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authorMridulS <mail@mriduls.com>2023-02-27 20:28:59 +0000
committerMridulS <mail@mriduls.com>2023-02-27 20:28:59 +0000
commita17f2f86b30ce45d82ab420b3cee63d9fb1463b3 (patch)
treed4be94fb53f45df586ebe5061505246e4527039a
parentcbab7889c75d2dcc729a104114e8c42bcb91fcea (diff)
downloadnetworkx-gh-pages.tar.gz
Deploying to gh-pages from @ networkx/networkx@523ff6aa9ea6c4d555f19a032c604d43251a5dac 🚀gh-pages
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index 17966289..267270aa 100644
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<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 a03efed6..9ffeebb1 100644
--- a/auto_examples/algorithms/plot_davis_club.html
+++ b/auto_examples/algorithms/plot_davis_club.html
@@ -652,7 +652,7 @@ 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 0.077 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-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 427879fe..45eaeb5c 100644
--- a/auto_examples/algorithms/plot_dedensification.html
+++ b/auto_examples/algorithms/plot_dedensification.html
@@ -606,7 +606,7 @@ 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 0.274 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.262 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 3f7d0177..597a012d 100644
--- a/auto_examples/algorithms/plot_iterated_dynamical_systems.html
+++ b/auto_examples/algorithms/plot_iterated_dynamical_systems.html
@@ -712,7 +712,7 @@ fixed points are []
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;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">&quot;</span><span class="p">)</span>
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.095 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.094 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 720a255f..9ae3ad18 100644
--- a/auto_examples/algorithms/plot_krackhardt_centrality.html
+++ b/auto_examples/algorithms/plot_krackhardt_centrality.html
@@ -582,7 +582,7 @@ 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 0.066 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-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_maximum_independent_set.html b/auto_examples/algorithms/plot_maximum_independent_set.html
index 910631b4..2adcd808 100644
--- a/auto_examples/algorithms/plot_maximum_independent_set.html
+++ b/auto_examples/algorithms/plot_maximum_independent_set.html
@@ -563,7 +563,7 @@ possible size for a given graph.</p>
<span class="p">)</span>
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.071 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-algorithms-plot-maximum-independent-set-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/8a508c78cefb7056d5a2d2af5a610ed4/plot_maximum_independent_set.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_maximum_independent_set.py</span></code></a></p>
diff --git a/auto_examples/algorithms/plot_parallel_betweenness.html b/auto_examples/algorithms/plot_parallel_betweenness.html
index 4dde53ac..a3cf3493 100644
--- a/auto_examples/algorithms/plot_parallel_betweenness.html
+++ b/auto_examples/algorithms/plot_parallel_betweenness.html
@@ -530,29 +530,29 @@ 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: 1.8475 seconds
- Betweenness centrality for node 0: 0.24859
+ Time: 1.7919 seconds
+ Betweenness centrality for node 0: 0.11637
Non-Parallel version
- Time: 2.9750 seconds
- Betweenness centrality for node 0: 0.24859
+ Time: 2.7852 seconds
+ Betweenness centrality for node 0: 0.11637
Computing betweenness centrality for:
-Graph with 1000 nodes and 4962 edges
+Graph with 1000 nodes and 5014 edges
Parallel version
- Time: 2.2561 seconds
- Betweenness centrality for node 0: 0.00112
+ Time: 2.1417 seconds
+ Betweenness centrality for node 0: 0.00464
Non-Parallel version
- Time: 3.9241 seconds
- Betweenness centrality for node 0: 0.00112
+ Time: 3.6891 seconds
+ Betweenness centrality for node 0: 0.00464
Computing betweenness centrality for:
Graph with 1000 nodes and 2000 edges
Parallel version
- Time: 1.5178 seconds
- Betweenness centrality for node 0: 0.01252
+ Time: 1.4666 seconds
+ Betweenness centrality for node 0: 0.00141
Non-Parallel version
- Time: 2.6976 seconds
- Betweenness centrality for node 0: 0.01252
+ Time: 2.5448 seconds
+ Betweenness centrality for node 0: 0.00141
</pre></div>
</div>
<div class="line-block">
@@ -624,7 +624,7 @@ 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 20.577 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 19.821 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 695af02f..74c71849 100644
--- a/auto_examples/algorithms/plot_rcm.html
+++ b/auto_examples/algorithms/plot_rcm.html
@@ -628,7 +628,7 @@ 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 1.183 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.734 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 e47cc919..fc988d14 100644
--- a/auto_examples/algorithms/plot_snap.html
+++ b/auto_examples/algorithms/plot_snap.html
@@ -623,7 +623,7 @@ 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 0.207 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.198 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 b8c64d0b..7a424f07 100644
--- a/auto_examples/algorithms/plot_subgraphs.html
+++ b/auto_examples/algorithms/plot_subgraphs.html
@@ -691,7 +691,7 @@ 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 0.700 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 18ebab3a..bc6a6e6f 100644
--- a/auto_examples/algorithms/sg_execution_times.html
+++ b/auto_examples/algorithms/sg_execution_times.html
@@ -475,31 +475,31 @@
<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>00:27.401</strong> total execution time for <strong>auto_examples_algorithms</strong> files:</p>
+<p><strong>00:26.244</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>00:20.577</p></td>
+<td><p>00:19.821</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">Betweenness Centrality</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_betweenness_centrality.py</span></code>)</p></td>
-<td><p>00:03.453</p></td>
+<td><p>00:03.555</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>00:01.183</p></td>
+<td><p>00:00.734</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>00:00.700</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>00:00.369</p></td>
+<td><p>00:00.372</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>00:00.274</p></td>
+<td><p>00:00.262</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>
@@ -507,27 +507,27 @@
<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>00:00.207</p></td>
+<td><p>00:00.198</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>00:00.112</p></td>
+<td><p>00:00.108</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>00:00.095</p></td>
+<td><p>00:00.094</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>00:00.077</p></td>
+<td><p>00:00.076</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="plot_maximum_independent_set.html#sphx-glr-auto-examples-algorithms-plot-maximum-independent-set-py"><span class="std std-ref">Maximum Independent Set</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_maximum_independent_set.py</span></code>)</p></td>
-<td><p>00:00.071</p></td>
+<td><p>00:00.068</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><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>00:00.066</p></td>
+<td><p>00:00.063</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 bb43a0db..d731edce 100644
--- a/auto_examples/basic/plot_properties.html
+++ b/auto_examples/basic/plot_properties.html
@@ -586,7 +586,7 @@ 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 0.093 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 e7cb0890..a73473b9 100644
--- a/auto_examples/basic/plot_read_write.html
+++ b/auto_examples/basic/plot_read_write.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 0.067 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.065 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 98ae7c79..f7004144 100644
--- a/auto_examples/basic/plot_simple_graph.html
+++ b/auto_examples/basic/plot_simple_graph.html
@@ -562,7 +562,7 @@ 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>
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+<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 0.337 seconds)</p>
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<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 0d5d32ca..e6961791 100644
--- a/auto_examples/basic/sg_execution_times.html
+++ b/auto_examples/basic/sg_execution_times.html
@@ -475,19 +475,19 @@
<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>00:00.558</strong> total execution time for <strong>auto_examples_basic</strong> files:</p>
+<p><strong>00:00.492</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>00:00.399</p></td>
+<td><p>00:00.337</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>00:00.093</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>00:00.067</p></td>
+<td><p>00:00.065</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 5eadd8ee..d558bddc 100644
--- a/auto_examples/drawing/plot_center_node.html
+++ b/auto_examples/drawing/plot_center_node.html
@@ -542,7 +542,7 @@ 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>
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<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 cbfd39e0..dd2c32de 100644
--- a/auto_examples/drawing/plot_chess_masters.html
+++ b/auto_examples/drawing/plot_chess_masters.html
@@ -548,7 +548,7 @@ 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:
-[&#39;Karpov, Anatoly&#39;, &#39;Korchnoi, Viktor L&#39;, &#39;Kasparov, Gary&#39;]
+[&#39;Korchnoi, Viktor L&#39;, &#39;Karpov, Anatoly&#39;, &#39;Kasparov, Gary&#39;]
From a total of 237 different openings,
the following games used the Sicilian opening
@@ -714,7 +714,7 @@ findfont: Font family &#39;Helvetica&#39; 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 0.399 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.389 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-chess-masters-py">
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<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 596e4850..6f445779 100644
