree_isomorphism.rooted_tree_isomorphism"]], "tree_isomorphism() (in module networkx.algorithms.isomorphism.tree_isomorphism)": [[560, "networkx.algorithms.isomorphism.tree_isomorphism.tree_isomorphism"]], "vf2pp_all_isomorphisms() (in module networkx.algorithms.isomorphism.vf2pp)": [[561, "networkx.algorithms.isomorphism.vf2pp.vf2pp_all_isomorphisms"]], "vf2pp_is_isomorphic() (in module networkx.algorithms.isomorphism.vf2pp)": [[562, "networkx.algorithms.isomorphism.vf2pp.vf2pp_is_isomorphic"]], "vf2pp_isomorphism() (in module networkx.algorithms.isomorphism.vf2pp)": [[563, "networkx.algorithms.isomorphism.vf2pp.vf2pp_isomorphism"]], "hits() (in module networkx.algorithms.link_analysis.hits_alg)": [[564, "networkx.algorithms.link_analysis.hits_alg.hits"]], "google_matrix() (in module networkx.algorithms.link_analysis.pagerank_alg)": [[565, "networkx.algorithms.link_analysis.pagerank_alg.google_matrix"]], "pagerank() (in module networkx.algorithms.link_analysis.pagerank_alg)": [[566, "networkx.algorithms.link_analysis.pagerank_alg.pagerank"]], "adamic_adar_index() (in module networkx.algorithms.link_prediction)": [[567, "networkx.algorithms.link_prediction.adamic_adar_index"]], "cn_soundarajan_hopcroft() (in module networkx.algorithms.link_prediction)": [[568, "networkx.algorithms.link_prediction.cn_soundarajan_hopcroft"]], "common_neighbor_centrality() (in module networkx.algorithms.link_prediction)": [[569, "networkx.algorithms.link_prediction.common_neighbor_centrality"]], "jaccard_coefficient() (in module networkx.algorithms.link_prediction)": [[570, "networkx.algorithms.link_prediction.jaccard_coefficient"]], "preferential_attachment() (in module networkx.algorithms.link_prediction)": [[571, "networkx.algorithms.link_prediction.preferential_attachment"]], "ra_index_soundarajan_hopcroft() (in module networkx.algorithms.link_prediction)": [[572, "networkx.algorithms.link_prediction.ra_index_soundarajan_hopcroft"]], "resource_allocation_index() (in module networkx.algorithms.link_prediction)": [[573, "networkx.algorithms.link_prediction.resource_allocation_index"]], "within_inter_cluster() (in module networkx.algorithms.link_prediction)": [[574, "networkx.algorithms.link_prediction.within_inter_cluster"]], "all_pairs_lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[575, "networkx.algorithms.lowest_common_ancestors.all_pairs_lowest_common_ancestor"]], "lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[576, "networkx.algorithms.lowest_common_ancestors.lowest_common_ancestor"]], "tree_all_pairs_lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[577, "networkx.algorithms.lowest_common_ancestors.tree_all_pairs_lowest_common_ancestor"]], "is_matching() (in module networkx.algorithms.matching)": [[578, "networkx.algorithms.matching.is_matching"]], "is_maximal_matching() (in module networkx.algorithms.matching)": [[579, "networkx.algorithms.matching.is_maximal_matching"]], "is_perfect_matching() (in module networkx.algorithms.matching)": [[580, "networkx.algorithms.matching.is_perfect_matching"]], "max_weight_matching() (in module networkx.algorithms.matching)": [[581, "networkx.algorithms.matching.max_weight_matching"]], "maximal_matching() (in module networkx.algorithms.matching)": [[582, "networkx.algorithms.matching.maximal_matching"]], "min_weight_matching() (in module networkx.algorithms.matching)": [[583, "networkx.algorithms.matching.min_weight_matching"]], "contracted_edge() (in module networkx.algorithms.minors)": [[584, "networkx.algorithms.minors.contracted_edge"]], "contracted_nodes() (in module networkx.algorithms.minors)": [[585, "networkx.algorithms.minors.contracted_nodes"]], "equivalence_classes() (in module networkx.algorithms.minors)": [[586, "networkx.algorithms.minors.equivalence_classes"]], "identified_nodes() (in module networkx.algorithms.minors)": [[587, "networkx.algorithms.minors.identified_nodes"]], "quotient_graph() (in module networkx.algorithms.minors)": [[588, "networkx.algorithms.minors.quotient_graph"]], "maximal_independent_set() (in module networkx.algorithms.mis)": [[589, "networkx.algorithms.mis.maximal_independent_set"]], "moral_graph() (in module networkx.algorithms.moral)": [[590, "networkx.algorithms.moral.moral_graph"]], "harmonic_function() (in module networkx.algorithms.node_classification)": [[591, "networkx.algorithms.node_classification.harmonic_function"]], "local_and_global_consistency() (in module networkx.algorithms.node_classification)": [[592, "networkx.algorithms.node_classification.local_and_global_consistency"]], "non_randomness() (in module networkx.algorithms.non_randomness)": [[593, "networkx.algorithms.non_randomness.non_randomness"]], "compose_all() (in module networkx.algorithms.operators.all)": [[594, "networkx.algorithms.operators.all.compose_all"]], "disjoint_union_all() (in module networkx.algorithms.operators.all)": [[595, "networkx.algorithms.operators.all.disjoint_union_all"]], "intersection_all() (in module networkx.algorithms.operators.all)": [[596, "networkx.algorithms.operators.all.intersection_all"]], "union_all() (in module networkx.algorithms.operators.all)": [[597, "networkx.algorithms.operators.all.union_all"]], "compose() (in module networkx.algorithms.operators.binary)": [[598, "networkx.algorithms.operators.binary.compose"]], "difference() (in module networkx.algorithms.operators.binary)": [[599, "networkx.algorithms.operators.binary.difference"]], "disjoint_union() (in module networkx.algorithms.operators.binary)": [[600, "networkx.algorithms.operators.binary.disjoint_union"]], "full_join() (in module networkx.algorithms.operators.binary)": [[601, "networkx.algorithms.operators.binary.full_join"]], "intersection() (in module networkx.algorithms.operators.binary)": [[602, "networkx.algorithms.operators.binary.intersection"]], "symmetric_difference() (in module networkx.algorithms.operators.binary)": [[603, "networkx.algorithms.operators.binary.symmetric_difference"]], "union() (in module networkx.algorithms.operators.binary)": [[604, "networkx.algorithms.operators.binary.union"]], "cartesian_product() (in module networkx.algorithms.operators.product)": [[605, "networkx.algorithms.operators.product.cartesian_product"]], "corona_product() (in module networkx.algorithms.operators.product)": [[606, "networkx.algorithms.operators.product.corona_product"]], "lexicographic_product() (in module networkx.algorithms.operators.product)": [[607, "networkx.algorithms.operators.product.lexicographic_product"]], "power() (in module networkx.algorithms.operators.product)": [[608, "networkx.algorithms.operators.product.power"]], "rooted_product() (in module networkx.algorithms.operators.product)": [[609, "networkx.algorithms.operators.product.rooted_product"]], "strong_product() (in module networkx.algorithms.operators.product)": [[610, "networkx.algorithms.operators.product.strong_product"]], "tensor_product() (in module networkx.algorithms.operators.product)": [[611, "networkx.algorithms.operators.product.tensor_product"]], "complement() (in module networkx.algorithms.operators.unary)": [[612, "networkx.algorithms.operators.unary.complement"]], "reverse() (in module networkx.algorithms.operators.unary)": [[613, "networkx.algorithms.operators.unary.reverse"]], "combinatorial_embedding_to_pos() (in module networkx.algorithms.planar_drawing)": [[614, "networkx.algorithms.planar_drawing.combinatorial_embedding_to_pos"]], "planarembedding (class in networkx.algorithms.planarity)": [[615, "networkx.algorithms.planarity.PlanarEmbedding"]], "__init__() (planarembedding method)": [[615, "networkx.algorithms.planarity.PlanarEmbedding.__init__"]], "check_planarity() (in module networkx.algorithms.planarity)": [[616, "networkx.algorithms.planarity.check_planarity"]], "is_planar() (in module networkx.algorithms.planarity)": [[617, "networkx.algorithms.planarity.is_planar"]], "chromatic_polynomial() (in module networkx.algorithms.polynomials)": [[618, "networkx.algorithms.polynomials.chromatic_polynomial"]], "tutte_polynomial() (in module networkx.algorithms.polynomials)": [[619, "networkx.algorithms.polynomials.tutte_polynomial"]], "overall_reciprocity() (in module networkx.algorithms.reciprocity)": [[620, "networkx.algorithms.reciprocity.overall_reciprocity"]], "reciprocity() (in module networkx.algorithms.reciprocity)": [[621, "networkx.algorithms.reciprocity.reciprocity"]], "is_k_regular() (in module networkx.algorithms.regular)": [[622, "networkx.algorithms.regular.is_k_regular"]], "is_regular() (in module networkx.algorithms.regular)": [[623, "networkx.algorithms.regular.is_regular"]], "k_factor() (in module networkx.algorithms.regular)": [[624, "networkx.algorithms.regular.k_factor"]], "rich_club_coefficient() (in module networkx.algorithms.richclub)": [[625, "networkx.algorithms.richclub.rich_club_coefficient"]], "astar_path() (in module networkx.algorithms.shortest_paths.astar)": [[626, "networkx.algorithms.shortest_paths.astar.astar_path"]], "astar_path_length() (in module networkx.algorithms.shortest_paths.astar)": [[627, "networkx.algorithms.shortest_paths.astar.astar_path_length"]], "floyd_warshall() (in module networkx.algorithms.shortest_paths.dense)": [[628, "networkx.algorithms.shortest_paths.dense.floyd_warshall"]], "floyd_warshall_numpy() (in module networkx.algorithms.shortest_paths.dense)": [[629, "networkx.algorithms.shortest_paths.dense.floyd_warshall_numpy"]], "floyd_warshall_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.dense)": [[630, "networkx.algorithms.shortest_paths.dense.floyd_warshall_predecessor_and_distance"]], "reconstruct_path() (in module networkx.algorithms.shortest_paths.dense)": [[631, "networkx.algorithms.shortest_paths.dense.reconstruct_path"]], "all_shortest_paths() (in module networkx.algorithms.shortest_paths.generic)": [[632, "networkx.algorithms.shortest_paths.generic.all_shortest_paths"]], "average_shortest_path_length() (in module networkx.algorithms.shortest_paths.generic)": [[633, "networkx.algorithms.shortest_paths.generic.average_shortest_path_length"]], "has_path() (in module networkx.algorithms.shortest_paths.generic)": [[634, "networkx.algorithms.shortest_paths.generic.has_path"]], "shortest_path() (in module networkx.algorithms.shortest_paths.generic)": [[635, "networkx.algorithms.shortest_paths.generic.shortest_path"]], "shortest_path_length() (in module networkx.algorithms.shortest_paths.generic)": [[636, "networkx.algorithms.shortest_paths.generic.shortest_path_length"]], "all_pairs_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[637, "networkx.algorithms.shortest_paths.unweighted.all_pairs_shortest_path"]], "all_pairs_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[638, "networkx.algorithms.shortest_paths.unweighted.all_pairs_shortest_path_length"]], "bidirectional_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[639, "networkx.algorithms.shortest_paths.unweighted.bidirectional_shortest_path"]], "predecessor() (in module networkx.algorithms.shortest_paths.unweighted)": [[640, "networkx.algorithms.shortest_paths.unweighted.predecessor"]], "single_source_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[641, "networkx.algorithms.shortest_paths.unweighted.single_source_shortest_path"]], "single_source_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[642, "networkx.algorithms.shortest_paths.unweighted.single_source_shortest_path_length"]], "single_target_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[643, "networkx.algorithms.shortest_paths.unweighted.single_target_shortest_path"]], "single_target_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[644, "networkx.algorithms.shortest_paths.unweighted.single_target_shortest_path_length"]], "all_pairs_bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[645, "networkx.algorithms.shortest_paths.weighted.all_pairs_bellman_ford_path"]], "all_pairs_bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[646, "networkx.algorithms.shortest_paths.weighted.all_pairs_bellman_ford_path_length"]], "all_pairs_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[647, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra"]], "all_pairs_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[648, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra_path"]], "all_pairs_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[649, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra_path_length"]], "bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[650, "networkx.algorithms.shortest_paths.weighted.bellman_ford_path"]], "bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[651, "networkx.algorithms.shortest_paths.weighted.bellman_ford_path_length"]], "bellman_ford_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.weighted)": [[652, "networkx.algorithms.shortest_paths.weighted.bellman_ford_predecessor_and_distance"]], "bidirectional_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[653, "networkx.algorithms.shortest_paths.weighted.bidirectional_dijkstra"]], "dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[654, "networkx.algorithms.shortest_paths.weighted.dijkstra_path"]], "dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[655, "networkx.algorithms.shortest_paths.weighted.dijkstra_path_length"]], "dijkstra_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.weighted)": [[656, "networkx.algorithms.shortest_paths.weighted.dijkstra_predecessor_and_distance"]], "find_negative_cycle() (in module networkx.algorithms.shortest_paths.weighted)": [[657, "networkx.algorithms.shortest_paths.weighted.find_negative_cycle"]], "goldberg_radzik() (in module networkx.algorithms.shortest_paths.weighted)": [[658, "networkx.algorithms.shortest_paths.weighted.goldberg_radzik"]], "johnson() (in module networkx.algorithms.shortest_paths.weighted)": [[659, "networkx.algorithms.shortest_paths.weighted.johnson"]], "multi_source_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[660, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra"]], "multi_source_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[661, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra_path"]], "multi_source_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[662, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra_path_length"]], "negative_edge_cycle() (in module networkx.algorithms.shortest_paths.weighted)": [[663, "networkx.algorithms.shortest_paths.weighted.negative_edge_cycle"]], "single_source_bellman_ford() (in module networkx.algorithms.shortest_paths.weighted)": [[664, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford"]], "single_source_bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[665, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford_path"]], "single_source_bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[666, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford_path_length"]], "single_source_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[667, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra"]], "single_source_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[668, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra_path"]], "single_source_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[669, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra_path_length"]], "generate_random_paths() (in module networkx.algorithms.similarity)": [[670, "networkx.algorithms.similarity.generate_random_paths"]], "graph_edit_distance() (in module