--- a/auto_examples/drawing/plot_custom_node_icons.html
+++ b/auto_examples/drawing/plot_custom_node_icons.html
@@ -597,7 +597,7 @@ 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 0.328 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.298 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 5768721e..0074edd3 100644
--- a/auto_examples/drawing/plot_degree.html
+++ b/auto_examples/drawing/plot_degree.html
@@ -573,7 +573,7 @@ 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 0.295 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.284 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 8e9c29c3..bbd7e864 100644
--- a/auto_examples/drawing/plot_directed.html
+++ b/auto_examples/drawing/plot_directed.html
@@ -568,7 +568,7 @@ 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 0.221 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.209 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 285dceb7..ac51dbdf 100644
--- a/auto_examples/drawing/plot_edge_colormap.html
+++ b/auto_examples/drawing/plot_edge_colormap.html
@@ -546,7 +546,7 @@ 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 0.067 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.065 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 9f087915..d1c1a3c4 100644
--- a/auto_examples/drawing/plot_ego_graph.html
+++ b/auto_examples/drawing/plot_ego_graph.html
@@ -558,7 +558,7 @@ 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 0.101 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.100 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 a8c1c272..03287b51 100644
--- a/auto_examples/drawing/plot_eigenvalues.html
+++ b/auto_examples/drawing/plot_eigenvalues.html
@@ -553,7 +553,7 @@ Smallest eigenvalue: -2.5363890312656235e-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 0.626 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.624 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 cc86cd30..a88cbba8 100644
--- a/auto_examples/drawing/plot_four_grids.html
+++ b/auto_examples/drawing/plot_four_grids.html
@@ -574,7 +574,7 @@ 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 0.350 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.332 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 218fb181..5819fd2c 100644
--- a/auto_examples/drawing/plot_house_with_colors.html
+++ b/auto_examples/drawing/plot_house_with_colors.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>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.089 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.084 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 d52eda4b..6aeaf328 100644
--- a/auto_examples/drawing/plot_knuth_miles.html
+++ b/auto_examples/drawing/plot_knuth_miles.html
@@ -672,7 +672,7 @@ 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>
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+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.103 seconds)</p>
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index 2157143b..29980647 100644
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--- a/auto_examples/drawing/plot_multipartite_graph.html
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@@ -565,7 +565,7 @@ to download the full example code</p>
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diff --git a/auto_examples/drawing/plot_node_colormap.html b/auto_examples/drawing/plot_node_colormap.html
index f5d4d560..9d0385e9 100644
--- a/auto_examples/drawing/plot_node_colormap.html
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@@ -538,7 +538,7 @@ to download the full example code</p>
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diff --git a/auto_examples/drawing/plot_rainbow_coloring.html b/auto_examples/drawing/plot_rainbow_coloring.html
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--- a/auto_examples/drawing/plot_rainbow_coloring.html
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diff --git a/auto_examples/drawing/plot_random_geometric_graph.html b/auto_examples/drawing/plot_random_geometric_graph.html
index eb3496a7..6a7163ef 100644
--- a/auto_examples/drawing/plot_random_geometric_graph.html
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@@ -567,7 +567,7 @@ 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>
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diff --git a/auto_examples/drawing/plot_sampson.html b/auto_examples/drawing/plot_sampson.html
index 43eb1279..c8ee737c 100644
--- a/auto_examples/drawing/plot_sampson.html
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@@ -569,7 +569,7 @@ 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>
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<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>
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index e7a7f05c..30b7b3a1 100644
--- a/auto_examples/drawing/plot_selfloops.html
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@@ -552,7 +552,7 @@ This example shows how to draw self-loops with <code class="xref py py-obj docut
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<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 036a63ea..723f505c 100644
--- a/auto_examples/drawing/plot_simple_path.html
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@@ -538,7 +538,7 @@ to download the full example code</p>
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diff --git a/auto_examples/drawing/plot_spectral_grid.html b/auto_examples/drawing/plot_spectral_grid.html
index d2b15533..d32f08e1 100644
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@@ -580,7 +580,7 @@ As you remove internal nodes, this effect increases.</p>
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<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 7b7e7d49..fbb09289 100644
--- a/auto_examples/drawing/plot_tsp.html
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<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 df4e81b4..16cd4f99 100644
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<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>
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<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 1eeb600d..190ab0b7 100644
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<table class="table">
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<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 ee085db6..0536ce2e 100644
--- a/auto_examples/graph/plot_degree_sequence.html
+++ b/auto_examples/graph/plot_degree_sequence.html
@@ -562,7 +562,7 @@ 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 0.062 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.086 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 2f58f86c..69ce4230 100644
--- a/auto_examples/graph/plot_erdos_renyi.html
+++ b/auto_examples/graph/plot_erdos_renyi.html
@@ -574,7 +574,7 @@ 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 0.063 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.061 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 e42f2399..de05db02 100644
--- a/auto_examples/graph/plot_expected_degree_sequence.html
+++ b/auto_examples/graph/plot_expected_degree_sequence.html
@@ -549,46 +549,46 @@ degree (#nodes) ****
27 ( 0)
28 ( 0)
29 ( 0)
-30 ( 1) *
-31 ( 0)
-32 ( 0)
-33 ( 2) **
-34 ( 4) ****
-35 ( 3) ***
-36 ( 3) ***
-37 ( 7) *******
-38 ( 6) ******
-39 (12) ************
-40 (11) ***********
-41 ( 8) ********
-42 (14) **************
-43 (13) *************
-44 (14) **************
-45 (21) *********************
-46 (34) **********************************
-47 (28) ****************************
-48 (25) *************************
-49 (38) **************************************
-50 (38) **************************************
-51 (34) **********************************
-52 (25) *************************
-53 (26) **************************
-54 (16) ****************
-55 (19) *******************
-56 (19) *******************
-57 (18) ******************
-58 (16) ****************
+30 ( 0)
+31 ( 2) **
+32 ( 1) *
+33 ( 1) *
+34 ( 2) **
+35 ( 1) *
+36 ( 4) ****
+37 ( 4) ****
+38 ( 7) *******
+39 ( 6) ******
+40 (14) **************
+41 (14) **************
+42 (11) ***********
+43 (25) *************************
+44 (16) ****************
+45 (17) *****************
+46 (21) *********************
+47 (20) ********************
+48 (27) ***************************
+49 (24) ************************
+50 (37) *************************************
+51 (30) ******************************
+52 (26) **************************
+53 (38) **************************************
+54 (25) *************************
+55 (29) *****************************
+56 (17) *****************
+57 (12) ************
+58 (12) ************
59 (12) ************
-60 (11) ***********
-61 ( 4) ****
-62 ( 3) ***
-63 ( 3) ***
-64 ( 1) *
-65 ( 4) ****
-66 ( 1) *
-67 ( 3) ***
-68 ( 2) **
-69 ( 0)
+60 ( 9) *********
+61 (10) **********
+62 ( 5) *****
+63 ( 7) *******
+64 ( 7) *******
+65 ( 3) ***
+66 ( 0)
+67 ( 1) *
+68 ( 1) *
+69 ( 1) *
70 ( 0)
71 ( 1) *
</pre></div>
@@ -610,7 +610,7 @@ degree (#nodes) ****
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</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">&#39;*&#39;</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">&quot;</span><span class="p">)</span>
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.031 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 1548d961..51303ec5 100644
--- a/auto_examples/graph/plot_football.html
+++ b/auto_examples/graph/plot_football.html
@@ -698,7 +698,7 @@ 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 0.449 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.299 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 4490d583..adb87c71 100644
--- a/auto_examples/graph/plot_karate_club.html
+++ b/auto_examples/graph/plot_karate_club.html
@@ -574,7 +574,7 @@ 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 0.093 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-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 2c8b589c..f696f3a9 100644
--- a/auto_examples/graph/plot_morse_trie.html
+++ b/auto_examples/graph/plot_morse_trie.html
@@ -615,7 +615,7 @@ the path.</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot; &quot;</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">&quot;ilovenetworkx&quot;</span><span class="p">]))</span>