networkx.algorithms.similarity)": [[671, "networkx.algorithms.similarity.graph_edit_distance"]], "optimal_edit_paths() (in module networkx.algorithms.similarity)": [[672, "networkx.algorithms.similarity.optimal_edit_paths"]], "optimize_edit_paths() (in module networkx.algorithms.similarity)": [[673, "networkx.algorithms.similarity.optimize_edit_paths"]], "optimize_graph_edit_distance() (in module networkx.algorithms.similarity)": [[674, "networkx.algorithms.similarity.optimize_graph_edit_distance"]], "panther_similarity() (in module networkx.algorithms.similarity)": [[675, "networkx.algorithms.similarity.panther_similarity"]], "simrank_similarity() (in module networkx.algorithms.similarity)": [[676, "networkx.algorithms.similarity.simrank_similarity"]], "all_simple_edge_paths() (in module networkx.algorithms.simple_paths)": [[677, "networkx.algorithms.simple_paths.all_simple_edge_paths"]], "all_simple_paths() (in module networkx.algorithms.simple_paths)": [[678, "networkx.algorithms.simple_paths.all_simple_paths"]], "is_simple_path() (in module networkx.algorithms.simple_paths)": [[679, "networkx.algorithms.simple_paths.is_simple_path"]], "shortest_simple_paths() (in module networkx.algorithms.simple_paths)": [[680, "networkx.algorithms.simple_paths.shortest_simple_paths"]], "lattice_reference() (in module networkx.algorithms.smallworld)": [[681, "networkx.algorithms.smallworld.lattice_reference"]], "omega() (in module networkx.algorithms.smallworld)": [[682, "networkx.algorithms.smallworld.omega"]], "random_reference() (in module networkx.algorithms.smallworld)": [[683, "networkx.algorithms.smallworld.random_reference"]], "sigma() (in module networkx.algorithms.smallworld)": [[684, "networkx.algorithms.smallworld.sigma"]], "s_metric() (in module networkx.algorithms.smetric)": [[685, "networkx.algorithms.smetric.s_metric"]], "spanner() (in module networkx.algorithms.sparsifiers)": [[686, "networkx.algorithms.sparsifiers.spanner"]], "constraint() (in module networkx.algorithms.structuralholes)": [[687, "networkx.algorithms.structuralholes.constraint"]], "effective_size() (in module networkx.algorithms.structuralholes)": [[688, "networkx.algorithms.structuralholes.effective_size"]], "local_constraint() (in module networkx.algorithms.structuralholes)": [[689, "networkx.algorithms.structuralholes.local_constraint"]], "dedensify() (in module networkx.algorithms.summarization)": [[690, "networkx.algorithms.summarization.dedensify"]], "snap_aggregation() (in module networkx.algorithms.summarization)": [[691, "networkx.algorithms.summarization.snap_aggregation"]], "connected_double_edge_swap() (in module networkx.algorithms.swap)": [[692, "networkx.algorithms.swap.connected_double_edge_swap"]], "directed_edge_swap() (in module networkx.algorithms.swap)": [[693, "networkx.algorithms.swap.directed_edge_swap"]], "double_edge_swap() (in module networkx.algorithms.swap)": [[694, "networkx.algorithms.swap.double_edge_swap"]], "find_threshold_graph() (in module networkx.algorithms.threshold)": [[695, "networkx.algorithms.threshold.find_threshold_graph"]], "is_threshold_graph() (in module networkx.algorithms.threshold)": [[696, "networkx.algorithms.threshold.is_threshold_graph"]], "hamiltonian_path() (in module networkx.algorithms.tournament)": [[697, "networkx.algorithms.tournament.hamiltonian_path"]], "is_reachable() (in module networkx.algorithms.tournament)": [[698, "networkx.algorithms.tournament.is_reachable"]], "is_strongly_connected() (in module networkx.algorithms.tournament)": [[699, "networkx.algorithms.tournament.is_strongly_connected"]], "is_tournament() (in module networkx.algorithms.tournament)": [[700, "networkx.algorithms.tournament.is_tournament"]], "random_tournament() (in module networkx.algorithms.tournament)": [[701, "networkx.algorithms.tournament.random_tournament"]], "score_sequence() (in module networkx.algorithms.tournament)": [[702, "networkx.algorithms.tournament.score_sequence"]], "bfs_beam_edges() (in module networkx.algorithms.traversal.beamsearch)": [[703, "networkx.algorithms.traversal.beamsearch.bfs_beam_edges"]], "bfs_edges() (in module networkx.algorithms.traversal.breadth_first_search)": [[704, "networkx.algorithms.traversal.breadth_first_search.bfs_edges"]], "bfs_layers() (in module networkx.algorithms.traversal.breadth_first_search)": [[705, "networkx.algorithms.traversal.breadth_first_search.bfs_layers"]], "bfs_predecessors() (in module networkx.algorithms.traversal.breadth_first_search)": [[706, "networkx.algorithms.traversal.breadth_first_search.bfs_predecessors"]], "bfs_successors() (in module networkx.algorithms.traversal.breadth_first_search)": [[707, "networkx.algorithms.traversal.breadth_first_search.bfs_successors"]], "bfs_tree() (in module networkx.algorithms.traversal.breadth_first_search)": [[708, "networkx.algorithms.traversal.breadth_first_search.bfs_tree"]], "descendants_at_distance() (in module networkx.algorithms.traversal.breadth_first_search)": [[709, "networkx.algorithms.traversal.breadth_first_search.descendants_at_distance"]], "dfs_edges() (in module networkx.algorithms.traversal.depth_first_search)": [[710, "networkx.algorithms.traversal.depth_first_search.dfs_edges"]], "dfs_labeled_edges() (in module networkx.algorithms.traversal.depth_first_search)": [[711, "networkx.algorithms.traversal.depth_first_search.dfs_labeled_edges"]], "dfs_postorder_nodes() (in module networkx.algorithms.traversal.depth_first_search)": [[712, "networkx.algorithms.traversal.depth_first_search.dfs_postorder_nodes"]], "dfs_predecessors() (in module networkx.algorithms.traversal.depth_first_search)": [[713, "networkx.algorithms.traversal.depth_first_search.dfs_predecessors"]], "dfs_preorder_nodes() (in module networkx.algorithms.traversal.depth_first_search)": [[714, "networkx.algorithms.traversal.depth_first_search.dfs_preorder_nodes"]], "dfs_successors() (in module networkx.algorithms.traversal.depth_first_search)": [[715, "networkx.algorithms.traversal.depth_first_search.dfs_successors"]], "dfs_tree() (in module networkx.algorithms.traversal.depth_first_search)": [[716, "networkx.algorithms.traversal.depth_first_search.dfs_tree"]], "edge_bfs() (in module networkx.algorithms.traversal.edgebfs)": [[717, "networkx.algorithms.traversal.edgebfs.edge_bfs"]], "edge_dfs() (in module networkx.algorithms.traversal.edgedfs)": [[718, "networkx.algorithms.traversal.edgedfs.edge_dfs"]], "arborescenceiterator (class in networkx.algorithms.tree.branchings)": [[719, "networkx.algorithms.tree.branchings.ArborescenceIterator"]], "__init__() (arborescenceiterator method)": [[719, "networkx.algorithms.tree.branchings.ArborescenceIterator.__init__"]], "edmonds (class in networkx.algorithms.tree.branchings)": [[720, "networkx.algorithms.tree.branchings.Edmonds"]], "__init__() (edmonds method)": [[720, "networkx.algorithms.tree.branchings.Edmonds.__init__"]], "branching_weight() (in module networkx.algorithms.tree.branchings)": [[721, "networkx.algorithms.tree.branchings.branching_weight"]], "greedy_branching() (in module networkx.algorithms.tree.branchings)": [[722, "networkx.algorithms.tree.branchings.greedy_branching"]], "maximum_branching() (in module networkx.algorithms.tree.branchings)": [[723, "networkx.algorithms.tree.branchings.maximum_branching"]], "maximum_spanning_arborescence() (in module networkx.algorithms.tree.branchings)": [[724, "networkx.algorithms.tree.branchings.maximum_spanning_arborescence"]], "minimum_branching() (in module networkx.algorithms.tree.branchings)": [[725, "networkx.algorithms.tree.branchings.minimum_branching"]], "minimum_spanning_arborescence() (in module networkx.algorithms.tree.branchings)": [[726, "networkx.algorithms.tree.branchings.minimum_spanning_arborescence"]], "notatree": [[727, "networkx.algorithms.tree.coding.NotATree"]], "from_nested_tuple() (in module networkx.algorithms.tree.coding)": [[728, "networkx.algorithms.tree.coding.from_nested_tuple"]], "from_prufer_sequence() (in module networkx.algorithms.tree.coding)": [[729, "networkx.algorithms.tree.coding.from_prufer_sequence"]], "to_nested_tuple() (in module networkx.algorithms.tree.coding)": [[730, "networkx.algorithms.tree.coding.to_nested_tuple"]], "to_prufer_sequence() (in module networkx.algorithms.tree.coding)": [[731, "networkx.algorithms.tree.coding.to_prufer_sequence"]], "junction_tree() (in module networkx.algorithms.tree.decomposition)": [[732, "networkx.algorithms.tree.decomposition.junction_tree"]], "spanningtreeiterator (class in networkx.algorithms.tree.mst)": [[733, "networkx.algorithms.tree.mst.SpanningTreeIterator"]], "__init__() (spanningtreeiterator method)": [[733, "networkx.algorithms.tree.mst.SpanningTreeIterator.__init__"]], "maximum_spanning_edges() (in module networkx.algorithms.tree.mst)": [[734, "networkx.algorithms.tree.mst.maximum_spanning_edges"]], "maximum_spanning_tree() (in module networkx.algorithms.tree.mst)": [[735, "networkx.algorithms.tree.mst.maximum_spanning_tree"]], "minimum_spanning_edges() (in module networkx.algorithms.tree.mst)": [[736, "networkx.algorithms.tree.mst.minimum_spanning_edges"]], "minimum_spanning_tree() (in module networkx.algorithms.tree.mst)": [[737, "networkx.algorithms.tree.mst.minimum_spanning_tree"]], "random_spanning_tree() (in module networkx.algorithms.tree.mst)": [[738, "networkx.algorithms.tree.mst.random_spanning_tree"]], "join() (in module networkx.algorithms.tree.operations)": [[739, "networkx.algorithms.tree.operations.join"]], "is_arborescence() (in module networkx.algorithms.tree.recognition)": [[740, "networkx.algorithms.tree.recognition.is_arborescence"]], "is_branching() (in module networkx.algorithms.tree.recognition)": [[741, "networkx.algorithms.tree.recognition.is_branching"]], "is_forest() (in module networkx.algorithms.tree.recognition)": [[742, "networkx.algorithms.tree.recognition.is_forest"]], "is_tree() (in module networkx.algorithms.tree.recognition)": [[743, "networkx.algorithms.tree.recognition.is_tree"]], "all_triads() (in module networkx.algorithms.triads)": [[744, "networkx.algorithms.triads.all_triads"]], "all_triplets() (in module networkx.algorithms.triads)": [[745, "networkx.algorithms.triads.all_triplets"]], "is_triad() (in module networkx.algorithms.triads)": [[746, "networkx.algorithms.triads.is_triad"]], "random_triad() (in module networkx.algorithms.triads)": [[747, "networkx.algorithms.triads.random_triad"]], "triad_type() (in module networkx.algorithms.triads)": [[748, "networkx.algorithms.triads.triad_type"]], "triadic_census() (in module networkx.algorithms.triads)": [[749, "networkx.algorithms.triads.triadic_census"]], "triads_by_type() (in module networkx.algorithms.triads)": [[750, "networkx.algorithms.triads.triads_by_type"]], "closeness_vitality() (in module networkx.algorithms.vitality)": [[751, "networkx.algorithms.vitality.closeness_vitality"]], "voronoi_cells() (in module networkx.algorithms.voronoi)": [[752, "networkx.algorithms.voronoi.voronoi_cells"]], "wiener_index() (in module networkx.algorithms.wiener)": [[753, "networkx.algorithms.wiener.wiener_index"]], "networkx.algorithms.graph_hashing": [[754, "module-networkx.algorithms.graph_hashing"]], "networkx.algorithms.graphical": [[755, "module-networkx.algorithms.graphical"]], "networkx.algorithms.hierarchy": [[756, "module-networkx.algorithms.hierarchy"]], "networkx.algorithms.hybrid": [[757, "module-networkx.algorithms.hybrid"]], "networkx.algorithms.isolate": [[759, "module-networkx.algorithms.isolate"]], "networkx.algorithms.isomorphism": [[760, "module-networkx.algorithms.isomorphism"]], "networkx.algorithms.isomorphism.tree_isomorphism": [[760, "module-networkx.algorithms.isomorphism.tree_isomorphism"]], "networkx.algorithms.isomorphism.vf2pp": [[760, "module-networkx.algorithms.isomorphism.vf2pp"]], "networkx.algorithms.isomorphism.ismags": [[761, "module-networkx.algorithms.isomorphism.ismags"]], "networkx.algorithms.isomorphism.isomorphvf2": [[762, "module-networkx.algorithms.isomorphism.isomorphvf2"]], "networkx.algorithms.link_analysis.hits_alg": [[763, "module-networkx.algorithms.link_analysis.hits_alg"]], "networkx.algorithms.link_analysis.pagerank_alg": [[763, "module-networkx.algorithms.link_analysis.pagerank_alg"]], "networkx.algorithms.link_prediction": [[764, "module-networkx.algorithms.link_prediction"]], "networkx.algorithms.lowest_common_ancestors": [[765, "module-networkx.algorithms.lowest_common_ancestors"]], "networkx.algorithms.matching": [[766, "module-networkx.algorithms.matching"]], "networkx.algorithms.minors": [[767, "module-networkx.algorithms.minors"]], "networkx.algorithms.mis": [[768, "module-networkx.algorithms.mis"]], "networkx.algorithms.moral": [[769, "module-networkx.algorithms.moral"]], "networkx.algorithms.node_classification": [[770, "module-networkx.algorithms.node_classification"]], "networkx.algorithms.non_randomness": [[771, "module-networkx.algorithms.non_randomness"]], "networkx.algorithms.operators.all": [[772, "module-networkx.algorithms.operators.all"]], "networkx.algorithms.operators.binary": [[772, "module-networkx.algorithms.operators.binary"]], "networkx.algorithms.operators.product": [[772, "module-networkx.algorithms.operators.product"]], "networkx.algorithms.operators.unary": [[772, "module-networkx.algorithms.operators.unary"]], "networkx.algorithms.planar_drawing": [[773, "module-networkx.algorithms.planar_drawing"]], "networkx.algorithms.planarity": [[774, "module-networkx.algorithms.planarity"]], "networkx.algorithms.polynomials": [[775, "module-networkx.algorithms.polynomials"]], "networkx.algorithms.reciprocity": [[776, "module-networkx.algorithms.reciprocity"]], "networkx.algorithms.regular": [[777, "module-networkx.algorithms.regular"]], "networkx.algorithms.richclub": [[778, "module-networkx.algorithms.richclub"]], "networkx.algorithms.shortest_paths.astar": [[779, "module-networkx.algorithms.shortest_paths.astar"]], "networkx.algorithms.shortest_paths.dense": [[779, "module-networkx.algorithms.shortest_paths.dense"]], "networkx.algorithms.shortest_paths.generic": [[779, "module-networkx.algorithms.shortest_paths.generic"]], "networkx.algorithms.shortest_paths.unweighted": [[779, "module-networkx.algorithms.shortest_paths.unweighted"]], "networkx.algorithms.shortest_paths.weighted": [[779, "module-networkx.algorithms.shortest_paths.weighted"]], "networkx.algorithms.similarity": [[780, "module-networkx.algorithms.similarity"]], "networkx.algorithms.simple_paths": [[781, "module-networkx.algorithms.simple_paths"]], "networkx.algorithms.smallworld": [[782, "module-networkx.algorithms.smallworld"]], "networkx.algorithms.smetric": [[783, "module-networkx.algorithms.smetric"]], "networkx.algorithms.sparsifiers": [[784, "module-networkx.algorithms.sparsifiers"]], "networkx.algorithms.structuralholes": [[785, "module-networkx.algorithms.structuralholes"]], "networkx.algorithms.summarization": [[786, "module-networkx.algorithms.summarization"]], "networkx.algorithms.swap": [[787, "module-networkx.algorithms.swap"]], "networkx.algorithms.threshold": [[788, "module-networkx.algorithms.threshold"]], "networkx.algorithms.tournament": [[789, "module-networkx.algorithms.tournament"]], "networkx.algorithms.traversal.beamsearch": [[790, "module-networkx.algorithms.traversal.beamsearch"]], "networkx.algorithms.traversal.breadth_first_search": [[790, "module-networkx.algorithms.traversal.breadth_first_search"]], "networkx.algorithms.traversal.depth_first_search": [[790, "module-networkx.algorithms.traversal.depth_first_search"]], "networkx.algorithms.traversal.edgebfs": [[790, "module-networkx.algorithms.traversal.edgebfs"]], "networkx.algorithms.traversal.edgedfs": [[790, "module-networkx.algorithms.traversal.edgedfs"]], "networkx.algorithms.tree.branchings": [[791, "module-networkx.algorithms.tree.branchings"]], "networkx.algorithms.tree.coding": [[791, "module-networkx.algorithms.tree.coding"]], "networkx.algorithms.tree.decomposition": [[791, "module-networkx.algorithms.tree.decomposition"]], "networkx.algorithms.tree.mst": [[791, "module-networkx.algorithms.tree.mst"]], "networkx.algorithms.tree.operations": [[791, "module-networkx.algorithms.tree.operations"]], "networkx.algorithms.tree.recognition": [[791, "module-networkx.algorithms.tree.recognition"]], "networkx.algorithms.triads": [[792, "module-networkx.algorithms.triads"]], "networkx.algorithms.vitality": [[793, "module-networkx.algorithms.vitality"]], "networkx.algorithms.voronoi": [[794, "module-networkx.algorithms.voronoi"]], "networkx.algorithms.wiener": [[795, "module-networkx.algorithms.wiener"]], "digraph (class in networkx)": [[796, "networkx.DiGraph"]], "copy() (adjacencyview method)": [[797, "networkx.classes.coreviews.AdjacencyView.copy"]], "get() (adjacencyview method)": [[798, "networkx.classes.coreviews.AdjacencyView.get"]], "items() (adjacencyview method)": [[799, "networkx.classes.coreviews.AdjacencyView.items"]], "keys() (adjacencyview method)": [[800, "networkx.classes.coreviews.AdjacencyView.keys"]], "values() (adjacencyview method)": [[801, "networkx.classes.coreviews.AdjacencyView.values"]], "copy() (atlasview method)": [[802, "networkx.classes.coreviews.AtlasView.copy"]], "get() (atlasview method)": [[803, 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"networkx.classes.coreviews.UnionAtlas.get"]], "items() (unionatlas method)": [[835, "networkx.classes.coreviews.UnionAtlas.items"]], "keys() (unionatlas method)": [[836, "networkx.classes.coreviews.UnionAtlas.keys"]], "values() (unionatlas method)": [[837, "networkx.classes.coreviews.UnionAtlas.values"]], "copy() (unionmultiadjacency method)": [[838, "networkx.classes.coreviews.UnionMultiAdjacency.copy"]], "get() (unionmultiadjacency method)": [[839, "networkx.classes.coreviews.UnionMultiAdjacency.get"]], "items() (unionmultiadjacency method)": [[840, "networkx.classes.coreviews.UnionMultiAdjacency.items"]], "keys() (unionmultiadjacency method)": [[841, "networkx.classes.coreviews.UnionMultiAdjacency.keys"]], "values() (unionmultiadjacency method)": [[842, "networkx.classes.coreviews.UnionMultiAdjacency.values"]], "copy() (unionmultiinner method)": [[843, "networkx.classes.coreviews.UnionMultiInner.copy"]], "get() (unionmultiinner method)": [[844, 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"add_weighted_edges_from() (digraph method)": [[857, "networkx.DiGraph.add_weighted_edges_from"]], "adj (digraph property)": [[858, "networkx.DiGraph.adj"]], "adjacency() (digraph method)": [[859, "networkx.DiGraph.adjacency"]], "clear() (digraph method)": [[860, "networkx.DiGraph.clear"]], "clear_edges() (digraph method)": [[861, "networkx.DiGraph.clear_edges"]], "copy() (digraph method)": [[862, "networkx.DiGraph.copy"]], "degree (digraph property)": [[863, "networkx.DiGraph.degree"]], "edge_subgraph() (digraph method)": [[864, "networkx.DiGraph.edge_subgraph"]], "edges (digraph property)": [[865, "networkx.DiGraph.edges"]], "get_edge_data() (digraph method)": [[866, "networkx.DiGraph.get_edge_data"]], "has_edge() (digraph method)": [[867, "networkx.DiGraph.has_edge"]], "has_node() (digraph method)": [[868, "networkx.DiGraph.has_node"]], "in_degree (digraph property)": [[869, "networkx.DiGraph.in_degree"]], "in_edges (digraph property)": [[870, "networkx.DiGraph.in_edges"]], "nbunch_iter() (digraph method)": [[871, "networkx.DiGraph.nbunch_iter"]], "neighbors() (digraph method)": [[872, "networkx.DiGraph.neighbors"]], "nodes (digraph property)": [[873, "networkx.DiGraph.nodes"]], "number_of_edges() (digraph method)": [[874, "networkx.DiGraph.number_of_edges"]], "number_of_nodes() (digraph method)": [[875, "networkx.DiGraph.number_of_nodes"]], "order() (digraph method)": [[876, "networkx.DiGraph.order"]], "out_degree (digraph property)": [[877, "networkx.DiGraph.out_degree"]], "out_edges (digraph property)": [[878, "networkx.DiGraph.out_edges"]], "pred (digraph property)": [[879, "networkx.DiGraph.pred"]], "predecessors() (digraph method)": [[880, "networkx.DiGraph.predecessors"]], "remove_edge() (digraph method)": [[881, "networkx.DiGraph.remove_edge"]], "remove_edges_from() (digraph method)": [[882, "networkx.DiGraph.remove_edges_from"]], "remove_node() (digraph method)": [[883, "networkx.DiGraph.remove_node"]], "remove_nodes_from() (digraph method)": 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method)": [[988, "networkx.MultiGraph.copy"]], "degree (multigraph property)": [[989, "networkx.MultiGraph.degree"]], "edge_subgraph() (multigraph method)": [[990, "networkx.MultiGraph.edge_subgraph"]], "edges (multigraph property)": [[991, "networkx.MultiGraph.edges"]], "get_edge_data() (multigraph method)": [[992, "networkx.MultiGraph.get_edge_data"]], "has_edge() (multigraph method)": [[993, "networkx.MultiGraph.has_edge"]], "has_node() (multigraph method)": [[994, "networkx.MultiGraph.has_node"]], "nbunch_iter() (multigraph method)": [[995, "networkx.MultiGraph.nbunch_iter"]], "neighbors() (multigraph method)": [[996, "networkx.MultiGraph.neighbors"]], "new_edge_key() (multigraph method)": [[997, "networkx.MultiGraph.new_edge_key"]], "nodes (multigraph property)": [[998, "networkx.MultiGraph.nodes"]], "number_of_edges() (multigraph method)": [[999, "networkx.MultiGraph.number_of_edges"]], "number_of_nodes() (multigraph method)": [[1000, "networkx.MultiGraph.number_of_nodes"]], "order() (multigraph method)": [[1001, "networkx.MultiGraph.order"]], "remove_edge() (multigraph method)": [[1002, "networkx.MultiGraph.remove_edge"]], "remove_edges_from() (multigraph method)": [[1003, "networkx.MultiGraph.remove_edges_from"]], "remove_node() (multigraph method)": [[1004, "networkx.MultiGraph.remove_node"]], "remove_nodes_from() (multigraph method)": [[1005, "networkx.MultiGraph.remove_nodes_from"]], "size() (multigraph method)": [[1006, "networkx.MultiGraph.size"]], "subgraph() (multigraph method)": [[1007, "networkx.MultiGraph.subgraph"]], "to_directed() (multigraph method)": [[1008, "networkx.MultiGraph.to_directed"]], "to_undirected() (multigraph method)": [[1009, "networkx.MultiGraph.to_undirected"]], "update() (multigraph method)": [[1010, "networkx.MultiGraph.update"]], "_dispatch() (in module networkx.classes.backends)": [[1011, "networkx.classes.backends._dispatch"]], "adjacencyview (class in networkx.classes.coreviews)": [[1012, "networkx.classes.coreviews.AdjacencyView"]], "__init__() (adjacencyview method)": [[1012, "networkx.classes.coreviews.AdjacencyView.__init__"]], "atlasview (class in networkx.classes.coreviews)": [[1013, "networkx.classes.coreviews.AtlasView"]], "__init__() (atlasview method)": [[1013, "networkx.classes.coreviews.AtlasView.__init__"]], "filteradjacency (class in networkx.classes.coreviews)": [[1014, "networkx.classes.coreviews.FilterAdjacency"]], "__init__() (filteradjacency method)": [[1014, "networkx.classes.coreviews.FilterAdjacency.__init__"]], "filteratlas (class in networkx.classes.coreviews)": [[1015, "networkx.classes.coreviews.FilterAtlas"]], "__init__() (filteratlas method)": [[1015, "networkx.classes.coreviews.FilterAtlas.__init__"]], "filtermultiadjacency (class in networkx.classes.coreviews)": [[1016, "networkx.classes.coreviews.FilterMultiAdjacency"]], "__init__() (filtermultiadjacency method)": [[1016, "networkx.classes.coreviews.FilterMultiAdjacency.__init__"]], "filtermultiinner (class in networkx.classes.coreviews)": [[1017, "networkx.classes.coreviews.FilterMultiInner"]], "__init__() (filtermultiinner method)": [[1017, "networkx.classes.coreviews.FilterMultiInner.__init__"]], "multiadjacencyview (class in networkx.classes.coreviews)": [[1018, "networkx.classes.coreviews.MultiAdjacencyView"]], "__init__() (multiadjacencyview method)": [[1018, "networkx.classes.coreviews.MultiAdjacencyView.__init__"]], "unionadjacency (class in networkx.classes.coreviews)": [[1019, "networkx.classes.coreviews.UnionAdjacency"]], "__init__() (unionadjacency method)": [[1019, "networkx.classes.coreviews.UnionAdjacency.__init__"]], "unionatlas (class in networkx.classes.coreviews)": [[1020, "networkx.classes.coreviews.UnionAtlas"]], "__init__() (unionatlas method)": [[1020, "networkx.classes.coreviews.UnionAtlas.__init__"]], "unionmultiadjacency (class in networkx.classes.coreviews)": [[1021, "networkx.classes.coreviews.UnionMultiAdjacency"]], "__init__() (unionmultiadjacency method)": [[1021, "networkx.classes.coreviews.UnionMultiAdjacency.__init__"]], "unionmultiinner (class in networkx.classes.coreviews)": [[1022, "networkx.classes.coreviews.UnionMultiInner"]], "__init__() (unionmultiinner method)": [[1022, "networkx.classes.coreviews.UnionMultiInner.__init__"]], "hide_diedges() (in module networkx.classes.filters)": [[1023, "networkx.classes.filters.hide_diedges"]], "hide_edges() (in module networkx.classes.filters)": [[1024, "networkx.classes.filters.hide_edges"]], "hide_multidiedges() (in module networkx.classes.filters)": [[1025, "networkx.classes.filters.hide_multidiedges"]], "hide_multiedges() (in module networkx.classes.filters)": [[1026, "networkx.classes.filters.hide_multiedges"]], "hide_nodes() (in module networkx.classes.filters)": [[1027, "networkx.classes.filters.hide_nodes"]], "no_filter() (in module networkx.classes.filters)": [[1028, "networkx.classes.filters.no_filter"]], "show_diedges() (in module networkx.classes.filters)": [[1029, "networkx.classes.filters.show_diedges"]], "show_edges() (in module networkx.classes.filters)": [[1030, "networkx.classes.filters.show_edges"]], "show_multidiedges() (in module networkx.classes.filters)": [[1031, "networkx.classes.filters.show_multidiedges"]], "show_multiedges() (in module networkx.classes.filters)": [[1032, "networkx.classes.filters.show_multiedges"]], "__init__() (show_nodes method)": [[1033, "networkx.classes.filters.show_nodes.__init__"]], "show_nodes (class in networkx.classes.filters)": [[1033, "networkx.classes.filters.show_nodes"]], "generic_graph_view() (in module networkx.classes.graphviews)": [[1034, "networkx.classes.graphviews.generic_graph_view"]], "reverse_view() (in module networkx.classes.graphviews)": [[1035, "networkx.classes.graphviews.reverse_view"]], "subgraph_view() (in module networkx.classes.graphviews)": [[1036, "networkx.classes.graphviews.subgraph_view"]], "graph (class in networkx)": [[1037, "networkx.Graph"]], "networkx.classes.backends": [[1038, "module-networkx.classes.backends"]], "networkx.classes.coreviews": [[1038, "module-networkx.classes.coreviews"]], "networkx.classes.filters": [[1038, "module-networkx.classes.filters"]], "networkx.classes.graphviews": [[1038, "module-networkx.classes.graphviews"]], "multidigraph (class in networkx)": [[1039, "networkx.MultiDiGraph"]], "multigraph (class in networkx)": [[1040, "networkx.MultiGraph"]], "networkx.convert": [[1041, "module-networkx.convert"]], "networkx.convert_matrix": [[1041, "module-networkx.convert_matrix"]], "networkx.drawing.layout": [[1042, "module-networkx.drawing.layout"]], "networkx.drawing.nx_agraph": [[1042, "module-networkx.drawing.nx_agraph"]], "networkx.drawing.nx_pydot": [[1042, "module-networkx.drawing.nx_pydot"]], "networkx.drawing.nx_pylab": [[1042, "module-networkx.drawing.nx_pylab"]], "ambiguoussolution (class in networkx)": [[1043, "networkx.AmbiguousSolution"]], "exceededmaxiterations (class in networkx)": [[1043, "networkx.ExceededMaxIterations"]], "hasacycle (class in networkx)": [[1043, "networkx.HasACycle"]], "networkxalgorithmerror (class in networkx)": [[1043, "networkx.NetworkXAlgorithmError"]], "networkxerror (class in networkx)": [[1043, "networkx.NetworkXError"]], "networkxexception (class in networkx)": [[1043, "networkx.NetworkXException"]], "networkxnocycle (class in networkx)": [[1043, "networkx.NetworkXNoCycle"]], "networkxnopath (class in networkx)": [[1043, "networkx.NetworkXNoPath"]], "networkxnotimplemented (class in networkx)": [[1043, "networkx.NetworkXNotImplemented"]], "networkxpointlessconcept (class in networkx)": [[1043, "networkx.NetworkXPointlessConcept"]], "networkxunbounded (class in networkx)": [[1043, "networkx.NetworkXUnbounded"]], "networkxunfeasible (class in networkx)": [[1043, "networkx.NetworkXUnfeasible"]], "nodenotfound (class in networkx)": [[1043, "networkx.NodeNotFound"]], "poweriterationfailedconvergence (class in networkx)": [[1043, "networkx.PowerIterationFailedConvergence"]], "networkx.exception": [[1043, "module-networkx.exception"]], "networkx.classes.function": [[1044, "module-networkx.classes.function"]], "assemble() (argmap method)": [[1045, "networkx.utils.decorators.argmap.assemble"]], "compile() (argmap method)": [[1046, "networkx.utils.decorators.argmap.compile"]], "signature() (argmap class method)": [[1047, "networkx.utils.decorators.argmap.signature"]], "pop() (mappedqueue method)": [[1048, "networkx.utils.mapped_queue.MappedQueue.pop"]], "push() (mappedqueue method)": [[1049, "networkx.utils.mapped_queue.MappedQueue.push"]], "remove() (mappedqueue method)": [[1050, "networkx.utils.mapped_queue.MappedQueue.remove"]], "update() (mappedqueue method)": [[1051, "networkx.utils.mapped_queue.MappedQueue.update"]], "add_cycle() (in module networkx.classes.function)": [[1052, "networkx.classes.function.add_cycle"]], "add_path() (in module networkx.classes.function)": [[1053, "networkx.classes.function.add_path"]], "add_star() (in module networkx.classes.function)": [[1054, "networkx.classes.function.add_star"]], "all_neighbors() (in module networkx.classes.function)": [[1055, "networkx.classes.function.all_neighbors"]], "common_neighbors() (in module networkx.classes.function)": [[1056, "networkx.classes.function.common_neighbors"]], "create_empty_copy() (in module networkx.classes.function)": [[1057, "networkx.classes.function.create_empty_copy"]], "degree() (in module networkx.classes.function)": [[1058, "networkx.classes.function.degree"]], "degree_histogram() (in module networkx.classes.function)": [[1059, "networkx.classes.function.degree_histogram"]], "density() (in module networkx.classes.function)": [[1060, "networkx.classes.function.density"]], "edge_subgraph() (in module networkx.classes.function)": [[1061, "networkx.classes.function.edge_subgraph"]], "edges() (in module networkx.classes.function)": [[1062, "networkx.classes.function.edges"]], "freeze() (in module networkx.classes.function)": [[1063, "networkx.classes.function.freeze"]], "get_edge_attributes() (in module networkx.classes.function)": [[1064, "networkx.classes.function.get_edge_attributes"]], "get_node_attributes() (in module networkx.classes.function)": [[1065, "networkx.classes.function.get_node_attributes"]], "induced_subgraph() (in module networkx.classes.function)": [[1066, "networkx.classes.function.induced_subgraph"]], "is_directed() (in module networkx.classes.function)": [[1067, "networkx.classes.function.is_directed"]], "is_empty() (in module networkx.classes.function)": [[1068, "networkx.classes.function.is_empty"]], "is_frozen() (in module