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.182 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.171 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 c7b0c171..5c847054 100644
--- a/auto_examples/graph/plot_napoleon_russian_campaign.html
+++ b/auto_examples/graph/plot_napoleon_russian_campaign.html
@@ -644,7 +644,7 @@ 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 0.133 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_triad_types.html b/auto_examples/graph/plot_triad_types.html
index 6000dfc7..b0a402bf 100644
--- a/auto_examples/graph/plot_triad_types.html
+++ b/auto_examples/graph/plot_triad_types.html
@@ -575,7 +575,7 @@ 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 1.292 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 1.222 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 9bde1f50..d4eff104 100644
--- a/auto_examples/graph/plot_words.html
+++ b/auto_examples/graph/plot_words.html
@@ -636,7 +636,7 @@ 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 0.376 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.361 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 6577464a..0e14b103 100644
--- a/auto_examples/graph/sg_execution_times.html
+++ b/auto_examples/graph/sg_execution_times.html
@@ -475,19 +475,19 @@
<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>00:03.045</strong> total execution time for <strong>auto_examples_graph</strong> files:</p>
+<p><strong>00:02.805</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>00:01.292</p></td>
+<td><p>00:01.222</p></td>
<td><p>0.0 MB</p></td>
</tr>
-<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>00:00.449</p></td>
+<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>00:00.361</p></td>
<td><p>0.0 MB</p></td>
</tr>
-<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>00:00.376</p></td>
+<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>00:00.299</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>
@@ -495,31 +495,31 @@
<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>00:00.182</p></td>
+<td><p>00:00.171</p></td>
<td><p>0.0 MB</p></td>
</tr>
<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>00:00.133</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_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>00:00.133</p></td>
+<td><p>00:00.125</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>00:00.093</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_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>00:00.063</p></td>
+<tr class="row-odd"><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>00:00.086</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>00:00.062</p></td>
+<tr class="row-even"><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>00:00.061</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>00:00.031</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 555b123e..5d6ead60 100644
--- a/auto_examples/graphviz_drawing/plot_attributes.html
+++ b/auto_examples/graphviz_drawing/plot_attributes.html
@@ -544,7 +544,7 @@ node node attributes
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</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.067 seconds)</p>
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<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graphviz-drawing-plot-attributes-py">
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<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 ccf6dba3..b679998e 100644
--- a/auto_examples/graphviz_drawing/plot_conversion.html
+++ b/auto_examples/graphviz_drawing/plot_conversion.html
@@ -526,7 +526,7 @@ 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">&quot;k5.png&quot;</span><span class="p">,</span> <span class="n">prog</span><span class="o">=</span><span class="s2">&quot;neato&quot;</span><span class="p">)</span>
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</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.026 seconds)</p>
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<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_mini_atlas.html b/auto_examples/graphviz_drawing/plot_mini_atlas.html
index b486f5fd..92401535 100644
--- a/auto_examples/graphviz_drawing/plot_mini_atlas.html
+++ b/auto_examples/graphviz_drawing/plot_mini_atlas.html
@@ -555,7 +555,7 @@ Graph named &#39;G19&#39; 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">&quot;A20.png&quot;</span><span class="p">,</span> <span class="n">prog</span><span class="o">=</span><span class="s2">&quot;neato&quot;</span><span class="p">)</span>
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-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.079 seconds)</p>
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<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 ed3d5100..3f4a8d0b 100644
--- a/auto_examples/graphviz_drawing/sg_execution_times.html
+++ b/auto_examples/graphviz_drawing/sg_execution_times.html
@@ -475,11 +475,11 @@
<section id="computation-times">
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+<p><strong>00:00.197</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>
-<td><p>00:00.079</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_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>
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<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>00:00.067</p></td>
+<td><p>00:00.029</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>00:00.026</p></td>
+<td><p>00:00.024</p></td>
<td><p>0.0 MB</p></td>
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</tbody>
diff --git a/auto_examples/graphviz_layout/plot_atlas.html b/auto_examples/graphviz_layout/plot_atlas.html
index 3749f5ee..234d8f50 100644
--- a/auto_examples/graphviz_layout/plot_atlas.html
+++ b/auto_examples/graphviz_layout/plot_atlas.html
@@ -561,7 +561,7 @@ 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 3.785 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 3.636 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 1bf6d235..60100885 100644
--- a/auto_examples/graphviz_layout/plot_circular_tree.html
+++ b/auto_examples/graphviz_layout/plot_circular_tree.html
@@ -522,7 +522,7 @@ 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 0.156 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.154 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 5a098a44..643f33a8 100644
--- a/auto_examples/graphviz_layout/plot_decomposition.html
+++ b/auto_examples/graphviz_layout/plot_decomposition.html
@@ -547,7 +547,7 @@ 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 0.347 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-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 e87b0cda..3edc6caf 100644
--- a/auto_examples/graphviz_layout/plot_giant_component.html
+++ b/auto_examples/graphviz_layout/plot_giant_component.html
@@ -555,7 +555,7 @@ 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 0.848 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.808 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 9c4c994e..80c8a497 100644
--- a/auto_examples/graphviz_layout/plot_lanl_routes.html
+++ b/auto_examples/graphviz_layout/plot_lanl_routes.html
@@ -573,7 +573,7 @@ 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 0.339 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.331 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 c029734e..1d3d79fa 100644
--- a/auto_examples/graphviz_layout/sg_execution_times.html
+++ b/auto_examples/graphviz_layout/sg_execution_times.html
@@ -475,27 +475,27 @@
<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>00:05.474</strong> total execution time for <strong>auto_examples_graphviz_layout</strong> files:</p>
+<p><strong>00:05.256</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>00:03.785</p></td>
+<td><p>00:03.636</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>00:00.848</p></td>
+<td><p>00:00.808</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>00:00.347</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>00:00.331</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>00:00.339</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>00:00.328</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>00:00.156</p></td>
+<td><p>00:00.154</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 ef54e64e..5fe7a07e 100644
--- a/auto_examples/subclass/plot_antigraph.html
+++ b/auto_examples/subclass/plot_antigraph.html
@@ -692,7 +692,7 @@ 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>
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<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-subclass-plot-antigraph-py">
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<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 73092e0a..8b2cab8c 100644
--- a/auto_examples/subclass/plot_printgraph.html
+++ b/auto_examples/subclass/plot_printgraph.html
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</pre></div>
</div>
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<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 5c9736b5..7a61326c 100644
--- a/auto_examples/subclass/sg_execution_times.html
+++ b/auto_examples/subclass/sg_execution_times.html
@@ -475,15 +475,15 @@
<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>00:00.158</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>00:00.095</p></td>
+<td><p>00:00.094</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>00:00.063</p></td>
+<td><p>00:00.061</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
diff --git a/developer/contribute.html b/developer/contribute.html
index abf9e6ac..a55159d6 100644
--- a/developer/contribute.html
+++ b/developer/contribute.html
@@ -852,22 +852,73 @@ detailing the test coverage:</p>
<span class="o">...</span>
</pre></div>
</div>
-</section>
<section id="adding-tests">
-<h2>Adding tests<a class="headerlink" href="#adding-tests" title="Permalink to this heading">#</a></h2>
+<h3>Adding tests<a class="headerlink" href="#adding-tests" title="Permalink to this heading">#</a></h3>
<p>If you’re <strong>new to testing</strong>, see existing test files for examples of things to do.