networkx.classes.function)": [[1069, "networkx.classes.function.is_frozen"]], "is_negatively_weighted() (in module networkx.classes.function)": [[1070, "networkx.classes.function.is_negatively_weighted"]], "is_path() (in module networkx.classes.function)": [[1071, "networkx.classes.function.is_path"]], "is_weighted() (in module networkx.classes.function)": [[1072, "networkx.classes.function.is_weighted"]], "neighbors() (in module networkx.classes.function)": [[1073, "networkx.classes.function.neighbors"]], "nodes() (in module networkx.classes.function)": [[1074, "networkx.classes.function.nodes"]], "nodes_with_selfloops() (in module networkx.classes.function)": [[1075, "networkx.classes.function.nodes_with_selfloops"]], "non_edges() (in module networkx.classes.function)": [[1076, "networkx.classes.function.non_edges"]], "non_neighbors() (in module networkx.classes.function)": [[1077, "networkx.classes.function.non_neighbors"]], "number_of_edges() (in module networkx.classes.function)": [[1078, "networkx.classes.function.number_of_edges"]], "number_of_nodes() (in module networkx.classes.function)": [[1079, "networkx.classes.function.number_of_nodes"]], "number_of_selfloops() (in module networkx.classes.function)": [[1080, "networkx.classes.function.number_of_selfloops"]], "path_weight() (in module networkx.classes.function)": [[1081, "networkx.classes.function.path_weight"]], "restricted_view() (in module networkx.classes.function)": [[1082, "networkx.classes.function.restricted_view"]], "reverse_view() (in module networkx.classes.function)": [[1083, "networkx.classes.function.reverse_view"]], "selfloop_edges() (in module networkx.classes.function)": [[1084, "networkx.classes.function.selfloop_edges"]], "set_edge_attributes() (in module networkx.classes.function)": [[1085, "networkx.classes.function.set_edge_attributes"]], "set_node_attributes() (in module networkx.classes.function)": [[1086, "networkx.classes.function.set_node_attributes"]], "subgraph() (in module networkx.classes.function)": [[1087, "networkx.classes.function.subgraph"]], "subgraph_view() (in module networkx.classes.function)": [[1088, "networkx.classes.function.subgraph_view"]], "to_directed() (in module networkx.classes.function)": [[1089, "networkx.classes.function.to_directed"]], "to_undirected() (in module networkx.classes.function)": [[1090, "networkx.classes.function.to_undirected"]], "from_dict_of_dicts() (in module networkx.convert)": [[1091, "networkx.convert.from_dict_of_dicts"]], "from_dict_of_lists() (in module networkx.convert)": [[1092, "networkx.convert.from_dict_of_lists"]], "from_edgelist() (in module networkx.convert)": [[1093, "networkx.convert.from_edgelist"]], "to_dict_of_dicts() (in module networkx.convert)": [[1094, "networkx.convert.to_dict_of_dicts"]], "to_dict_of_lists() (in module networkx.convert)": [[1095, "networkx.convert.to_dict_of_lists"]], "to_edgelist() (in module networkx.convert)": [[1096, "networkx.convert.to_edgelist"]], "to_networkx_graph() (in module networkx.convert)": [[1097, "networkx.convert.to_networkx_graph"]], "from_numpy_array() (in module networkx.convert_matrix)": [[1098, "networkx.convert_matrix.from_numpy_array"]], "from_pandas_adjacency() (in module networkx.convert_matrix)": [[1099, "networkx.convert_matrix.from_pandas_adjacency"]], "from_pandas_edgelist() (in module networkx.convert_matrix)": [[1100, "networkx.convert_matrix.from_pandas_edgelist"]], "from_scipy_sparse_array() (in module networkx.convert_matrix)": [[1101, "networkx.convert_matrix.from_scipy_sparse_array"]], "to_numpy_array() (in module networkx.convert_matrix)": [[1102, "networkx.convert_matrix.to_numpy_array"]], "to_pandas_adjacency() (in module networkx.convert_matrix)": [[1103, "networkx.convert_matrix.to_pandas_adjacency"]], "to_pandas_edgelist() (in module networkx.convert_matrix)": [[1104, "networkx.convert_matrix.to_pandas_edgelist"]], "to_scipy_sparse_array() (in module networkx.convert_matrix)": [[1105, "networkx.convert_matrix.to_scipy_sparse_array"]], "bipartite_layout() (in module networkx.drawing.layout)": [[1106, "networkx.drawing.layout.bipartite_layout"]], "circular_layout() (in module networkx.drawing.layout)": [[1107, "networkx.drawing.layout.circular_layout"]], "kamada_kawai_layout() (in module networkx.drawing.layout)": [[1108, "networkx.drawing.layout.kamada_kawai_layout"]], "multipartite_layout() (in module networkx.drawing.layout)": [[1109, "networkx.drawing.layout.multipartite_layout"]], "planar_layout() (in module networkx.drawing.layout)": [[1110, "networkx.drawing.layout.planar_layout"]], "random_layout() (in module networkx.drawing.layout)": [[1111, "networkx.drawing.layout.random_layout"]], "rescale_layout() (in module networkx.drawing.layout)": [[1112, "networkx.drawing.layout.rescale_layout"]], "rescale_layout_dict() (in module networkx.drawing.layout)": [[1113, "networkx.drawing.layout.rescale_layout_dict"]], "shell_layout() (in module networkx.drawing.layout)": [[1114, "networkx.drawing.layout.shell_layout"]], "spectral_layout() (in module networkx.drawing.layout)": [[1115, "networkx.drawing.layout.spectral_layout"]], "spiral_layout() (in module networkx.drawing.layout)": [[1116, "networkx.drawing.layout.spiral_layout"]], 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module networkx.drawing.nx_pydot)": [[1126, "networkx.drawing.nx_pydot.pydot_layout"]], "read_dot() (in module networkx.drawing.nx_pydot)": [[1127, "networkx.drawing.nx_pydot.read_dot"]], "to_pydot() (in module networkx.drawing.nx_pydot)": [[1128, "networkx.drawing.nx_pydot.to_pydot"]], "write_dot() (in module networkx.drawing.nx_pydot)": [[1129, "networkx.drawing.nx_pydot.write_dot"]], "draw() (in module networkx.drawing.nx_pylab)": [[1130, "networkx.drawing.nx_pylab.draw"]], "draw_circular() (in module networkx.drawing.nx_pylab)": [[1131, "networkx.drawing.nx_pylab.draw_circular"]], "draw_kamada_kawai() (in module networkx.drawing.nx_pylab)": [[1132, "networkx.drawing.nx_pylab.draw_kamada_kawai"]], "draw_networkx() (in module networkx.drawing.nx_pylab)": [[1133, "networkx.drawing.nx_pylab.draw_networkx"]], "draw_networkx_edge_labels() (in module networkx.drawing.nx_pylab)": [[1134, "networkx.drawing.nx_pylab.draw_networkx_edge_labels"]], "draw_networkx_edges() (in module networkx.drawing.nx_pylab)": [[1135, "networkx.drawing.nx_pylab.draw_networkx_edges"]], "draw_networkx_labels() (in module networkx.drawing.nx_pylab)": [[1136, "networkx.drawing.nx_pylab.draw_networkx_labels"]], "draw_networkx_nodes() (in module networkx.drawing.nx_pylab)": [[1137, "networkx.drawing.nx_pylab.draw_networkx_nodes"]], "draw_planar() (in module networkx.drawing.nx_pylab)": [[1138, "networkx.drawing.nx_pylab.draw_planar"]], "draw_random() (in module networkx.drawing.nx_pylab)": [[1139, "networkx.drawing.nx_pylab.draw_random"]], "draw_shell() (in module networkx.drawing.nx_pylab)": [[1140, "networkx.drawing.nx_pylab.draw_shell"]], "draw_spectral() (in module networkx.drawing.nx_pylab)": [[1141, "networkx.drawing.nx_pylab.draw_spectral"]], "draw_spring() (in module networkx.drawing.nx_pylab)": [[1142, "networkx.drawing.nx_pylab.draw_spring"]], "graph_atlas() (in module networkx.generators.atlas)": [[1143, "networkx.generators.atlas.graph_atlas"]], "graph_atlas_g() (in module networkx.generators.atlas)": [[1144, "networkx.generators.atlas.graph_atlas_g"]], "balanced_tree() (in module networkx.generators.classic)": [[1145, "networkx.generators.classic.balanced_tree"]], "barbell_graph() (in module networkx.generators.classic)": [[1146, "networkx.generators.classic.barbell_graph"]], "binomial_tree() (in module networkx.generators.classic)": [[1147, "networkx.generators.classic.binomial_tree"]], "circulant_graph() (in module networkx.generators.classic)": [[1148, "networkx.generators.classic.circulant_graph"]], "circular_ladder_graph() (in module networkx.generators.classic)": [[1149, "networkx.generators.classic.circular_ladder_graph"]], "complete_graph() (in module networkx.generators.classic)": [[1150, "networkx.generators.classic.complete_graph"]], "complete_multipartite_graph() (in module networkx.generators.classic)": [[1151, "networkx.generators.classic.complete_multipartite_graph"]], "cycle_graph() (in module networkx.generators.classic)": [[1152, 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module networkx.generators.classic)": [[1161, "networkx.generators.classic.trivial_graph"]], "turan_graph() (in module networkx.generators.classic)": [[1162, "networkx.generators.classic.turan_graph"]], "wheel_graph() (in module networkx.generators.classic)": [[1163, "networkx.generators.classic.wheel_graph"]], "random_cograph() (in module networkx.generators.cographs)": [[1164, "networkx.generators.cographs.random_cograph"]], "lfr_benchmark_graph() (in module networkx.generators.community)": [[1165, "networkx.generators.community.LFR_benchmark_graph"]], "caveman_graph() (in module networkx.generators.community)": [[1166, "networkx.generators.community.caveman_graph"]], "connected_caveman_graph() (in module networkx.generators.community)": [[1167, "networkx.generators.community.connected_caveman_graph"]], "gaussian_random_partition_graph() (in module networkx.generators.community)": [[1168, "networkx.generators.community.gaussian_random_partition_graph"]], "planted_partition_graph() 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"networkx.generators.degree_seq.degree_sequence_tree"]], "directed_configuration_model() (in module networkx.generators.degree_seq)": [[1177, "networkx.generators.degree_seq.directed_configuration_model"]], "directed_havel_hakimi_graph() (in module networkx.generators.degree_seq)": [[1178, "networkx.generators.degree_seq.directed_havel_hakimi_graph"]], "expected_degree_graph() (in module networkx.generators.degree_seq)": [[1179, "networkx.generators.degree_seq.expected_degree_graph"]], "havel_hakimi_graph() (in module networkx.generators.degree_seq)": [[1180, "networkx.generators.degree_seq.havel_hakimi_graph"]], "random_degree_sequence_graph() (in module networkx.generators.degree_seq)": [[1181, "networkx.generators.degree_seq.random_degree_sequence_graph"]], "gn_graph() (in module networkx.generators.directed)": [[1182, "networkx.generators.directed.gn_graph"]], "gnc_graph() (in module networkx.generators.directed)": [[1183, "networkx.generators.directed.gnc_graph"]], "gnr_graph() 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networkx.generators.intersection)": [[1205, "networkx.generators.intersection.uniform_random_intersection_graph"]], "interval_graph() (in module networkx.generators.interval_graph)": [[1206, "networkx.generators.interval_graph.interval_graph"]], "directed_joint_degree_graph() (in module networkx.generators.joint_degree_seq)": [[1207, "networkx.generators.joint_degree_seq.directed_joint_degree_graph"]], "is_valid_directed_joint_degree() (in module networkx.generators.joint_degree_seq)": [[1208, "networkx.generators.joint_degree_seq.is_valid_directed_joint_degree"]], "is_valid_joint_degree() (in module networkx.generators.joint_degree_seq)": [[1209, "networkx.generators.joint_degree_seq.is_valid_joint_degree"]], "joint_degree_graph() (in module networkx.generators.joint_degree_seq)": [[1210, "networkx.generators.joint_degree_seq.joint_degree_graph"]], "grid_2d_graph() (in module networkx.generators.lattice)": [[1211, "networkx.generators.lattice.grid_2d_graph"]], "grid_graph() (in module networkx.generators.lattice)": [[1212, "networkx.generators.lattice.grid_graph"]], "hexagonal_lattice_graph() (in module networkx.generators.lattice)": [[1213, "networkx.generators.lattice.hexagonal_lattice_graph"]], "hypercube_graph() (in module networkx.generators.lattice)": [[1214, "networkx.generators.lattice.hypercube_graph"]], "triangular_lattice_graph() (in module networkx.generators.lattice)": [[1215, "networkx.generators.lattice.triangular_lattice_graph"]], "inverse_line_graph() (in module networkx.generators.line)": [[1216, "networkx.generators.line.inverse_line_graph"]], "line_graph() (in module networkx.generators.line)": [[1217, "networkx.generators.line.line_graph"]], "mycielski_graph() (in module networkx.generators.mycielski)": [[1218, "networkx.generators.mycielski.mycielski_graph"]], "mycielskian() (in module networkx.generators.mycielski)": [[1219, "networkx.generators.mycielski.mycielskian"]], "nonisomorphic_trees() (in module networkx.generators.nonisomorphic_trees)": [[1220, "networkx.generators.nonisomorphic_trees.nonisomorphic_trees"]], "number_of_nonisomorphic_trees() (in module networkx.generators.nonisomorphic_trees)": [[1221, "networkx.generators.nonisomorphic_trees.number_of_nonisomorphic_trees"]], "random_clustered_graph() (in module networkx.generators.random_clustered)": [[1222, "networkx.generators.random_clustered.random_clustered_graph"]], "barabasi_albert_graph() (in module networkx.generators.random_graphs)": [[1223, "networkx.generators.random_graphs.barabasi_albert_graph"]], "binomial_graph() (in module networkx.generators.random_graphs)": [[1224, "networkx.generators.random_graphs.binomial_graph"]], "connected_watts_strogatz_graph() (in module networkx.generators.random_graphs)": [[1225, "networkx.generators.random_graphs.connected_watts_strogatz_graph"]], "dense_gnm_random_graph() (in module networkx.generators.random_graphs)": [[1226, 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"networkx.generators.random_graphs.random_shell_graph"]], "watts_strogatz_graph() (in module networkx.generators.random_graphs)": [[1241, "networkx.generators.random_graphs.watts_strogatz_graph"]], "lcf_graph() (in module networkx.generators.small)": [[1242, "networkx.generators.small.LCF_graph"]], "bull_graph() (in module networkx.generators.small)": [[1243, "networkx.generators.small.bull_graph"]], "chvatal_graph() (in module networkx.generators.small)": [[1244, "networkx.generators.small.chvatal_graph"]], "cubical_graph() (in module networkx.generators.small)": [[1245, "networkx.generators.small.cubical_graph"]], "desargues_graph() (in module networkx.generators.small)": [[1246, "networkx.generators.small.desargues_graph"]], "diamond_graph() (in module networkx.generators.small)": [[1247, "networkx.generators.small.diamond_graph"]], "dodecahedral_graph() (in module networkx.generators.small)": [[1248, "networkx.generators.small.dodecahedral_graph"]], "frucht_graph() (in module networkx.generators.small)": [[1249, "networkx.generators.small.frucht_graph"]], "heawood_graph() (in module networkx.generators.small)": [[1250, "networkx.generators.small.heawood_graph"]], "hoffman_singleton_graph() (in module networkx.generators.small)": [[1251, "networkx.generators.small.hoffman_singleton_graph"]], "house_graph() (in module networkx.generators.small)": [[1252, "networkx.generators.small.house_graph"]], "house_x_graph() (in module networkx.generators.small)": [[1253, "networkx.generators.small.house_x_graph"]], "icosahedral_graph() (in module networkx.generators.small)": [[1254, "networkx.generators.small.icosahedral_graph"]], "krackhardt_kite_graph() (in module networkx.generators.small)": [[1255, "networkx.generators.small.krackhardt_kite_graph"]], "moebius_kantor_graph() (in module networkx.generators.small)": [[1256, "networkx.generators.small.moebius_kantor_graph"]], "octahedral_graph() (in module networkx.generators.small)": [[1257, "networkx.generators.small.octahedral_graph"]], "pappus_graph() (in module