<strong>Don’t let the tests keep you from submitting your contribution!</strong>
If you’re not sure how to do this or are having trouble, submit your pull request
anyway.
We will help you create the tests and sort out any kind of problem during code review.</p>
</section>
+<section id="image-comparison">
+<h3>Image comparison<a class="headerlink" href="#image-comparison" title="Permalink to this heading">#</a></h3>
+<p>To run image comparisons:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ PYTHONPATH=. pytest --mpl --pyargs networkx.drawing
+</pre></div>
+</div>
+<p>The <code class="docutils literal notranslate"><span class="pre">--mpl</span></code> tells <code class="docutils literal notranslate"><span class="pre">pytest</span></code> to use <code class="docutils literal notranslate"><span class="pre">pytest-mpl</span></code> to compare the generated plots
+with baseline ones stored in <code class="docutils literal notranslate"><span class="pre">networkx/drawing/tests/baseline</span></code>.</p>
+<p>To add a new test, add a test function to <code class="docutils literal notranslate"><span class="pre">networkx/drawing/tests</span></code> that
+returns a Matplotlib figure (or any figure object that has a savefig method)
+and decorate it as follows:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@pytest</span><span class="o">.</span><span class="n">mark</span><span class="o">.</span><span class="n">mpl_image_compare</span>
+<span class="k">def</span> <span class="nf">test_barbell</span><span class="p">():</span>
+ <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+ <span class="n">barbell</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">barbell_graph</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">6</span><span class="p">)</span>
+ <span class="c1"># make sure to fix any randomness</span>
+ <span class="n">pos</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">spring_layout</span><span class="p">(</span><span class="n">barbell</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
+ <span class="n">nx</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="n">barbell</span><span class="p">,</span> <span class="n">pos</span><span class="o">=</span><span class="n">pos</span><span class="p">)</span>
+ <span class="k">return</span> <span class="n">fig</span>
+</pre></div>
+</div>
+<p>Then create a baseline image to compare against later:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ pytest -k test_barbell --mpl-generate-path=networkx/drawing/tests/baseline
+</pre></div>
+</div>
+<p>And test:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ pytest -k test_barbell --mpl
+</pre></div>
+</div>
+</section>
+</section>
+<section id="documentation">
+<h2>Documentation<a class="headerlink" href="#documentation" title="Permalink to this heading">#</a></h2>
+<p>Building the documentation locally requires that the additional dependencies
+specified in <code class="docutils literal notranslate"><span class="pre">requirements/doc.txt</span></code> be installed in your development
+environment.</p>
+<p>The documentation is built with <code class="docutils literal notranslate"><span class="pre">sphinx</span></code>. To build the documentation locally,
+navigate to the <code class="docutils literal notranslate"><span class="pre">doc/</span></code> directory and:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">make</span> <span class="n">html</span>
+</pre></div>
+</div>
+<p>This will generate both the reference documentation as well as the example
+gallery. If you want to build the documentation <em>without</em> building the
+gallery examples use:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">make</span> <span class="n">html</span><span class="o">-</span><span class="n">noplot</span>
+</pre></div>
+</div>
+<p>The build products are stored in <code class="docutils literal notranslate"><span class="pre">doc/build/</span></code> and can be viewed directly.
+For example, to view the built html, open <code class="docutils literal notranslate"><span class="pre">build/html/index.html</span></code>
+in your preferred web browser.</p>
<section id="adding-examples">
-<h2>Adding examples<a class="headerlink" href="#adding-examples" title="Permalink to this heading">#</a></h2>
+<h3>Adding examples<a class="headerlink" href="#adding-examples" title="Permalink to this heading">#</a></h3>
<p>The gallery examples are managed by
<a class="reference external" href="https://sphinx-gallery.readthedocs.io/">sphinx-gallery</a>.
The source files for the example gallery are <code class="docutils literal notranslate"><span class="pre">.py</span></code> scripts in <code class="docutils literal notranslate"><span class="pre">examples/</span></code> that
generate one or more figures. They are executed automatically by sphinx-gallery when the
documentation is built. The output is gathered and assembled into the gallery.</p>
+<p>Building the example gallery locally requires that the additional dependencies
+in <code class="docutils literal notranslate"><span class="pre">requirements/example.txt</span></code> be installed in your development environment.</p>
<p>You can <strong>add a new</strong> plot by placing a new <code class="docutils literal notranslate"><span class="pre">.py</span></code> file in one of the directories inside the
<code class="docutils literal notranslate"><span class="pre">examples</span></code> directory of the repository. See the other examples to get an idea for the
format.</p>
@@ -886,7 +937,7 @@ documentation.</p></li>
</ul>
</section>
<section id="adding-references">
-<h2>Adding References<a class="headerlink" href="#adding-references" title="Permalink to this heading">#</a></h2>
+<h3>Adding References<a class="headerlink" href="#adding-references" title="Permalink to this heading">#</a></h3>
<p>If you are contributing a new algorithm (or an improvement to a current algorithm),
a reference paper or resource should also be provided in the function docstring.
For references to published papers, we try to follow the
@@ -909,35 +960,6 @@ to use the <a class="reference external" href="https://web.archive.org/">wayback
and link the internet archive link. The URL of the resource can change, and it creates unreachable
links from the documentation.</p>
</section>
-<section id="image-comparison">
-<h2>Image comparison<a class="headerlink" href="#image-comparison" title="Permalink to this heading">#</a></h2>
-<p>To run image comparisons:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ PYTHONPATH=. pytest --mpl --pyargs networkx.drawing
-</pre></div>
-</div>
-<p>The <code class="docutils literal notranslate"><span class="pre">--mpl</span></code> tells <code class="docutils literal notranslate"><span class="pre">pytest</span></code> to use <code class="docutils literal notranslate"><span class="pre">pytest-mpl</span></code> to compare the generated plots
-with baseline ones stored in <code class="docutils literal notranslate"><span class="pre">networkx/drawing/tests/baseline</span></code>.</p>
-<p>To add a new test, add a test function to <code class="docutils literal notranslate"><span class="pre">networkx/drawing/tests</span></code> that
-returns a Matplotlib figure (or any figure object that has a savefig method)
-and decorate it as follows:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@pytest</span><span class="o">.</span><span class="n">mark</span><span class="o">.</span><span class="n">mpl_image_compare</span>
-<span class="k">def</span> <span class="nf">test_barbell</span><span class="p">():</span>
- <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
- <span class="n">barbell</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">barbell_graph</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">6</span><span class="p">)</span>
- <span class="c1"># make sure to fix any randomness</span>
- <span class="n">pos</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">spring_layout</span><span class="p">(</span><span class="n">barbell</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
- <span class="n">nx</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="n">barbell</span><span class="p">,</span> <span class="n">pos</span><span class="o">=</span><span class="n">pos</span><span class="p">)</span>
- <span class="k">return</span> <span class="n">fig</span>
-</pre></div>
-</div>
-<p>Then create a baseline image to compare against later:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ pytest -k test_barbell --mpl-generate-path=networkx/drawing/tests/baseline
-</pre></div>
-</div>
-<p>And test:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ pytest -k test_barbell --mpl
-</pre></div>
-</div>
</section>
<section id="bugs">
<h2>Bugs<a class="headerlink" href="#bugs" title="Permalink to this heading">#</a></h2>
@@ -1009,26 +1031,35 @@ and decorate it as follows:</p>
<a class="reference internal nav-link" href="#testing">
Testing
</a>
+ <ul class="nav section-nav flex-column">
+ <li class="toc-h3 nav-item toc-entry">
+ <a class="reference internal nav-link" href="#adding-tests">
+ Adding tests
+ </a>
+ </li>
+ <li class="toc-h3 nav-item toc-entry">
+ <a class="reference internal nav-link" href="#image-comparison">
+ Image comparison
+ </a>
+ </li>
+ </ul>
</li>
<li class="toc-h2 nav-item toc-entry">
- <a class="reference internal nav-link" href="#adding-tests">
- Adding tests
- </a>
- </li>
- <li class="toc-h2 nav-item toc-entry">
- <a class="reference internal nav-link" href="#adding-examples">
- Adding examples
- </a>
- </li>
- <li class="toc-h2 nav-item toc-entry">
- <a class="reference internal nav-link" href="#adding-references">
- Adding References
- </a>
- </li>
- <li class="toc-h2 nav-item toc-entry">
- <a class="reference internal nav-link" href="#image-comparison">
- Image comparison
+ <a class="reference internal nav-link" href="#documentation">
+ Documentation
</a>
+ <ul class="nav section-nav flex-column">
+ <li class="toc-h3 nav-item toc-entry">
+ <a class="reference internal nav-link" href="#adding-examples">
+ Adding examples
+ </a>
+ </li>
+ <li class="toc-h3 nav-item toc-entry">
+ <a class="reference internal nav-link" href="#adding-references">
+ Adding References
+ </a>
+ </li>
+ </ul>
</li>
<li class="toc-h2 nav-item toc-entry">
<a class="reference internal nav-link" href="#bugs">
diff --git a/developer/index.html b/developer/index.html
index dfbb486a..c8ca8bdd 100644
--- a/developer/index.html
+++ b/developer/index.html
@@ -505,7 +505,7 @@
<dd class="field-odd"><p>3.1rc1.dev0</p>
</dd>
<dt class="field-even">Date<span class="colon">:</span></dt>
-<dd class="field-even"><p>Feb 26, 2023</p>
+<dd class="field-even"><p>Feb 27, 2023</p>
</dd>
</dl>
<div class="toctree-wrapper compound">
@@ -537,10 +537,7 @@
<li class="toctree-l2"><a class="reference internal" href="contribute.html#divergence-from-upstream-main">Divergence from <code class="docutils literal notranslate"><span class="pre">upstream</span> <span class="pre">main</span></code></a></li>
<li class="toctree-l2"><a class="reference internal" href="contribute.html#guidelines">Guidelines</a></li>
<li class="toctree-l2"><a class="reference internal" href="contribute.html#testing">Testing</a></li>
-<li class="toctree-l2"><a class="reference internal" href="contribute.html#adding-tests">Adding tests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="contribute.html#adding-examples">Adding examples</a></li>
-<li class="toctree-l2"><a class="reference internal" href="contribute.html#adding-references">Adding References</a></li>
-<li class="toctree-l2"><a class="reference internal" href="contribute.html#image-comparison">Image comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#documentation">Documentation</a></li>
<li class="toctree-l2"><a class="reference internal" href="contribute.html#bugs">Bugs</a></li>
<li class="toctree-l2"><a class="reference internal" href="contribute.html#policies">Policies</a></li>
</ul>
diff --git a/index.html b/index.html
index b51877ad..b1360304 100644
--- a/index.html
+++ b/index.html
@@ -476,7 +476,7 @@
<dd class="field-odd"><p>3.1rc1.dev0</p>
</dd>
<dt class="field-even">Date<span class="colon">:</span></dt>
-<dd class="field-even"><p>Feb 26, 2023</p>
+<dd class="field-even"><p>Feb 27, 2023</p>
</dd>
</dl>
<p>NetworkX is a Python package for the creation, manipulation, and study
diff --git a/reference/index.html b/reference/index.html
index e82c6d89..475001bf 100644