networkx.generators.small)": [[1258, "networkx.generators.small.pappus_graph"]], "petersen_graph() (in module networkx.generators.small)": [[1259, "networkx.generators.small.petersen_graph"]], "sedgewick_maze_graph() (in module networkx.generators.small)": [[1260, "networkx.generators.small.sedgewick_maze_graph"]], "tetrahedral_graph() (in module networkx.generators.small)": [[1261, "networkx.generators.small.tetrahedral_graph"]], "truncated_cube_graph() (in module networkx.generators.small)": [[1262, "networkx.generators.small.truncated_cube_graph"]], "truncated_tetrahedron_graph() (in module networkx.generators.small)": [[1263, "networkx.generators.small.truncated_tetrahedron_graph"]], "tutte_graph() (in module networkx.generators.small)": [[1264, "networkx.generators.small.tutte_graph"]], "davis_southern_women_graph() (in module networkx.generators.social)": [[1265, "networkx.generators.social.davis_southern_women_graph"]], "florentine_families_graph() (in module networkx.generators.social)": [[1266, "networkx.generators.social.florentine_families_graph"]], "karate_club_graph() (in module networkx.generators.social)": [[1267, "networkx.generators.social.karate_club_graph"]], "les_miserables_graph() (in module networkx.generators.social)": [[1268, "networkx.generators.social.les_miserables_graph"]], "spectral_graph_forge() (in module networkx.generators.spectral_graph_forge)": [[1269, "networkx.generators.spectral_graph_forge.spectral_graph_forge"]], "stochastic_graph() (in module networkx.generators.stochastic)": [[1270, "networkx.generators.stochastic.stochastic_graph"]], "sudoku_graph() (in module networkx.generators.sudoku)": [[1271, "networkx.generators.sudoku.sudoku_graph"]], "prefix_tree() (in module networkx.generators.trees)": [[1272, "networkx.generators.trees.prefix_tree"]], "random_tree() (in module networkx.generators.trees)": [[1273, "networkx.generators.trees.random_tree"]], "triad_graph() (in module networkx.generators.triads)": [[1274, "networkx.generators.triads.triad_graph"]], "algebraic_connectivity() (in module networkx.linalg.algebraicconnectivity)": [[1275, "networkx.linalg.algebraicconnectivity.algebraic_connectivity"]], "fiedler_vector() (in module networkx.linalg.algebraicconnectivity)": [[1276, "networkx.linalg.algebraicconnectivity.fiedler_vector"]], "spectral_ordering() (in module networkx.linalg.algebraicconnectivity)": [[1277, "networkx.linalg.algebraicconnectivity.spectral_ordering"]], "attr_matrix() (in module networkx.linalg.attrmatrix)": [[1278, "networkx.linalg.attrmatrix.attr_matrix"]], "attr_sparse_matrix() (in module networkx.linalg.attrmatrix)": [[1279, "networkx.linalg.attrmatrix.attr_sparse_matrix"]], "bethe_hessian_matrix() (in module networkx.linalg.bethehessianmatrix)": [[1280, "networkx.linalg.bethehessianmatrix.bethe_hessian_matrix"]], "adjacency_matrix() (in module networkx.linalg.graphmatrix)": [[1281, "networkx.linalg.graphmatrix.adjacency_matrix"]], "incidence_matrix() (in module networkx.linalg.graphmatrix)": [[1282, "networkx.linalg.graphmatrix.incidence_matrix"]], "directed_combinatorial_laplacian_matrix() (in module networkx.linalg.laplacianmatrix)": [[1283, "networkx.linalg.laplacianmatrix.directed_combinatorial_laplacian_matrix"]], "directed_laplacian_matrix() (in module networkx.linalg.laplacianmatrix)": [[1284, "networkx.linalg.laplacianmatrix.directed_laplacian_matrix"]], "laplacian_matrix() (in module networkx.linalg.laplacianmatrix)": [[1285, "networkx.linalg.laplacianmatrix.laplacian_matrix"]], "normalized_laplacian_matrix() (in module networkx.linalg.laplacianmatrix)": [[1286, "networkx.linalg.laplacianmatrix.normalized_laplacian_matrix"]], "directed_modularity_matrix() (in module networkx.linalg.modularitymatrix)": [[1287, "networkx.linalg.modularitymatrix.directed_modularity_matrix"]], "modularity_matrix() (in module networkx.linalg.modularitymatrix)": [[1288, "networkx.linalg.modularitymatrix.modularity_matrix"]], "adjacency_spectrum() (in module networkx.linalg.spectrum)": [[1289, "networkx.linalg.spectrum.adjacency_spectrum"]], "bethe_hessian_spectrum() (in module networkx.linalg.spectrum)": [[1290, "networkx.linalg.spectrum.bethe_hessian_spectrum"]], "laplacian_spectrum() (in module networkx.linalg.spectrum)": [[1291, "networkx.linalg.spectrum.laplacian_spectrum"]], "modularity_spectrum() (in module networkx.linalg.spectrum)": [[1292, "networkx.linalg.spectrum.modularity_spectrum"]], "normalized_laplacian_spectrum() (in module networkx.linalg.spectrum)": [[1293, "networkx.linalg.spectrum.normalized_laplacian_spectrum"]], "convert_node_labels_to_integers() (in module networkx.relabel)": [[1294, "networkx.relabel.convert_node_labels_to_integers"]], "relabel_nodes() (in module networkx.relabel)": [[1295, "networkx.relabel.relabel_nodes"]], "__init__() (argmap method)": [[1296, "networkx.utils.decorators.argmap.__init__"]], "argmap (class in networkx.utils.decorators)": [[1296, "networkx.utils.decorators.argmap"]], "nodes_or_number() (in module networkx.utils.decorators)": [[1297, "networkx.utils.decorators.nodes_or_number"]], "not_implemented_for() (in module networkx.utils.decorators)": [[1298, "networkx.utils.decorators.not_implemented_for"]], "np_random_state() (in module networkx.utils.decorators)": [[1299, "networkx.utils.decorators.np_random_state"]], "open_file() (in module networkx.utils.decorators)": [[1300, "networkx.utils.decorators.open_file"]], "py_random_state() (in module networkx.utils.decorators)": [[1301, "networkx.utils.decorators.py_random_state"]], "mappedqueue (class in networkx.utils.mapped_queue)": [[1302, "networkx.utils.mapped_queue.MappedQueue"]], "__init__() (mappedqueue method)": [[1302, "networkx.utils.mapped_queue.MappedQueue.__init__"]], "arbitrary_element() (in module networkx.utils.misc)": [[1303, "networkx.utils.misc.arbitrary_element"]], "create_py_random_state() (in module networkx.utils.misc)": [[1304, "networkx.utils.misc.create_py_random_state"]], "create_random_state() (in module networkx.utils.misc)": [[1305, "networkx.utils.misc.create_random_state"]], "dict_to_numpy_array() (in module networkx.utils.misc)": [[1306, "networkx.utils.misc.dict_to_numpy_array"]], "edges_equal() (in module networkx.utils.misc)": [[1307, "networkx.utils.misc.edges_equal"]], "flatten() (in module networkx.utils.misc)": [[1308, "networkx.utils.misc.flatten"]], "graphs_equal() (in module networkx.utils.misc)": [[1309, "networkx.utils.misc.graphs_equal"]], "groups() (in module networkx.utils.misc)": [[1310, "networkx.utils.misc.groups"]], "make_list_of_ints() (in module networkx.utils.misc)": [[1311, "networkx.utils.misc.make_list_of_ints"]], "nodes_equal() (in module networkx.utils.misc)": [[1312, "networkx.utils.misc.nodes_equal"]], "pairwise() (in module networkx.utils.misc)": [[1313, "networkx.utils.misc.pairwise"]], "cumulative_distribution() (in module networkx.utils.random_sequence)": [[1314, "networkx.utils.random_sequence.cumulative_distribution"]], "discrete_sequence() (in module networkx.utils.random_sequence)": [[1315, "networkx.utils.random_sequence.discrete_sequence"]], "powerlaw_sequence() (in module networkx.utils.random_sequence)": [[1316, "networkx.utils.random_sequence.powerlaw_sequence"]], "random_weighted_sample() (in module networkx.utils.random_sequence)": [[1317, "networkx.utils.random_sequence.random_weighted_sample"]], "weighted_choice() (in module networkx.utils.random_sequence)": [[1318, "networkx.utils.random_sequence.weighted_choice"]], "zipf_rv() (in module networkx.utils.random_sequence)": [[1319, "networkx.utils.random_sequence.zipf_rv"]], "cuthill_mckee_ordering() (in module networkx.utils.rcm)": [[1320, "networkx.utils.rcm.cuthill_mckee_ordering"]], "reverse_cuthill_mckee_ordering() (in module networkx.utils.rcm)": [[1321, "networkx.utils.rcm.reverse_cuthill_mckee_ordering"]], "union() (unionfind method)": [[1322, "networkx.utils.union_find.UnionFind.union"]], "networkx.generators.atlas": [[1323, "module-networkx.generators.atlas"]], "networkx.generators.classic": [[1323, "module-networkx.generators.classic"]], "networkx.generators.cographs": [[1323, "module-networkx.generators.cographs"]], "networkx.generators.community": [[1323, "module-networkx.generators.community"]], "networkx.generators.degree_seq": [[1323, "module-networkx.generators.degree_seq"]], "networkx.generators.directed": [[1323, "module-networkx.generators.directed"]], "networkx.generators.duplication": [[1323, "module-networkx.generators.duplication"]], "networkx.generators.ego": [[1323, "module-networkx.generators.ego"]], "networkx.generators.expanders": [[1323, "module-networkx.generators.expanders"]], "networkx.generators.geometric": [[1323, "module-networkx.generators.geometric"]], "networkx.generators.harary_graph": [[1323, "module-networkx.generators.harary_graph"]], "networkx.generators.internet_as_graphs": [[1323, "module-networkx.generators.internet_as_graphs"]], "networkx.generators.intersection": [[1323, "module-networkx.generators.intersection"]], "networkx.generators.interval_graph": [[1323, "module-networkx.generators.interval_graph"]], "networkx.generators.joint_degree_seq": [[1323, "module-networkx.generators.joint_degree_seq"]], "networkx.generators.lattice": [[1323, "module-networkx.generators.lattice"]], "networkx.generators.line": [[1323, "module-networkx.generators.line"]], "networkx.generators.mycielski": [[1323, "module-networkx.generators.mycielski"]], "networkx.generators.nonisomorphic_trees": [[1323, "module-networkx.generators.nonisomorphic_trees"]], "networkx.generators.random_clustered": [[1323, "module-networkx.generators.random_clustered"]], "networkx.generators.random_graphs": [[1323, "module-networkx.generators.random_graphs"]], "networkx.generators.small": [[1323, "module-networkx.generators.small"]], "networkx.generators.social": [[1323, "module-networkx.generators.social"]], "networkx.generators.spectral_graph_forge": [[1323, "module-networkx.generators.spectral_graph_forge"]], "networkx.generators.stochastic": [[1323, "module-networkx.generators.stochastic"]], "networkx.generators.sudoku": [[1323, "module-networkx.generators.sudoku"]], "networkx.generators.trees": [[1323, "module-networkx.generators.trees"]], "networkx.generators.triads": [[1323, "module-networkx.generators.triads"]], "dictionary": [[1324, "term-dictionary"]], "ebunch": [[1324, "term-ebunch"]], "edge": [[1324, "term-edge"]], "edge attribute": [[1324, "term-edge-attribute"]], "nbunch": [[1324, "term-nbunch"]], "node": [[1324, "term-node"]], "node attribute": [[1324, "term-node-attribute"]], "networkx.linalg.algebraicconnectivity": [[1327, "module-networkx.linalg.algebraicconnectivity"]], "networkx.linalg.attrmatrix": [[1327, "module-networkx.linalg.attrmatrix"]], "networkx.linalg.bethehessianmatrix": [[1327, "module-networkx.linalg.bethehessianmatrix"]], "networkx.linalg.graphmatrix": [[1327, "module-networkx.linalg.graphmatrix"]], "networkx.linalg.laplacianmatrix": [[1327, "module-networkx.linalg.laplacianmatrix"]], "networkx.linalg.modularitymatrix": [[1327, "module-networkx.linalg.modularitymatrix"]], "networkx.linalg.spectrum": [[1327, "module-networkx.linalg.spectrum"]], "networkx.readwrite.adjlist": [[1329, "module-networkx.readwrite.adjlist"]], "networkx.readwrite.edgelist": [[1330, "module-networkx.readwrite.edgelist"]], "generate_adjlist() (in module networkx.readwrite.adjlist)": [[1331, "networkx.readwrite.adjlist.generate_adjlist"]], "parse_adjlist() (in module networkx.readwrite.adjlist)": [[1332, "networkx.readwrite.adjlist.parse_adjlist"]], "read_adjlist() (in module networkx.readwrite.adjlist)": [[1333, "networkx.readwrite.adjlist.read_adjlist"]], "write_adjlist() (in module networkx.readwrite.adjlist)": [[1334, "networkx.readwrite.adjlist.write_adjlist"]], "generate_edgelist() (in module networkx.readwrite.edgelist)": [[1335, "networkx.readwrite.edgelist.generate_edgelist"]], "parse_edgelist() (in module networkx.readwrite.edgelist)": [[1336, "networkx.readwrite.edgelist.parse_edgelist"]], "read_edgelist() (in module networkx.readwrite.edgelist)": [[1337, "networkx.readwrite.edgelist.read_edgelist"]], "read_weighted_edgelist() (in module networkx.readwrite.edgelist)": [[1338, "networkx.readwrite.edgelist.read_weighted_edgelist"]], "write_edgelist() (in module networkx.readwrite.edgelist)": [[1339, "networkx.readwrite.edgelist.write_edgelist"]], "write_weighted_edgelist() (in module networkx.readwrite.edgelist)": [[1340, "networkx.readwrite.edgelist.write_weighted_edgelist"]], "generate_gexf() (in module networkx.readwrite.gexf)": [[1341, "networkx.readwrite.gexf.generate_gexf"]], "read_gexf() (in module networkx.readwrite.gexf)": [[1342, "networkx.readwrite.gexf.read_gexf"]], "relabel_gexf_graph() (in module networkx.readwrite.gexf)": [[1343, "networkx.readwrite.gexf.relabel_gexf_graph"]], "write_gexf() (in module networkx.readwrite.gexf)": [[1344, "networkx.readwrite.gexf.write_gexf"]], "generate_gml() (in module networkx.readwrite.gml)": [[1345, "networkx.readwrite.gml.generate_gml"]], "literal_destringizer() (in module networkx.readwrite.gml)": [[1346, "networkx.readwrite.gml.literal_destringizer"]], "literal_stringizer() (in module networkx.readwrite.gml)": [[1347, "networkx.readwrite.gml.literal_stringizer"]], "parse_gml() (in module networkx.readwrite.gml)": [[1348, "networkx.readwrite.gml.parse_gml"]], "read_gml() (in module networkx.readwrite.gml)": [[1349, "networkx.readwrite.gml.read_gml"]], "write_gml() (in module networkx.readwrite.gml)": [[1350, "networkx.readwrite.gml.write_gml"]], "from_graph6_bytes() (in module networkx.readwrite.graph6)": [[1351, "networkx.readwrite.graph6.from_graph6_bytes"]], "read_graph6() (in module networkx.readwrite.graph6)": [[1352, "networkx.readwrite.graph6.read_graph6"]], "to_graph6_bytes() (in module networkx.readwrite.graph6)": [[1353, "networkx.readwrite.graph6.to_graph6_bytes"]], "write_graph6() (in module networkx.readwrite.graph6)": [[1354, "networkx.readwrite.graph6.write_graph6"]], "generate_graphml() (in module networkx.readwrite.graphml)": [[1355, "networkx.readwrite.graphml.generate_graphml"]], "parse_graphml() (in module networkx.readwrite.graphml)": [[1356, "networkx.readwrite.graphml.parse_graphml"]], "read_graphml() (in module networkx.readwrite.graphml)": [[1357, "networkx.readwrite.graphml.read_graphml"]], "write_graphml() (in module networkx.readwrite.graphml)": [[1358, "networkx.readwrite.graphml.write_graphml"]], "adjacency_data() (in module networkx.readwrite.json_graph)": [[1359, "networkx.readwrite.json_graph.adjacency_data"]], "adjacency_graph() (in module networkx.readwrite.json_graph)": [[1360, "networkx.readwrite.json_graph.adjacency_graph"]], "cytoscape_data() (in module networkx.readwrite.json_graph)": [[1361, "networkx.readwrite.json_graph.cytoscape_data"]], "cytoscape_graph() (in module networkx.readwrite.json_graph)": [[1362, "networkx.readwrite.json_graph.cytoscape_graph"]], "node_link_data() (in module networkx.readwrite.json_graph)": [[1363, "networkx.readwrite.json_graph.node_link_data"]], "node_link_graph() (in module networkx.readwrite.json_graph)": [[1364, "networkx.readwrite.json_graph.node_link_graph"]], "tree_data() (in module networkx.readwrite.json_graph)": [[1365, "networkx.readwrite.json_graph.tree_data"]], "tree_graph() (in module networkx.readwrite.json_graph)": [[1366, "networkx.readwrite.json_graph.tree_graph"]], "parse_leda() (in module networkx.readwrite.leda)": [[1367, "networkx.readwrite.leda.parse_leda"]], "read_leda() (in