--- a/reference/index.html
+++ b/reference/index.html
@@ -508,7 +508,7 @@
<dd class="field-odd"><p>3.1rc1.dev0</p>
</dd>
<dt class="field-even">Date<span class="colon">:</span></dt>
-<dd class="field-even"><p>Feb 26, 2023</p>
+<dd class="field-even"><p>Feb 27, 2023</p>
</dd>
</dl>
</div></blockquote>
diff --git a/reference/introduction-7.hires.png b/reference/introduction-7.hires.png
index 32217995..f124329a 100644
--- a/reference/introduction-7.hires.png
+++ b/reference/introduction-7.hires.png
Binary files differ
diff --git a/reference/introduction-7.pdf b/reference/introduction-7.pdf
index 01514330..0f513c52 100644
--- a/reference/introduction-7.pdf
+++ b/reference/introduction-7.pdf
Binary files differ
diff --git a/reference/introduction-7.png b/reference/introduction-7.png
index a025c927..b2d07ab2 100644
--- a/reference/introduction-7.png
+++ b/reference/introduction-7.png
Binary files differ
diff --git a/reference/introduction.ipynb b/reference/introduction.ipynb
index 37ee5ea1..b3b7a1fd 100644
--- a/reference/introduction.ipynb
+++ b/reference/introduction.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "markdown",
- "id": "b5a8eb6b",
+ "id": "117ac35c",
"metadata": {},
"source": [
"## Introduction\n",
@@ -34,7 +34,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "5695930c",
+ "id": "8f452748",
"metadata": {},
"outputs": [],
"source": [
@@ -43,7 +43,7 @@
},
{
"cell_type": "markdown",
- "id": "692c2305",
+ "id": "2fde317d",
"metadata": {},
"source": [
"To save repetition, in the documentation we assume that\n",
@@ -82,7 +82,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "cb802a24",
+ "id": "97fa77c4",
"metadata": {},
"outputs": [],
"source": [
@@ -94,7 +94,7 @@
},
{
"cell_type": "markdown",
- "id": "bf48d000",
+ "id": "0e493f1d",
"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": "b57bd24b",
+ "id": "ab96e3c8",
"metadata": {},
"outputs": [],
"source": [
@@ -205,7 +205,7 @@
},
{
"cell_type": "markdown",
- "id": "af944dba",
+ "id": "d38340ff",
"metadata": {},
"source": [
"Edge attributes can be anything:"
@@ -214,7 +214,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "e2400ba2",
+ "id": "563e93a0",
"metadata": {},
"outputs": [],
"source": [
@@ -225,7 +225,7 @@
},
{
"cell_type": "markdown",
- "id": "30ce4b31",
+ "id": "041a73e2",
"metadata": {},
"source": [
"You can add many edges at one time:"
@@ -234,7 +234,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "6f3061ba",
+ "id": "742c952f",
"metadata": {},
"outputs": [],
"source": [
@@ -246,7 +246,7 @@
},
{
"cell_type": "markdown",
- "id": "cf4c1a29",
+ "id": "ede8209a",
"metadata": {},
"source": [
"See the Tutorial for more examples.\n",
@@ -311,7 +311,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "33060076",
+ "id": "191e4c79",
"metadata": {},
"outputs": [],
"source": [
@@ -323,7 +323,7 @@
},
{
"cell_type": "markdown",
- "id": "43a66e19",
+ "id": "e7dff3b1",
"metadata": {},
"source": [
"# Drawing\n",
@@ -344,7 +344,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "50d5b2a3",
+ "id": "031a0f23",
"metadata": {},
"outputs": [],
"source": [
@@ -358,7 +358,7 @@
},
{
"cell_type": "markdown",
- "id": "3a564e32",
+ "id": "388c86a6",
"metadata": {},
"source": [
"See the examples for more ideas.\n",
@@ -398,7 +398,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "8867e559",
+ "id": "9d6089b1",
"metadata": {},
"outputs": [],
"source": [
@@ -410,7 +410,7 @@
},
{
"cell_type": "markdown",
- "id": "475498e4",
+ "id": "eb62a513",
"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": "843dfcd2",
+ "id": "f7232475",
"metadata": {},
"outputs": [],
"source": [
diff --git a/reference/introduction_full.ipynb b/reference/introduction_full.ipynb
index 73dd2f07..f5105cfc 100644
--- a/reference/introduction_full.ipynb
+++ b/reference/introduction_full.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "markdown",
- "id": "b5a8eb6b",
+ "id": "117ac35c",
"metadata": {},
"source": [
"## Introduction\n",
@@ -34,13 +34,13 @@
{
"cell_type": "code",
"execution_count": 1,
- "id": "5695930c",
+ "id": "8f452748",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-26T01:59:50.313796Z",
- "iopub.status.busy": "2023-02-26T01:59:50.313539Z",
- "iopub.status.idle": "2023-02-26T01:59:50.385812Z",
- "shell.execute_reply": "2023-02-26T01:59:50.384788Z"
+ "iopub.execute_input": "2023-02-27T20:27:38.791241Z",
+ "iopub.status.busy": "2023-02-27T20:27:38.791030Z",
+ "iopub.status.idle": "2023-02-27T20:27:38.858930Z",
+ "shell.execute_reply": "2023-02-27T20:27:38.858282Z"
}
},
"outputs": [],
@@ -50,7 +50,7 @@
},
{
"cell_type": "markdown",
- "id": "692c2305",
+ "id": "2fde317d",
"metadata": {},
"source": [
"To save repetition, in the documentation we assume that\n",
@@ -89,13 +89,13 @@
{
"cell_type": "code",
"execution_count": 2,
- "id": "cb802a24",
+ "id": "97fa77c4",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-26T01:59:50.389267Z",
- "iopub.status.busy": "2023-02-26T01:59:50.388895Z",
- "iopub.status.idle": "2023-02-26T01:59:50.392539Z",
- "shell.execute_reply": "2023-02-26T01:59:50.391906Z"
+ "iopub.execute_input": "2023-02-27T20:27:38.861728Z",
+ "iopub.status.busy": "2023-02-27T20:27:38.861391Z",
+ "iopub.status.idle": "2023-02-27T20:27:38.865573Z",
+ "shell.execute_reply": "2023-02-27T20:27:38.864969Z"
}
},
"outputs": [],
@@ -108,7 +108,7 @@
},
{
"cell_type": "markdown",
- "id": "bf48d000",
+ "id": "0e493f1d",
"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": "b57bd24b",
+ "id": "ab96e3c8",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-26T01:59:50.395175Z",
- "iopub.status.busy": "2023-02-26T01:59:50.394773Z",
- "iopub.status.idle": "2023-02-26T01:59:50.398382Z",
- "shell.execute_reply": "2023-02-26T01:59:50.397765Z"
+ "iopub.execute_input": "2023-02-27T20:27:38.868251Z",
+ "iopub.status.busy": "2023-02-27T20:27:38.867757Z",
+ "iopub.status.idle": "2023-02-27T20:27:38.871217Z",
+ "shell.execute_reply": "2023-02-27T20:27:38.870623Z"
}
},
"outputs": [],
@@ -226,7 +226,7 @@
},
{
"cell_type": "markdown",
- "id": "af944dba",
+ "id": "d38340ff",
"metadata": {},
"source": [
"Edge attributes can be anything:"
@@ -235,13 +235,13 @@
{
"cell_type": "code",
"execution_count": 4,
- "id": "e2400ba2",
+ "id": "563e93a0",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-26T01:59:50.401191Z",
- "iopub.status.busy": "2023-02-26T01:59:50.400793Z",
- "iopub.status.idle": "2023-02-26T01:59:50.405671Z",
- "shell.execute_reply": "2023-02-26T01:59:50.404985Z"