module networkx.readwrite.leda)": [[1368, "networkx.readwrite.leda.read_leda"]], "generate_multiline_adjlist() (in module networkx.readwrite.multiline_adjlist)": [[1369, "networkx.readwrite.multiline_adjlist.generate_multiline_adjlist"]], "parse_multiline_adjlist() (in module networkx.readwrite.multiline_adjlist)": [[1370, "networkx.readwrite.multiline_adjlist.parse_multiline_adjlist"]], "read_multiline_adjlist() (in module networkx.readwrite.multiline_adjlist)": [[1371, "networkx.readwrite.multiline_adjlist.read_multiline_adjlist"]], "write_multiline_adjlist() (in module networkx.readwrite.multiline_adjlist)": [[1372, "networkx.readwrite.multiline_adjlist.write_multiline_adjlist"]], "generate_pajek() (in module networkx.readwrite.pajek)": [[1373, "networkx.readwrite.pajek.generate_pajek"]], "parse_pajek() (in module networkx.readwrite.pajek)": [[1374, "networkx.readwrite.pajek.parse_pajek"]], "read_pajek() (in module networkx.readwrite.pajek)": [[1375, "networkx.readwrite.pajek.read_pajek"]], "write_pajek() (in module networkx.readwrite.pajek)": [[1376, "networkx.readwrite.pajek.write_pajek"]], "from_sparse6_bytes() (in module networkx.readwrite.sparse6)": [[1377, "networkx.readwrite.sparse6.from_sparse6_bytes"]], "read_sparse6() (in module networkx.readwrite.sparse6)": [[1378, "networkx.readwrite.sparse6.read_sparse6"]], "to_sparse6_bytes() (in module networkx.readwrite.sparse6)": [[1379, "networkx.readwrite.sparse6.to_sparse6_bytes"]], "write_sparse6() (in module networkx.readwrite.sparse6)": [[1380, "networkx.readwrite.sparse6.write_sparse6"]], "networkx.readwrite.gexf": [[1381, "module-networkx.readwrite.gexf"]], "networkx.readwrite.gml": [[1382, "module-networkx.readwrite.gml"]], "networkx.readwrite.graphml": [[1383, "module-networkx.readwrite.graphml"]], "networkx.readwrite.json_graph": [[1385, "module-networkx.readwrite.json_graph"]], "networkx.readwrite.leda": [[1386, "module-networkx.readwrite.leda"]], "networkx.readwrite.multiline_adjlist": [[1388, "module-networkx.readwrite.multiline_adjlist"]], "networkx.readwrite.pajek": [[1389, "module-networkx.readwrite.pajek"]], "networkx.readwrite.graph6": [[1390, "module-networkx.readwrite.graph6"]], "networkx.readwrite.sparse6": [[1390, "module-networkx.readwrite.sparse6"]], "networkx.relabel": [[1391, "module-networkx.relabel"]], "networkx.utils": [[1392, "module-networkx.utils"]], "networkx.utils.decorators": [[1392, "module-networkx.utils.decorators"]], "networkx.utils.mapped_queue": [[1392, "module-networkx.utils.mapped_queue"]], "networkx.utils.misc": [[1392, "module-networkx.utils.misc"]], "networkx.utils.random_sequence": [[1392, "module-networkx.utils.random_sequence"]], "networkx.utils.rcm": [[1392, "module-networkx.utils.rcm"]], "networkx.utils.union_find": [[1392, "module-networkx.utils.union_find"]]}})
\ No newline at end of file diff --git a/tutorial-34.pdf b/tutorial-34.pdf Binary files differindex 75c5e84a..79359703 100644 --- a/tutorial-34.pdf +++ b/tutorial-34.pdf diff --git a/tutorial-35.hires.png b/tutorial-35.hires.png Binary files differindex 51b54532..95fa708b 100644 --- a/tutorial-35.hires.png +++ b/tutorial-35.hires.png diff --git a/tutorial-35.pdf b/tutorial-35.pdf Binary files differindex 1ce89295..046fae41 100644 --- a/tutorial-35.pdf +++ b/tutorial-35.pdf diff --git a/tutorial-35.png b/tutorial-35.png Binary files differindex 5ae1b78d..d7651092 100644 --- a/tutorial-35.png +++ b/tutorial-35.png diff --git a/tutorial-36.pdf b/tutorial-36.pdf Binary files differindex 0fb18923..82480046 100644 --- a/tutorial-36.pdf +++ b/tutorial-36.pdf diff --git a/tutorial.ipynb b/tutorial.ipynb index d81d1d09..19d2dbc3 100644 --- a/tutorial.ipynb +++ b/tutorial.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "e94e9fa9", + "id": "312377de", "metadata": {}, "source": [ "## Tutorial\n", @@ -17,7 +17,7 @@ { "cell_type": "code", "execution_count": null, - "id": "a229eaa1", + "id": "ada53fd7", "metadata": {}, "outputs": [], "source": [ @@ -27,7 +27,7 @@ }, { "cell_type": "markdown", - "id": "fe98dbb0", + "id": "861ed04b", "metadata": {}, "source": [ "By definition, a `Graph` is a collection of nodes (vertices) along with\n", @@ -47,7 +47,7 @@ { "cell_type": "code", "execution_count": null, - "id": "8b198e62", + "id": "b9cad2ae", "metadata": {}, "outputs": [], "source": [ @@ -56,7 +56,7 @@ }, { "cell_type": "markdown", - "id": "5eebe239", + "id": "ee92c95e", "metadata": {}, "source": [ "or add nodes from any [iterable](https://docs.python.org/3/glossary.html#term-iterable) container, such as a list" @@ -65,7 +65,7 @@ { "cell_type": "code", "execution_count": null, - "id": "f983e2e2", + "id": "c0e405fc", "metadata": {}, "outputs": [], "source": [ @@ -74,7 +74,7 @@ }, { "cell_type": "markdown", - "id": "ed7ca5a6", + "id": "f8433d62", "metadata": {}, "source": [ "You can also add nodes along with node\n", @@ -96,7 +96,7 @@ { "cell_type": "code", "execution_count": null, - "id": "0e97adf2", + "id": "8ad10d76", "metadata": {}, "outputs": [], "source": [ @@ -106,7 +106,7 @@ }, { "cell_type": "markdown", - "id": "f2460766", + "id": "4b290abf", "metadata": {}, "source": [ "`G` now contains the nodes of `H` as nodes of `G`.\n", @@ -116,7 +116,7 @@ { "cell_type": "code", "execution_count": null, - "id": "58bf775f", + "id": "6b6f23d6", "metadata": {}, "outputs": [], "source": [ @@ -125,7 +125,7 @@ }, { "cell_type": "markdown", - "id": "5bafb2aa", + "id": "3e80029a", "metadata": {}, "source": [ "The graph `G` now contains `H` as a node. This flexibility is very powerful as\n", @@ -143,7 +143,7 @@ { "cell_type": "code", "execution_count": null, - "id": "6e83f2e8", + "id": "36508268", "metadata": {}, "outputs": [], "source": [ @@ -154,7 +154,7 @@ }, { "cell_type": "markdown", - "id": "bf6c3bd4", + "id": "04ef8bcd", "metadata": {}, "source": [ "by adding a list of edges," @@ -163,7 +163,7 @@ { "cell_type": "code", "execution_count": null, - "id": "77dafe76", + "id": "8d12d16a", "metadata": {}, "outputs": [], "source": [ @@ -172,7 +172,7 @@ }, { "cell_type": "markdown", - "id": "aa532e9e", + "id": "561aac0b", "metadata": {}, "source": [ "or by adding any ebunch of edges. An *ebunch* is any iterable\n", @@ -185,7 +185,7 @@ { "cell_type": "code", "execution_count": null, - "id": "98cca3d2", + "id": "b5cd9094", "metadata": {}, "outputs": [], "source": [ @@ -194,7 +194,7 @@ }, { "cell_type": "markdown", - "id": "2ed337ea", + "id": "20d91c24", "metadata": {}, "source": [ "There are no complaints when adding existing nodes or edges. For example,\n", @@ -204,7 +204,7 @@ { "cell_type": "code", "execution_count": null, - "id": "85d84eb0", + "id": "911bef45", "metadata": {}, "outputs": [], "source": [ @@ -213,7 +213,7 @@ }, { "cell_type": "markdown", - "id": "0a2e3f8a", + "id": "1479e67f", "metadata": {}, "source": [ "we add new nodes/edges and NetworkX quietly ignores any that are\n", @@ -223,7 +223,7 @@ { "cell_type": "code", "execution_count": null, - "id": "366065d8", + "id": "9876d72a", "metadata": {}, "outputs": [], "source": [ @@ -237,7 +237,7 @@ }, { "cell_type": "markdown", - "id": "84d014fb", + "id": "1b6509d6", "metadata": {}, "source": [ "At this stage the graph `G` consists of 8 nodes and 3 edges, as can be seen by:" @@ -246,7 +246,7 @@ { "cell_type": "code", "execution_count": null, - "id": "4add5e21", + "id": "e6d84a52", "metadata": {}, "outputs": [], "source": [ @@ -257,7 +257,7 @@ { "cell_type": "code", "execution_count": null, - "id": "7163af50", + "id": "5e59c941", "metadata": {}, "outputs": [], "source": [ @@ -272,7 +272,7 @@ }, { "cell_type": "markdown", - "id": "24f0059e", + "id": "fda7f705", "metadata": {}, "source": [ "# Examining elements of a graph\n", @@ -292,7 +292,7 @@ { "cell_type": "code", "execution_count": null, - "id": "b1b8ecad", + "id": "ec11a1d2", "metadata": {}, "outputs": [], "source": [ @@ -304,7 +304,7 @@ }, { "cell_type": "markdown", - "id": "1ad3b3af", + "id": "1c4c6ba0", "metadata": {}, "source": [ "One can specify to report the edges and degree from a subset of all nodes\n", @@ -316,7 +316,7 @@ { "cell_type": "code", "execution_count": null, - "id": "33627768", + "id": "5727a553", "metadata": {}, "outputs": [], "source": [ @@ -326,7 +326,7 @@ }, { "cell_type": "markdown", - "id": "80f5aa84", + "id": "13c35913", "metadata": {}, "source": [ "# Removing elements from a graph\n", @@ -343,7 +343,7 @@ { "cell_type": "code", "execution_count": null, - "id": "38808384", + "id": "dfbfa9de", "metadata": {}, "outputs": [], "source": [ @@ -355,7 +355,7 @@ }, { "cell_type": "markdown", - "id": "12955ef4", + "id": "9323e65e", "metadata": {}, "source": [ "# Using the graph constructors\n", @@ -370,7 +370,7 @@ { "cell_type": "code", "execution_count": null, - "id": "6e1176ac", + "id": "2f77fb1a", "metadata": {}, "outputs": [], "source": [ @@ -387,7 +387,7 @@ }, { "cell_type": "markdown", - "id": "ac25aeb4", + "id": "5ea52d3f", "metadata": {}, "source": [ "# What to use as nodes and edges\n", @@ -416,7 +416,7 @@ { "cell_type": "code", "execution_count": null, - "id": "1d6d8fb3", + "id": "2f3a3154", "metadata": {}, "outputs": [], "source": [ @@ -428,7 +428,7 @@ }, { "cell_type": "markdown", - "id": "dc6c043f", + "id": "15fe4901", "metadata": {}, "source": [ "You can get/set the attributes of an edge using subscript notation\n", @@ -438,7 +438,7 @@ { "cell_type": "code", "execution_count": null, - "id": "81f5148a", + "id": "d341804f", "metadata": {}, "outputs": [], "source": [ @@ -450,7 +450,7 @@ }, { "cell_type": "markdown", - "id": "9747bd1c", + "id": "646578b4", "metadata": {}, "source": [ "Fast examination of all (node, adjacency) pairs is achieved using\n", @@ -461,7 +461,7 @@ { "cell_type": "code", "execution_count": null, - "id": "b3ebc5ff", + "id": "b90f8cb5", "metadata": {}, "outputs": [], "source": [ @@ -475,7 +475,7 @@ }, { "cell_type": "markdown", - "id": "f6b61148", + "id": "4da363b8", "metadata": {}, "source": [ "Convenient access to all edges is achieved with the edges property." @@ -484,7 +484,7 @@ { "cell_type": "code", "execution_count": null, - "id": "b3cb7f30", + "id": "5a312550", "metadata": {}, "outputs": [], "source": [ @@ -495,7 +495,7 @@ }, { "cell_type": "markdown", - "id": "366448ac", + "id": "94a3558c", "metadata": {}, "source": [ "# Adding attributes to graphs, nodes, and edges\n", @@ -517,7 +517,7 @@ { "cell_type": "code", "execution_count": null, - "id": "c4171db9", + "id": "0d2ce790", "metadata": {}, "outputs": [], "source": [ @@ -527,7 +527,7 @@ }, { "cell_type": "markdown", - "id": "39a5e273", + "id": "81df7088", "metadata": {}, "source": [ "Or you can modify attributes later" @@ -536,7 +536,7 @@ { "cell_type": "code", "execution_count": null, - "id": "7f926fcf", + "id": "5a139b3e", "metadata": {}, "outputs": [], "source": [ @@ -546,7 +546,7 @@ }, { "cell_type": "markdown", - "id": "7448bf4e", + "id": "0fd0e7ac", "metadata": {}, "source": [ "# Node attributes\n", @@ -557,7 +557,7 @@ { "cell_type": "code", "execution_count": null, - "id": "79c57af5", + "id": "c0a9ec59", "metadata": {}, "outputs": [], "source": [ @@ -570,7 +570,7 @@ }, { "cell_type": "markdown", - "id": "b7346935", + "id": "81609bde", "metadata": {}, "source": [ "Note that adding a node to `G.nodes` does not add it to the graph, use\n", @@ -585,7 +585,7 @@ { "cell_type": "code", "execution_count": null, - "id": "8d4375dd", + "id": "bd32be4d", "metadata": {}, "outputs": [], "source": [ @@ -598,7 +598,7 @@ }, { "cell_type": "markdown", - "id": "6627979e", + "id": "05ce04fd", "metadata": {}, "source": [ "The special attribute `weight` should be numeric as it is used by\n", @@ -619,7 +619,7 @@ { "cell_type": "code", "execution_count": null, - "id": "6dc40410", + "id": "a47da3f3", "metadata": {}, "outputs": [], "source": [ @@ -633,7 +633,7 @@ }, { "cell_type": "markdown", - "id": "a7506961", + "id": "1b6bc284", "metadata": {}, "source": [ "Some algorithms work only for directed graphs and others are not well\n", @@ -646,7 +646,7 @@ { "cell_type": "code", "execution_count": null, - "id": "66c55ec9", + "id": "7f61d304", "metadata": {}, "outputs": [], "source": [ @@ -655,7 +655,7 @@ }, { "cell_type": "markdown", - "id": "283d6899", + "id": "5cdde51b", "metadata": {}, "source": [ "# Multigraphs\n", @@ -675,7 +675,7 @@ { "cell_type": "code", "execution_count": null, - "id": "1d408dce", + "id": "3d1a9e24", "metadata": {}, "outputs": [], "source": [ @@ -693,7 +693,7 @@ }, { "cell_type": "markdown", - "id": "0b95c68e", + "id": "a5588bf3", "metadata": {}, "source": [ "# Graph generators and graph operations\n", @@ -713,7 +713,7 @@ { "cell_type": "code", "execution_count": null, - "id": "02686d3c", + "id": "2187cfc0", "metadata": {}, "outputs": [], "source": [ @@ -725,7 +725,7 @@ }, { "cell_type": "markdown", - "id": "051f39a8", + "id": "dca58439", "metadata": {}, "source": [ "# 4. Using a stochastic graph generator, e.g,\n", @@ -736,7 +736,7 @@ { "cell_type": "code", "execution_count": null, - "id": "064eaaa3", + "id": "80c96d5f", "metadata": {}, "outputs": [], "source": [ @@ -748,7 +748,7 @@ }, { "cell_type": "markdown", - "id": "e98ae0ab", + "id": "9164e590", "metadata": {}, "source": [ "# 5. Reading a graph stored in a file using common graph formats\n", @@ -760,7 +760,7 @@ { "cell_type": "code", "execution_count": null, - "id": "06e2199a", + "id": "73a4bcd5", "metadata": {}, "outputs": [], "source": [ @@ -770,7 +770,7 @@ }, { "cell_type": "markdown", - "id": "7cb6eafd", + "id": "4fb6f129", "metadata": {}, "source": [ "For details on graph formats see Reading and writing graphs\n", @@ -785,7 +785,7 @@ { "cell_type": "code", "execution_count": null, - "id": "3629c227", + "id": "89af8ee4", "metadata": {}, "outputs": [], "source": [ @@ -799,7 +799,7 @@ }, { "cell_type": "markdown", - "id": "3dee69eb", + "id": "72fa2e2c", "metadata": {}, "source": [ "Some functions with large output iterate over (node, value) 2-tuples.\n", @@ -809,7 +809,7 @@ { "cell_type": "code", "execution_count": null, - "id": "9c1e726c", + "id": "858fbc37", "metadata": {}, "outputs": [], "source": [ @@ -819,7 +819,7 @@ }, { "cell_type": "markdown", - "id": "cc26616b", + "id": "17a4943b", "metadata": {}, "source": [ "See Algorithms for details on graph algorithms\n", @@ -838,7 +838,7 @@ { "cell_type": "code", "execution_count": null, - "id": "2bb836ef", + "id": "7edb9a44", "metadata": {}, "outputs": [], "source": [ @@ -847,7 +847,7 @@ }, { "cell_type": "markdown", - "id": "b1d71ebf", + "id": "6c277f0c", "metadata": {}, "source": [ "To test if the import of `nx_pylab` was successful draw `G`\n", @@ -857,7 +857,7 @@ { "cell_type": "code", "execution_count": null, - "id": "7f78afc5", + "id": "5784854d", "metadata": {}, "outputs": [], "source": [ @@ -870,7 +870,7 @@ }, { "cell_type": "markdown", - "id": "40b05aaf", + "id": "0f58701d", "metadata": {}, "source": [ "when drawing to an interactive display. Note that you may need to issue a\n", @@ -880,7 +880,7 @@ { "cell_type": "code", "execution_count": null, - "id": "6aa13f9c", + "id": "184f747d", "metadata": {}, "outputs": [], "source": [ @@ -889,7 +889,7 @@ }, { "cell_type": "markdown", - "id": "cdb34214", + "id": "5bec10f4", "metadata": {}, "source": [ "command if you are not using matplotlib in interactive mode." @@ -898,7 +898,7 @@ { "cell_type": "code", "execution_count": null, - "id": "6bae2d6b", + "id": "e626beb2", "metadata": {}, "outputs": [], "source": [ @@ -919,7 +919,7 @@ }, { "cell_type": "markdown", - "id": "bd41ce21", + "id": "7979eef0", "metadata": {}, "source": [ "You can find additional options via `draw_networkx()` and\n", @@ -930,7 +930,7 @@ { "cell_type": "code", "execution_count": null, - "id": "c8483998", + "id": "1fd1a0fa", "metadata": {}, "outputs": [], "source": [ @@ -941,7 +941,7 @@ }, { "cell_type": "markdown", - "id": "e3525408", + "id": "5abfdb9a", "metadata": {}, "source": [ "To save drawings to a file, use, for example" @@ -950,7 +950,7 @@ { "cell_type": "code", "execution_count": null, - "id": "5f16fecb", + "id": "a6a694ad", "metadata": {}, "outputs": [], "source": [ @@ -960,7 +960,7 @@ }, { "cell_type": "markdown", - "id": "5eccd196", + "id": "11ba58bc", "metadata": {}, "source": [ "This function writes to the file `path.png` in the local directory. If Graphviz and\n", @@ -973,7 +973,7 @@ { "cell_type": "code", "execution_count": null, - "id": "49be0aff", + "id": "cf6bedc9", "metadata": {}, "outputs": [], "source": [ @@ -985,7 +985,7 @@ }, { "cell_type": "markdown", - "id": "7601eacf", + "id": "dca97229", "metadata": {}, "source": [ "See Drawing for additional details." diff --git a/tutorial_full.ipynb b/tutorial_full.ipynb index 022ea528..fd8d355f 100644 --- a/tutorial_full.ipynb +++ b/tutorial_full.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "e94e9fa9", + "id": "312377de", "metadata": {}, "source": [ "## Tutorial\n", @@ -17,13 +17,13 @@ { "cell_type": "code", "execution_count": 1, - "id": "a229eaa1", + "id": "ada53fd7", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T21:20:11.517279Z", - "iopub.status.busy": "2023-01-03T21:20:11.516765Z", - "iopub.status.idle": "2023-01-03T21:20:11.611529Z", - "shell.execute_reply": "2023-01-03T21:20:11.610444Z" + "iopub.execute_input": "2023-01-04T02:02:32.443436Z", + "iopub.status.busy": "2023-01-04T02:02:32.443016Z", + "iopub.status.idle": "2023-01-04T02:02:32.519371Z", + "shell.execute_reply": "2023-01-04T02:02:32.518645Z" } }, "outputs": [], @@ -34,7 +34,7 @@ }, { "cell_type": "markdown", - "id": "fe98dbb0", + "id": "861ed04b", "metadata": {}, "source": [ "By definition, a `Graph` is a collection of nodes (vertices) along with\n", @@ -54,13 +54,13 @@ { "cell_type": "code", "execution_count": 2, - "id": "8b198e62", + "id": "b9cad2ae", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T21:20:11.616295Z", - "iopub.status.busy": "2023-01-03T21:20:11.616011Z", - "iopub.status.idle": "2023-01-03T21:20:11.619858Z", - "shell.execute_reply": "2023-01-03T21:20:11.619092Z" + "iopub.execute_input": "2023-01-04T02:02:32.523307Z", + "iopub.status.busy": "2023-01-04T02:02:32.523062Z", + "iopub.status.idle": "2023-01-04T02:02:32.526242Z", + "shell.execute_reply": "2023-01-04T02:02:32.525585Z" } }, "outputs": [], @@ -70,7 +70,7 @@ }, { "cell_type": "markdown", - "id": "5eebe239", + "id": "ee92c95e", "metadata": {}, "source": [ "or add nodes from any [iterable](https://docs.python.org/3/glossary.html#term-iterable) container, such as a list" @@ -79,13 +79,13 @@ { "cell_type": "code", "execution_count": 3, - "id": "f983e2e2", + "id": "c0e405fc", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T21:20:11.623539Z", - "iopub.status.busy": "2023-01-03T21:20:11.623279Z", - "iopub.status.idle": "2023-01-03T21:20:11.626811Z", - "shell.execute_reply": "2023-01-03T21:20:11.625917Z" + "iopub.execute_input": "2023-01-04T02:02:32.529414Z", + "iopub.status.busy": "2023-01-04T02:02:32.529183Z", + "iopub.status.idle": "2023-01-04T02:02:32.532314Z", + "shell.execute_reply": "2023-01-04T02:02:32.531647Z" } }, "outputs": [], @@ -95,7 +95,7 @@ }, { "cell_type": "markdown", - "id": "ed7ca5a6", + "id": "f8433d62", "metadata": {}, "source": [ "You can also add nodes along with node\n", @@ -117,13 +117,13 @@ { "cell_type": "code", "execution_count": 4, - "id": "0e97adf2", + "id": "8ad10d76", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T21:20:11.630627Z", - "iopub.status.busy": "2023-01-03T21:20:11.630296Z", - "iopub.status.idle": "2023-01-03T21:20:11.634526Z", - "shell.execute_reply": "2023-01-03T21:20:11.633742Z" + "iopub.execute_input": "2023-01-04T02:02:32.535543Z", + "iopub.status.busy": "2023-01-04T02:02:32.535303Z", + "iopub.status.idle": "2023-01-04T02:02:32.538802Z", + "shell.execute_reply": "2023-01-04T02:02:32.538176Z" } }, "outputs": [], @@ -134,7 +134,7 @@ }, { "cell_type": "markdown", - "id": "f2460766", + "id": "4b290abf", "metadata": {}, "source": [ "`G` now contains the nodes of `H` as nodes of `G`.\n", @@ -144,13 +144,13 @@ { "cell_type": "code", "execution_count": 5, - "id": "58bf775f", + "id": "6b6f23d6", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T21:20:11.638139Z", - "iopub.status.busy": "2023-01-03T21:20:11.637837Z", - "iopub.status.idle": "2023-01-03T21:20:11.641492Z", - "shell.execute_reply": "2023-01-03T21:20:11.640549Z" + "iopub.execute_input": "2023-01-04T02:02:32.541828Z", + "iopub.status.busy": "2023-01-04T02:02:32.541617Z", + "iopub.status.idle": "2023-01-04T02:02:32.544513Z", + "shell.execute_reply": "2023-01-04T02:02:32.543882Z" } }, "outputs": [], @@ -160,7 +160,7 @@ }, { "cell_type": "markdown", - "id": "5bafb2aa", + "id": "3e80029a", "metadata": {}, "source": [ "The graph `G` now contains `H` as a node. This flexibility is very powerful as\n", @@ -178,13 +178,13 @@ { "cell_type": "code", "execution_count": 6, - "id": "6e83f2e8", + "id": "36508268", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T21:20:11.649435Z", - "iopub.status.busy": "2023-01-03T21:20:11.646640Z", - "iopub.status.idle": "2023-01-03T21:20:11.653187Z", - "shell.execute_reply": "2023-01-03T21:20:11.652163Z" + "iopub.execute_input": "2023-01-04T02:02:32.547715Z", + "iopub.status.busy": "2023-01-04T02:02:32.547498Z", + "iopub.status.idle": "2023-01-04T02:02:32.550635Z", + "shell.execute_reply": "2023-01-04T02:02:32.550006Z" } }, "outputs": [], @@ -196,7 +196,7 @@ }, { "cell_type": "markdown", - "id": "bf6c3bd4", + "id": "04ef8bcd", "metadata": {}, "source": [ "by adding a list of edges," @@ -205,13 +205,13 @@ { "cell_type": "code", "execution_count": 7, - "id": "77dafe76", + "id": "8d12d16a", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T21:20:11.656885Z", - "iopub.status.busy": "2023-01-03T21:20:11.656635Z", - "iopub.status.idle": "2023-01-03T21:20:11.660189Z", - "shell.execute_reply": "2023-01-03T21:20:11.659550Z" + "iopub.execute_input": "2023-01-04T02:02:32.553720Z", + "iopub.status.busy": "2023-01-04T02:02:32.553510Z", + "iopub.status.idle": "2023-01-04T02:02:32.556625Z", + "shell.execute_reply": "2023-01-04T02:02:32.555981Z" } }, "outputs": [], @@ -221,7 +221,7 @@ }, { "cell_type": "markdown", - "id": "aa532e9e", + "id": "561aac0b", "metadata": {}, "source": [ "or by adding any ebunch of edges. An *ebunch* is any iterable\n", @@ -234,13 +234,13 @@ { "cell_type": "code", "execution_count": 8, - "id": "98cca3d2", + "id": "b5cd9094", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T21:20:11.664187Z", - "iopub.status.busy": "2023-01-03T21:20:11.663951Z", - "iopub.status.idle": "2023-01-03T21:20:11.667656Z", - "shell.execute_reply": "2023-01-03T21:20:11.666671Z" + "iopub.execute_input": "2023-01-04T02:02:32.559812Z", + "iopub.status.busy": "2023-01-04T02:02:32.559594Z", + "iopub.status.idle": "2023-01-04T02:02:32.562630Z", + "shell.execute_reply": "2023-01-04T02:02:32.561969Z" } }, "outputs": [], @@ -250,7 +250,7 @@ }, { "cell_type": "markdown", - "id": "2ed337ea", + "id": "20d91c24", "metadata": {}, "source": [ "There are no complaints when adding existing nodes or edges. For example,\n", @@ -260,13 +260,13 @@ { "cell_type": "code", "execution_count": 9, - "id": "85d84eb0", + "id": "911bef45", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T21:20:11.671619Z", - "iopub.status.busy": "2023-01-03T21:20:11.671312Z", - "iopub.status.idle": "2023-01-03T21:20:11.675608Z", - "shell.execute_reply": "2023-01-03T21:20:11.674778Z" + "iopub.execute_input": "2023-01-04T02:02:32.565780Z", + "iopub.status.busy": "2023-01-04T02:02:32.565553Z", + "iopub.status.idle": "2023-01-04T02:02:32.568452Z", + "shell.execute_reply": "2023-01-04T02:02:32.567829Z" } }, "outputs": [], @@ -276,7 +276,7 @@ }, { "cell_type": "markdown", - "id": "0a2e3f8a", + "id": "1479e67f", "metadata": {}, "source": [ "we add new nodes/edges and NetworkX quietly ignores any that are\n", @@ -286,13 +286,13 @@ { "cell_type": "code", "execution_count": 10, - "id": "366065d8", + "id": "9876d72a", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T21:20:11.679800Z", - "iopub.status.busy": "2023-01-03T21:20:11.679482Z", - "iopub.status.idle": "2023-01-03T21:20:11.684503Z", - "shell.execute_reply": "2023-01-03T21:20:11.683571Z" + "iopub.execute_input": "2023-01-04T02:02:32.571835Z", + "iopub.status.busy": "2023-01-04T02:02:32.571620Z", + "iopub.status.idle": "2023-01-04T02:02:32.575417Z", + "shell.execute_reply": "2023-01-04T02:02:32.574920Z" } }, "outputs": [], @@ -307,7 +307,7 @@ }, { "cell_type": "markdown", - "id": "84d014fb", + "id": "1b6509d6", "metadata": {}, "source": [ "At this stage the graph `G` consists of 8 nodes and 3 edges, as can be seen by:" @@ -316,13 +316,13 @@ { "cell_type": "code", "execution_count": 11, - "id": "4add5e21", + "id": "e6d84a52", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T21:20:11.688080Z", - "iopub.status.busy": "2023-01-03T21:20:11.687807Z", - "iopub.status.idle": "2023-01-03T21:20:11.696717Z", - "shell.execute_reply": "2023-01-03T21:20:11.695793Z" + "iopub.execute_input": "2023-01-04T02:02:32.578586Z", + "iopub.status.busy": "2023-01-04T02:02:32.578164Z", + "iopub.status.idle": "2023-01-04T02:02:32.585024Z", + "shell.execute_reply": "2023-01-04T02:02:32.584388Z" } }, "outputs": [ @@ -345,13 +345,13 @@ { "cell_type": "code", "execution_count": 12, - "id": "7163af50", + "id": "5e59c941", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T21:20:11.702535Z", - "iopub.status.busy": "2023-01-03T21:20:11.701800Z", - "iopub.status.idle": "2023-01-03T21:20:11.708943Z", - "shell.execute_reply": "2023-01-03T21:20:11.708041Z" + "iopub.execute_input": "2023-01-04T02:02:32.589666Z", + "iopub.status.busy": "2023-01-04T02:02:32.589201Z", + "iopub.status.idle": "2023-01-04T02:02:32.593849Z", + "shell.execute_reply": "2023-01-04T02:02:32.593155Z" } }, "outputs": [], @@ -367,7 +367,7 @@ }, { "cell_type": "markdown", - "id": "24f0059e", + "id": "fda7f705", "metadata": {}, "source": [ "# Examining elements of a graph\n", @@ -387,13 +387,13 @@ { "cell_type": "code", "execution_count": 13, - "id": "b1b8ecad", + "id": "ec11a1d2", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T21:20:11.712984Z", - "iopub.status.busy": "2023-01-03T21:20:11.712299Z", - "iopub.status.idle": "2023-01-03T21:20:11.718249Z", - "shell.execute_reply": "2023-01-03T21:20:11.717361Z" + "iopub.execute_input": "2023-01-04T02:02:32.597138Z", + "iopub.status.busy": "2023-01-04T02:02:32.596704Z", + "iopub.status.idle": "2023-01-04T02:02:32.601613Z", + "shell.execute_reply": "2023-01-04T02:02:32.600956Z" } }, "outputs": [ @@ -417,7 +417,7 @@ }, { "cell_type": "markdown", - "id": "1ad3b3af", + "id": "1c4c6ba0", "metadata": {}, "source": [ "One can specify to report the edges and degree from a subset of all nodes\n", @@ -429,13 +429,13 @@ { "cell_type": "code", "execution_count": 14, - "id": "33627768", + "id": "5727a553", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T21:20:11.723590Z", - "iopub.status.busy": "2023-01-03T21:20:11.723050Z", - "iopub.status.idle": "2023-01-03T21:20:11.729191Z", - "shell.execute_reply": "2023-01-03T21:20:11.728304Z" + "iopub.execute_input": "2023-01-04T02:02:32.605900Z", + "iopub.status.busy": "2023-01-04T02:02:32.605302Z", + "iopub.status.idle": "2023-01-04T02:02:32.609955Z", + "shell.execute_reply": "2023-01-04T02:02:32.609332Z" } }, "outputs": [ @@ -457,7 +457,7 @@ }, { "cell_type": "markdown", - "id": "80f5aa84", + "id": "13c35913", "metadata": {}, "source": [ "# Removing elements from a graph\n", @@ -474,13 +474,13 @@ { "cell_type": "code", "execution_count": 15, - "id": "38808384", + "id": "dfbfa9de", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T21:20:11.734382Z", - "iopub.status.busy": "2023-01-03T21:20:11.733993Z", - "iopub.status.idle": "2023-01-03T21:20:11.738282Z", - "shell.execute_reply": "2023-01-03T21:20:11.737466Z" + "iopub.execute_input": "2023-01-04T02:02:32.612939Z", + "iopub.status.busy": "2023-01-04T02:02:32.612719Z", + "iopub.status.idle": "2023-01-04T02:02:32.616120Z", + "shell.execute_reply": "2023-01-04T02:02:32.615506Z" } }, "outputs": [], @@ -493,7 +493,7 @@ }, { "cell_type": "markdown", - "id": "12955ef4", + "id": "9323e65e", "metadata": {}, "source": [ "# Using the graph constructors\n", @@ -508,13 +508,13 @@ { "cell_type": "code", "execution_count": 16, - "id": "6e1176ac", + "id": "2f77fb1a", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T21:20:11.742182Z", - "iopub.status.busy": "2023-01-03T21:20:11.741699Z", - "iopub.status.idle": "2023-01-03T21:20:12.076557Z", - "shell.execute_reply": "2023-01-03T21:20:12.075388Z" + "iopub.execute_input": "2023-01-04T02:02:32.618847Z", + "iopub.status.busy": "2023-01-04T02:02:32.618630Z", + "iopub.status.idle": "2023-01-04T02:02:32.896706Z", + "shell.execute_reply": "2023-01-04T02:02:32.896002Z" } }, "outputs": [ @@ -543,7 +543,7 @@ }, { "cell_type": "markdown", - "id": "ac25aeb4", + "id": "5ea52d3f", "metadata": {}, "source": [ "# What to use as nodes and edges\n", @@ -572,13 +572,13 @@ { "cell_type": "code", "execution_count": 17, - "id": "1d6d8fb3", + "id": "2f3a3154", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T21:20:12.080317Z", - "iopub.status.busy": "2023-01-03T21:20:12.079923Z", - "iopub.status.idle": "2023-01-03T21:20:12.088033Z", - "shell.execute_reply": "2023-01-03T21:20:12.087097Z" + "iopub.execute_input": "2023-01-04T02:02:32.900283Z", + "iopub.status.busy": "2023-01-04T02:02:32.899925Z", + "iopub.status.idle": "2023-01-04T02:02:32.906496Z", + "shell.execute_reply": "2023-01-04T02:02:32.905851Z" } }, "outputs": [ @@ -602,7 +602,7 @@ }, { "cell_type": "markdown", - "id": "dc6c043f", + "id": "15fe4901", "metadata": {}, "source": [ "You can get/set the attributes of an edge using subscript notation\n", @@ -612,13 +612,13 @@ { "cell_type": "code", "execution_count": 18, - "id": "81f5148a", + "id": "d341804f", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T21:20:12.091873Z", - "iopub.status.busy": "2023-01-03T21:20:12.091596Z", - "iopub.status.idle": "2023-01-03T21:20:12.097372Z", - "shell.execute_reply": "2023-01-03T21:20:12.096381Z" + "iopub.execute_input": "2023-01-04T02:02:32.909558Z", + "iopub.status.busy": "2023-01-04T02:02:32.909118Z", + "iopub.status.idle": "2023-01-04T02:02:32.914120Z", + "shell.execute_reply": "2023-01-04T02:02:32.913490Z" } }, "outputs": [ @@ -642,7 +642,7 @@ }, { "cell_type": "markdown", - "id": "9747bd1c", + "id": "646578b4", "metadata": {}, "source": [ "Fast examination of all (node, adjacency) pairs is achieved using\n", @@ -653,13 +653,13 @@ { "cell_type": "code", "execution_count": 19, - "id": "b3ebc5ff", + "id": "b90f8cb5", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T21:20:12.102309Z", - "iopub.status.busy": "2023-01-03T21:20:12.101977Z", - "iopub.status.idle": "2023-01-03T21:20:12.108489Z", - "shell.execute_reply": "2023-01-03T21:20:12.107799Z" + "iopub.execute_input": "2023-01-04T02:02:32.917978Z", + "iopub.status.busy": "2023-01-04T02:02:32.917451Z", + "iopub.status.idle": "2023-01-04T02:02:32.922509Z", + "shell.execute_reply": "2023-01-04T02:02:32.921863Z" } }, "outputs": [ @@ -685,7 +685,7 @@ }, { "cell_type": "markdown", - "id": "f6b61148", + "id": "4da363b8", "metadata": {}, "source": [ "Convenient access to all edges is achieved with the edges property." @@ -694,13 +694,13 @@ { "cell_type": "code", "execution_count": 20, - "id": "b3cb7f30", + "id": "5a312550", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T21:20:12.112429Z", - "iopub.status.busy": "2023-01-03T21:20:12.111906Z", - "iopub.status.idle": "2023-01-03T21:20:12.116676Z", - "shell.execute_reply": "2023-01-03T21:20:12.115762Z" + "iopub.execute_input": "2023-01-04T02:02:32.925908Z", + "iopub.status.busy": "2023-01-04T02:02:32.925564Z", + "iopub.status.idle": "2023-01-04T02:02:32.929317Z", + "shell.execute_reply": "2023-01-04T02:02:32.928823Z" } }, "outputs": [ @@ -721,7 +721,7 @@ }, { "cell_type": "markdown", - "id": "366448ac", + "id": "94a3558c", "metadata": {}, "source": [ "# Adding attributes to graphs, nodes, and edges\n", @@ -743,13 +743,13 @@ { "cell_type": "code", "execution_count": 21, - "id": "c4171db9", + "id": "0d2ce790", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T21:20:12.121627Z", - "iopub.status.busy": "2023-01-03T21:20:12.121164Z", - "iopub.status.idle": "2023-01-03T21:20:12.126621Z", - "shell.execute_reply": "2023-01-03T21:20:12.125824Z" + "iopub.execute_input": "2023-01-04T02:02:32.932621Z", + "iopub.status.busy": "2023-01-04T02:02:32.932082Z", + "iopub.status.idle": "2023-01-04T02:02:32.936376Z", + "shell.execute_reply": "2023-01-04T02:02:32.935881Z" } }, "outputs": [ @@ -771,7 +771,7 @@ }, { "cell_type": "markdown", - "id": "39a5e273", + "id": "81df7088", "metadata": {}, "source": [ "Or you can modify attributes later" @@ -780,13 +780,13 @@ { "cell_type": "code", "execution_count": 22, - "id": "7f926fcf", + "id": "5a139b3e", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T21:20:12.131169Z", - "iopub.status.busy": "2023-01-03T21:20:12.130669Z", - "iopub.status.idle": "2023-01-03T21:20:12.135877Z", - "shell.execute_reply": "2023-01-03T21:20:12.135015Z" + "iopub.execute_input": "2023-01-04T02:02:32.939435Z", + "iopub.status.busy": "2023-01-04T02:02:32.938897Z", + "iopub.status.idle": "2023-01-04T02:02:32.945251Z", + "shell.execute_reply": "2023-01-04T02:02:32.944678Z" } }, "outputs": [ @@ -808,7 +808,7 @@ }, { "cell_type": "markdown", - "id": "7448bf4e", + "id": "0fd0e7ac", "metadata": {}, "source": [ "# Node attributes\n", @@ -819,13 +819,13 @@ { "cell_type": "code", "execution_count": 23, - "id": "79c57af5", + "id": "c0a9ec59", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T21:20:12.140788Z", - "iopub.status.busy": "2023-01-03T21:20:12.140283Z", - "iopub.status.idle": "2023-01-03T21:20:12.146562Z", - "shell.execute_reply": "2023-01-03T21:20:12.145656Z" + "iopub.execute_input": "2023-01-04T02:02:32.948383Z", + "iopub.status.busy": "2023-01-04T02:02:32.948052Z", + "iopub.status.idle": "2023-01-04T02:02:32.954908Z", + "shell.execute_reply": "2023-01-04T02:02:32.954325Z" } }, "outputs": [ @@ -850,7 +850,7 @@ }, { "cell_type": "markdown", - "id": "b7346935", + "id": "81609bde", "metadata": {}, "source": [ "Note that adding a node to `G.nodes` does not add it to the graph, use\n", @@ -865,13 +865,13 @@ { "cell_type": "code", "execution_count": 24, - "id": "8d4375dd", + "id": "bd32be4d", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T21:20:12.151186Z", - "iopub.status.busy": "2023-01-03T21:20:12.150794Z", - "iopub.status.idle": "2023-01-03T21:20:12.156775Z", - "shell.execute_reply": "2023-01-03T21:20:12.155876Z" + "iopub.execute_input": "2023-01-04T02:02:32.958144Z", + "iopub.status.busy": "2023-01-04T02:02:32.957811Z", + "iopub.status.idle": "2023-01-04T02:02:32.963135Z", + "shell.execute_reply": "2023-01-04T02:02:32.962553Z" } }, "outputs": [], @@ -885,7 +885,7 @@ }, { "cell_type": "markdown", - "id": "6627979e", + "id": "05ce04fd", "metadata": {}, "source": [ "The special attribute `weight` should be numeric as it is used by\n", @@ -906,13 +906,13 @@ { "cell_type": "code", "execution_count": 25, - "id": "6dc40410", + "id": "a47da3f3", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T21:20:12.160961Z", - "iopub.status.busy": "2023-01-03T21:20:12.160376Z", - "iopub.status.idle": "2023-01-03T21:20:12.167987Z", - "shell.execute_reply": "2023-01-03T21:20:12.166903Z" + "iopub.execute_input": "2023-01-04T02:02:32.966299Z", + "iopub.status.busy": "2023-01-04T02:02:32.965964Z", + "iopub.status.idle": "2023-01-04T02:02:32.973518Z", + "shell.execute_reply": "2023-01-04T02:02:32.972933Z" } }, "outputs": [ @@ -938,7 +938,7 @@ }, { "cell_type": "markdown", - "id": "a7506961", + "id": "1b6bc284", "metadata": {}, "source": [ "Some algorithms work only for directed graphs and others are not well\n", @@ -951,13 +951,13 @@ { "cell_type": "code", "execution_count": 26, - "id": "66c55ec9", + "id": "7f61d304", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T21:20:12.173703Z", - "iopub.status.busy": "2023-01-03T21:20:12.173121Z", - "iopub.status.idle": "2023-01-03T21:20:12.177225Z", - "shell.execute_reply": "2023-01-03T21:20:12.176426Z" + "iopub.execute_input": "2023-01-04T02:02:32.976585Z", + "iopub.status.busy": "2023-01-04T02:02:32.976158Z", + "iopub.status.idle": "2023-01-04T02:02:32.980578Z", + "shell.execute_reply": "2023-01-04T02:02:32.979983Z" } }, "outputs": [], @@ -967,7 +967,7 @@ }, { "cell_type": "markdown", - "id": "283d6899", + "id": "5cdde51b", "metadata": {}, "source": [ "# Multigraphs\n", @@ -987,13 +987,13 @@ { "cell_type": "code", "execution_count": 27, - "id": "1d408dce", + "id": "3d1a9e24", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T21:20:12.181256Z", - "iopub.status.busy": "2023-01-03T21:20:12.180925Z", - "iopub.status.idle": "2023-01-03T21:20:12.189938Z", - "shell.execute_reply": "2023-01-03T21:20:12.189002Z" + "iopub.execute_input": "2023-01-04T02:02:32.983733Z", + "iopub.status.busy": "2023-01-04T02:02:32.983284Z", + "iopub.status.idle": "2023-01-04T02:02:32.990141Z", + "shell.execute_reply": "2023-01-04T02:02:32.989486Z" } }, "outputs": [ @@ -1023,7 +1023,7 @@ }, { "cell_type": "markdown", - "id": "0b95c68e", + "id": "a5588bf3", "metadata": {}, "source": [ "# Graph generators and graph operations\n", @@ -1043,13 +1043,13 @@ { "cell_type": "code", "execution_count": 28, - "id": "02686d3c", + "id": "2187cfc0", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T21:20:12.194990Z", - "iopub.status.busy": "2023-01-03T21:20:12.194481Z", - "iopub.status.idle": "2023-01-03T21:20:12.200621Z", - "shell.execute_reply": "2023-01-03T21:20:12.199726Z" + "iopub.execute_input": "2023-01-04T02:02:32.994022Z", + "iopub.status.busy": "2023-01-04T02:02:32.993599Z", + "iopub.status.idle": "2023-01-04T02:02:32.998169Z", + "shell.execute_reply": "2023-01-04T02:02:32.997518Z" } }, "outputs": [], @@ -1062,7 +1062,7 @@ }, { "cell_type": "markdown", - "id": "051f39a8", + "id": "dca58439", "metadata": {}, "source": [ "# 4. Using a stochastic graph generator, e.g,\n", @@ -1073,13 +1073,13 @@ { "cell_type": "code", "execution_count": 29, - "id": "064eaaa3", + "id": "80c96d5f", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T21:20:12.205008Z", - "iopub.status.busy": "2023-01-03T21:20:12.204729Z", - "iopub.status.idle": "2023-01-03T21:20:12.278236Z", - "shell.execute_reply": "2023-01-03T21:20:12.277298Z" + "iopub.execute_input": "2023-01-04T02:02:33.001371Z", + "iopub.status.busy": "2023-01-04T02:02:33.000975Z", + "iopub.status.idle": "2023-01-04T02:02:33.085302Z", + "shell.execute_reply": "2023-01-04T02:02:33.084409Z" } }, "outputs": [], @@ -1092,7 +1092,7 @@ }, { "cell_type": "markdown", - "id": "e98ae0ab", + "id": "9164e590", "metadata": {}, "source": [ "# 5. Reading a graph stored in a file using common graph formats\n", @@ -1104,13 +1104,13 @@ { "cell_type": "code", "execution_count": 30, - "id": "06e2199a", + "id": "73a4bcd5", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T21:20:12.283118Z", - "iopub.status.busy": "2023-01-03T21:20:12.282791Z", - "iopub.status.idle": "2023-01-03T21:20:13.302482Z", - "shell.execute_reply": "2023-01-03T21:20:13.301386Z" + "iopub.execute_input": "2023-01-04T02:02:33.088817Z", + "iopub.status.busy": "2023-01-04T02:02:33.088412Z", + "iopub.status.idle": "2023-01-04T02:02:34.134783Z", + "shell.execute_reply": "2023-01-04T02:02:34.134062Z" } }, "outputs": [], @@ -1121,7 +1121,7 @@ }, { "cell_type": "markdown", - "id": "7cb6eafd", + "id": "4fb6f129", "metadata": {}, "source": [ "For details on graph formats see Reading and writing graphs\n", @@ -1136,13 +1136,13 @@ { "cell_type": "code", "execution_count": 31, - "id": "3629c227", + "id": "89af8ee4", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T21:20:13.309196Z", - "iopub.status.busy": "2023-01-03T21:20:13.308857Z", - "iopub.status.idle": "2023-01-03T21:20:13.317836Z", - "shell.execute_reply": "2023-01-03T21:20:13.317011Z" + "iopub.execute_input": "2023-01-04T02:02:34.139449Z", + "iopub.status.busy": "2023-01-04T02:02:34.138958Z", + "iopub.status.idle": "2023-01-04T02:02:34.148568Z", + "shell.execute_reply": "2023-01-04T02:02:34.147930Z" } }, "outputs": [ @@ -1168,7 +1168,7 @@ }, { "cell_type": "markdown", - "id": "3dee69eb", + "id": "72fa2e2c", "metadata": {}, "source": [ "Some functions with large output iterate over (node, value) 2-tuples.\n", @@ -1178,13 +1178,13 @@ { "cell_type": "code", "execution_count": 32, - "id": "9c1e726c", + "id": "858fbc37", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T21:20:13.322359Z", - "iopub.status.busy": "2023-01-03T21:20:13.321791Z", - "iopub.status.idle": "2023-01-03T21:20:13.328915Z", - "shell.execute_reply": "2023-01-03T21:20:13.327882Z" + "iopub.execute_input": "2023-01-04T02:02:34.152013Z", + "iopub.status.busy": "2023-01-04T02:02:34.151575Z", + "iopub.status.idle": "2023-01-04T02:02:34.158402Z", + "shell.execute_reply": "2023-01-04T02:02:34.157811Z" } }, "outputs": [ @@ -1206,7 +1206,7 @@ }, { "cell_type": "markdown", - "id": "cc26616b", + "id": "17a4943b", "metadata": {}, "source": [ "See Algorithms for details on graph algorithms\n", @@ -1225,13 +1225,13 @@ { "cell_type": "code", "execution_count": 33, - "id": "2bb836ef", + "id": "7edb9a44", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T21:20:13.335269Z", - "iopub.status.busy": "2023-01-03T21:20:13.334633Z", - "iopub.status.idle": "2023-01-03T21:20:13.786076Z", - "shell.execute_reply": "2023-01-03T21:20:13.784800Z" + "iopub.execute_input": "2023-01-04T02:02:34.161422Z", + "iopub.status.busy": "2023-01-04T02:02:34.161190Z", + "iopub.status.idle": "2023-01-04T02:02:34.562006Z", + "shell.execute_reply": "2023-01-04T02:02:34.561263Z" } }, "outputs": [], @@ -1241,7 +1241,7 @@ }, { "cell_type": "markdown", - "id": "b1d71ebf", + "id": "6c277f0c", "metadata": {}, "source": [ "To test if the import of `nx_pylab` was successful draw `G`\n", @@ -1251,19 +1251,19 @@ { "cell_type": "code", "execution_count": 34, - "id": "7f78afc5", + "id": "5784854d", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T21:20:13.790813Z", - "iopub.status.busy": "2023-01-03T21:20:13.789642Z", - "iopub.status.idle": "2023-01-03T21:20:14.119423Z", - "shell.execute_reply": "2023-01-03T21:20:14.118422Z" + "iopub.execute_input": "2023-01-04T02:02:34.566425Z", + "iopub.status.busy": "2023-01-04T02:02:34.565983Z", + "iopub.status.idle": "2023-01-04T02:02:34.781366Z", + "shell.execute_reply": "2023-01-04T02:02:34.780767Z" } }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "<Figure size 640x480 with 2 Axes>" ] @@ -1282,7 +1282,7 @@ }, { "cell_type": "markdown", - "id": "40b05aaf", + "id": "0f58701d", "metadata": {}, "source": [ "when drawing to an interactive display. Note that you may need to issue a\n", @@ -1292,13 +1292,13 @@ { "cell_type": "code", "execution_count": 35, - "id": "6aa13f9c", + "id": "184f747d", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T21:20:14.124525Z", - "iopub.status.busy": "2023-01-03T21:20:14.124248Z", - "iopub.status.idle": "2023-01-03T21:20:14.128175Z", - "shell.execute_reply": "2023-01-03T21:20:14.127330Z" + "iopub.execute_input": "2023-01-04T02:02:34.785386Z", + "iopub.status.busy": "2023-01-04T02:02:34.785147Z", + "iopub.status.idle": "2023-01-04T02:02:34.788174Z", + "shell.execute_reply": "2023-01-04T02:02:34.787652Z" } }, "outputs": [], @@ -1308,7 +1308,7 @@ }, { "cell_type": "markdown", - "id": "cdb34214", + "id": "5bec10f4", "metadata": {}, "source": [ "command if you are not using matplotlib in interactive mode." @@ -1317,19 +1317,19 @@ { "cell_type": "code", "execution_count": 36, - "id": "6bae2d6b", + "id": "e626beb2", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T21:20:14.132120Z", - "iopub.status.busy": "2023-01-03T21:20:14.131507Z", - "iopub.status.idle": "2023-01-03T21:20:14.560218Z", - "shell.execute_reply": "2023-01-03T21:20:14.554717Z" + "iopub.execute_input": "2023-01-04T02:02:34.791096Z", + "iopub.status.busy": "2023-01-04T02:02:34.790747Z", + "iopub.status.idle": "2023-01-04T02:02:35.084060Z", + "shell.execute_reply": "2023-01-04T02:02:35.083131Z" } }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "<Figure size 640x480 with 4 Axes>" ] @@ -1356,7 +1356,7 @@ }, { "cell_type": "markdown", - "id": "bd41ce21", + "id": "7979eef0", "metadata": {}, "source": [ "You can find additional options via `draw_networkx()` and\n", @@ -1367,13 +1367,13 @@ { "cell_type": "code", "execution_count": 37, - "id": "c8483998", + "id": "1fd1a0fa", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T21:20:14.564227Z", - "iopub.status.busy": "2023-01-03T21:20:14.563927Z", - "iopub.status.idle": "2023-01-03T21:20:14.714875Z", - "shell.execute_reply": "2023-01-03T21:20:14.714076Z" + "iopub.execute_input": "2023-01-04T02:02:35.087630Z", + "iopub.status.busy": "2023-01-04T02:02:35.087351Z", + "iopub.status.idle": "2023-01-04T02:02:35.200638Z", + "shell.execute_reply": "2023-01-04T02:02:35.199898Z" } }, "outputs": [ @@ -1396,7 +1396,7 @@ }, { "cell_type": "markdown", - "id": "e3525408", + "id": "5abfdb9a", "metadata": {}, "source": [ "To save drawings to a file, use, for example" @@ -1405,19 +1405,19 @@ { "cell_type": "code", "execution_count": 38, - "id": "5f16fecb", + "id": "a6a694ad", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T21:20:14.718449Z", - "iopub.status.busy": "2023-01-03T21:20:14.718195Z", - "iopub.status.idle": "2023-01-03T21:20:14.903716Z", - "shell.execute_reply": "2023-01-03T21:20:14.902905Z" + "iopub.execute_input": "2023-01-04T02:02:35.204251Z", + "iopub.status.busy": "2023-01-04T02:02:35.203978Z", + "iopub.status.idle": "2023-01-04T02:02:35.345177Z", + "shell.execute_reply": "2023-01-04T02:02:35.344585Z" } }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -1433,7 +1433,7 @@ }, { "cell_type": "markdown", - "id": "5eccd196", + "id": "11ba58bc", "metadata": {}, "source": [ "This function writes to the file `path.png` in the local directory. If Graphviz and\n", @@ -1446,13 +1446,13 @@ { "cell_type": "code", "execution_count": 39, - "id": "49be0aff", + "id": "cf6bedc9", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T21:20:14.907434Z", - "iopub.status.busy": "2023-01-03T21:20:14.906989Z", - "iopub.status.idle": "2023-01-03T21:20:15.098668Z", - "shell.execute_reply": "2023-01-03T21:20:15.097945Z" + "iopub.execute_input": "2023-01-04T02:02:35.348480Z", + "iopub.status.busy": "2023-01-04T02:02:35.348047Z", + "iopub.status.idle": "2023-01-04T02:02:35.580346Z", + "shell.execute_reply": "2023-01-04T02:02:35.579722Z" } }, "outputs": [ @@ -1476,7 +1476,7 @@ }, { "cell_type": "markdown", - "id": "7601eacf", + "id": "dca97229", "metadata": {}, "source": [ "See Drawing for additional details." |