+ "iopub.execute_input": "2023-02-27T20:27:38.873521Z",
+ "iopub.status.busy": "2023-02-27T20:27:38.873202Z",
+ "iopub.status.idle": "2023-02-27T20:27:38.876442Z",
+ "shell.execute_reply": "2023-02-27T20:27:38.875847Z"
}
},
"outputs": [],
@@ -253,7 +253,7 @@
},
{
"cell_type": "markdown",
- "id": "30ce4b31",
+ "id": "041a73e2",
"metadata": {},
"source": [
"You can add many edges at one time:"
@@ -262,13 +262,13 @@
{
"cell_type": "code",
"execution_count": 5,
- "id": "6f3061ba",
+ "id": "742c952f",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-26T01:59:50.408338Z",
- "iopub.status.busy": "2023-02-26T01:59:50.407936Z",
- "iopub.status.idle": "2023-02-26T01:59:50.411971Z",
- "shell.execute_reply": "2023-02-26T01:59:50.411340Z"
+ "iopub.execute_input": "2023-02-27T20:27:38.878905Z",
+ "iopub.status.busy": "2023-02-27T20:27:38.878583Z",
+ "iopub.status.idle": "2023-02-27T20:27:38.882514Z",
+ "shell.execute_reply": "2023-02-27T20:27:38.881906Z"
}
},
"outputs": [],
@@ -281,7 +281,7 @@
},
{
"cell_type": "markdown",
- "id": "cf4c1a29",
+ "id": "ede8209a",
"metadata": {},
"source": [
"See the Tutorial for more examples.\n",
@@ -346,13 +346,13 @@
{
"cell_type": "code",
"execution_count": 6,
- "id": "33060076",
+ "id": "191e4c79",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-26T01:59:50.414634Z",
- "iopub.status.busy": "2023-02-26T01:59:50.414126Z",
- "iopub.status.idle": "2023-02-26T01:59:50.418556Z",
- "shell.execute_reply": "2023-02-26T01:59:50.417890Z"
+ "iopub.execute_input": "2023-02-27T20:27:38.884850Z",
+ "iopub.status.busy": "2023-02-27T20:27:38.884532Z",
+ "iopub.status.idle": "2023-02-27T20:27:38.888761Z",
+ "shell.execute_reply": "2023-02-27T20:27:38.888268Z"
}
},
"outputs": [
@@ -373,7 +373,7 @@
},
{
"cell_type": "markdown",
- "id": "43a66e19",
+ "id": "e7dff3b1",
"metadata": {},
"source": [
"# Drawing\n",
@@ -394,19 +394,19 @@
{
"cell_type": "code",
"execution_count": 7,
- "id": "50d5b2a3",
+ "id": "031a0f23",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-26T01:59:50.421843Z",
- "iopub.status.busy": "2023-02-26T01:59:50.421512Z",
- "iopub.status.idle": "2023-02-26T01:59:51.018318Z",
- "shell.execute_reply": "2023-02-26T01:59:51.017614Z"
+ "iopub.execute_input": "2023-02-27T20:27:38.891286Z",
+ "iopub.status.busy": "2023-02-27T20:27:38.890749Z",
+ "iopub.status.idle": "2023-02-27T20:27:39.461288Z",
+ "shell.execute_reply": "2023-02-27T20:27:39.460654Z"
}
},
"outputs": [
{
"data": {
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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
@@ -426,7 +426,7 @@
},
{
"cell_type": "markdown",
- "id": "3a564e32",
+ "id": "388c86a6",
"metadata": {},
"source": [
"See the examples for more ideas.\n",
@@ -466,13 +466,13 @@
{
"cell_type": "code",
"execution_count": 8,
- "id": "8867e559",
+ "id": "9d6089b1",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-26T01:59:51.021335Z",
- "iopub.status.busy": "2023-02-26T01:59:51.020981Z",
- "iopub.status.idle": "2023-02-26T01:59:51.026075Z",
- "shell.execute_reply": "2023-02-26T01:59:51.025337Z"
+ "iopub.execute_input": "2023-02-27T20:27:39.464119Z",
+ "iopub.status.busy": "2023-02-27T20:27:39.463785Z",
+ "iopub.status.idle": "2023-02-27T20:27:39.468588Z",
+ "shell.execute_reply": "2023-02-27T20:27:39.467992Z"
}
},
"outputs": [
@@ -493,7 +493,7 @@
},
{
"cell_type": "markdown",
- "id": "475498e4",
+ "id": "eb62a513",
"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": "843dfcd2",
+ "id": "f7232475",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-26T01:59:51.028637Z",
- "iopub.status.busy": "2023-02-26T01:59:51.028418Z",
- "iopub.status.idle": "2023-02-26T01:59:51.033388Z",
- "shell.execute_reply": "2023-02-26T01:59:51.032689Z"
+ "iopub.execute_input": "2023-02-27T20:27:39.470994Z",
+ "iopub.status.busy": "2023-02-27T20:27:39.470785Z",
+ "iopub.status.idle": "2023-02-27T20:27:39.475235Z",
+ "shell.execute_reply": "2023-02-27T20:27:39.474154Z"
}
},
"outputs": [
diff --git a/searchindex.js b/searchindex.js
index c96dbce7..da76747e 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"docnames": ["auto_examples/3d_drawing/index", "auto_examples/3d_drawing/mayavi2_spring", "auto_examples/3d_drawing/plot_basic", "auto_examples/3d_drawing/sg_execution_times", "auto_examples/algorithms/index", "auto_examples/algorithms/plot_beam_search", "auto_examples/algorithms/plot_betweenness_centrality", "auto_examples/algorithms/plot_blockmodel", "auto_examples/algorithms/plot_circuits", "auto_examples/algorithms/plot_davis_club", "auto_examples/algorithms/plot_dedensification", "auto_examples/algorithms/plot_iterated_dynamical_systems", "auto_examples/algorithms/plot_krackhardt_centrality", "auto_examples/algorithms/plot_maximum_independent_set", "auto_examples/algorithms/plot_parallel_betweenness", "auto_examples/algorithms/plot_rcm", "auto_examples/algorithms/plot_snap", "auto_examples/algorithms/plot_subgraphs", "auto_examples/algorithms/sg_execution_times", "auto_examples/basic/index", "auto_examples/basic/plot_properties", "auto_examples/basic/plot_read_write", "auto_examples/basic/plot_simple_graph", "auto_examples/basic/sg_execution_times", "auto_examples/drawing/index", "auto_examples/drawing/plot_center_node", "auto_examples/drawing/plot_chess_masters", "auto_examples/drawing/plot_custom_node_icons", "auto_examples/drawing/plot_degree", "auto_examples/drawing/plot_directed", "auto_examples/drawing/plot_edge_colormap", "auto_examples/drawing/plot_ego_graph", "auto_examples/drawing/plot_eigenvalues", "auto_examples/drawing/plot_four_grids", "auto_examples/drawing/plot_house_with_colors", "auto_examples/drawing/plot_knuth_miles", "auto_examples/drawing/plot_labels_and_colors", "auto_examples/drawing/plot_multipartite_graph", "auto_examples/drawing/plot_node_colormap", "auto_examples/drawing/plot_rainbow_coloring", "auto_examples/drawing/plot_random_geometric_graph", "auto_examples/drawing/plot_sampson", "auto_examples/drawing/plot_selfloops", "auto_examples/drawing/plot_simple_path", "auto_examples/drawing/plot_spectral_grid", "auto_examples/drawing/plot_tsp", "auto_examples/drawing/plot_unix_email", "auto_examples/drawing/plot_weighted_graph", "auto_examples/drawing/sg_execution_times", "auto_examples/external/index", "auto_examples/external/javascript_force", "auto_examples/external/plot_igraph", "auto_examples/external/sg_execution_times", "auto_examples/geospatial/extended_description", "auto_examples/geospatial/index", "auto_examples/geospatial/plot_delaunay", "auto_examples/geospatial/plot_lines", "auto_examples/geospatial/plot_osmnx", "auto_examples/geospatial/plot_points", "auto_examples/geospatial/plot_polygons", "auto_examples/geospatial/sg_execution_times", "auto_examples/graph/index", "auto_examples/graph/plot_dag_layout", "auto_examples/graph/plot_degree_sequence", "auto_examples/graph/plot_erdos_renyi", "auto_examples/graph/plot_expected_degree_sequence", "auto_examples/graph/plot_football", "auto_examples/graph/plot_karate_club", "auto_examples/graph/plot_morse_trie", "auto_examples/graph/plot_napoleon_russian_campaign", "auto_examples/graph/plot_roget", "auto_examples/graph/plot_triad_types", "auto_examples/graph/plot_words", "auto_examples/graph/sg_execution_times", "auto_examples/graphviz_drawing/index", "auto_examples/graphviz_drawing/plot_attributes", "auto_examples/graphviz_drawing/plot_conversion", "auto_examples/graphviz_drawing/plot_grid", "auto_examples/graphviz_drawing/plot_mini_atlas", "auto_examples/graphviz_drawing/sg_execution_times", "auto_examples/graphviz_layout/index", "auto_examples/graphviz_layout/plot_atlas", "auto_examples/graphviz_layout/plot_circular_tree", "auto_examples/graphviz_layout/plot_decomposition", "auto_examples/graphviz_layout/plot_giant_component", "auto_examples/graphviz_layout/plot_lanl_routes", "auto_examples/graphviz_layout/sg_execution_times", "auto_examples/index", "auto_examples/subclass/index", "auto_examples/subclass/plot_antigraph", 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1170, 1171, 1172, 1173, 1180, 1181, 1183, 1199, 1202, 1203, 1204, 1212, 1213, 1223, 1224, 1225, 1228, 1241, 1252, 1254, 1256, 1264, 1269, 1270, 1275, 1278, 1281, 1282, 1284, 1285, 1287, 1288, 1289, 1290, 1301, 1302, 1303, 1305, 1307, 1308, 1309, 1326, 1327, 1329, 1330, 1332, 1334, 1335, 1336, 1339, 1340, 1353, 1355, 1358, 1360, 1362, 1363, 1368, 1369, 1377, 1378, 1384, 1386, 1387, 1388, 1390, 1393, 1395, 1396, 1401, 1402, 1403, 1404, 1405, 1408, 1411, 1413, 1414, 1415, 1417, 1418, 1421, 1434, 1436], "more": [8, 44, 54, 68, 87, 93, 94, 95, 98, 100, 101, 102, 103, 104, 106, 108, 110, 111, 112, 115, 116, 122, 128, 129, 144, 166, 173, 199, 200, 203, 205, 216, 217, 219, 220, 221, 222, 231, 232, 236, 257, 268, 278, 279, 282, 290, 300, 311, 315, 325, 326, 337, 340, 363, 380, 385, 387, 389, 391, 392, 394, 401, 407, 408, 409, 424, 429, 430, 434, 435, 439, 462, 466, 482, 522, 523, 561, 562, 583, 584, 585, 592, 595, 616, 621, 628, 633, 637, 655, 658, 662, 663, 664, 678, 681, 685, 693, 700, 701, 705, 713, 719, 720, 737, 739, 750, 762, 784, 788, 798, 864, 870, 888, 889, 892, 893, 909, 915, 926, 927, 928, 929, 945, 951, 970, 971, 974, 975, 991, 997, 1009, 1010, 1011, 1012, 1040, 1042, 1043, 1045, 1046, 1074, 1097, 1103, 1119, 1122, 1123, 1126, 1136, 1137, 1138, 1139, 1141, 1143, 1144, 1145, 1146, 1147, 1148, 1149, 1191, 1198, 1199, 1212, 1220, 1223, 1224, 1225, 1278, 1293, 1294, 1301, 1302, 1303, 1329, 1332, 1334, 1343, 1351, 1354, 1355, 1356, 1387, 1398, 1403, 1404, 1406, 1407, 1408, 1410, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434, 1435, 1436], "express": [8, 93, 111, 185, 316, 331, 332, 385, 386, 620, 621, 875, 918, 957, 1001, 1205, 1293, 1332], "than": [8, 11, 35, 44, 56, 98, 100, 102, 103, 104, 116, 129, 143, 144, 145, 162, 200, 215, 216, 217, 219, 220, 222, 228, 232, 236, 242, 257, 278, 279, 282, 289, 290, 298, 299, 300, 305, 307, 308, 311, 312, 316, 317, 322, 325, 326, 328, 330, 331, 332, 343, 354, 360, 363, 376, 382, 383, 385, 386, 387, 389, 391, 392, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 427, 428, 431, 437, 466, 470, 471, 502, 529, 539, 561, 562, 583, 584, 585, 592, 627, 628, 637, 638, 654, 655, 658, 660, 661, 675, 678, 680, 681, 683, 685, 688, 692, 694, 695, 696, 700, 701, 713, 733, 737, 739, 750, 754, 763, 788, 889, 927, 949, 971, 995, 1010, 1041, 1045, 1046, 1063, 1105, 1141, 1152, 1160, 1168, 1171, 1173, 1178, 1180, 1191, 1193, 1200, 1204, 1232, 1236, 1237, 1242, 1243, 1244, 1245, 1281, 1282, 1302, 1303, 1332, 1334, 1351, 1354, 1355, 1356, 1359, 1360, 1364, 1371, 1372, 1385, 1390, 1404, 1411, 1413, 1414, 1417, 1422, 1432, 1434], "worst": [8, 211, 212, 213, 222, 229, 236, 265, 294, 295, 340, 347, 348, 349, 442, 515, 517, 518, 519, 520], "reus": [8, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 1137, 1138, 1144, 1145, 1146, 1147, 1148, 1334, 1411], "subcircuit": 8, "multipl": [8, 11, 26, 41, 46, 78, 94, 95, 100, 104, 108, 110, 144, 158, 159, 167, 176, 189, 196, 208, 288, 312, 359, 387, 388, 425, 445, 449, 460, 462, 466, 487, 488, 489, 596, 597, 599, 617, 618, 643, 645, 680, 692, 693, 699, 707, 740, 764, 788, 798, 858, 859, 865, 871, 879, 886, 894, 903, 904, 910, 925, 930, 939, 940, 946, 948, 952, 961, 962, 965, 966, 968, 976, 985, 986, 992, 994, 1005, 1006, 1008, 1013, 1040, 1042, 1043, 1048, 1049, 1105, 1106, 1108, 1127, 1129, 1133, 1141, 1143, 1222, 1223, 1225, 1291, 1297, 1302, 1304, 1332, 1358, 1384, 1402, 1414, 1415, 1421, 1422, 1426, 1434, 1436], "wherea": [8, 104, 684, 764, 788, 793, 1171, 1426], "cannot": [8, 102, 104, 128, 133, 200, 233, 301, 364, 396, 478, 583, 584, 585, 586, 634, 724, 889, 927, 936, 971, 982, 1010, 1046, 1171, 1214, 1215, 1302, 1304, 1308, 1309, 1332, 1351, 1353, 1354, 1355, 1356], "subformula": 8, "onc": [8, 39, 55, 56, 89, 94, 95, 100, 101, 113, 128, 200, 228, 231, 232, 233, 247, 248, 362, 376, 382, 390, 424, 425, 430, 490, 493, 494, 583, 584, 585, 654, 680, 681, 719, 720, 889, 927, 971, 1010, 1049, 1069, 1090, 1223, 1317, 1332, 1387, 1412, 1416], "thu": [8, 89, 102, 104, 116, 216, 217, 221, 257, 259, 333, 420, 421, 429, 430, 464, 479, 502, 514, 585, 681, 700, 701, 762, 764, 798, 1040, 1042, 1043, 1046, 1090, 1115, 1154, 1221, 1223, 1240, 1284, 1285, 1302, 1334, 1411, 1414, 1416, 1434], "wai": [8, 28, 53, 54, 56, 76, 87, 89, 94, 98, 100, 101, 102, 103, 104, 105, 106, 108, 111, 113, 116, 133, 153, 158, 159, 166, 185, 227, 282, 298, 299, 316, 332, 339, 358, 590, 600, 617, 620, 680, 693, 732, 762, 793, 798, 856, 858, 859, 864, 875, 901, 903, 904, 909, 917, 918, 937, 939, 940, 945, 957, 983, 985, 986, 991, 999, 1001, 1040, 1042, 1043, 1044, 1100, 1171, 1219, 1221, 1223, 1245, 1268, 1275, 1278, 1332, 1334, 1336, 1387, 1402, 1403, 1413, 1415, 1420, 1436], "infeas": [8, 424], "circuit_to_formula": 8, "dag_to_branch": [8, 760, 1417], "transfer": [8, 203, 205, 231, 232, 471, 892, 893, 928, 929, 974, 975, 1011, 1012, 1429], "oper": [8, 31, 53, 96, 102, 113, 116, 169, 185, 190, 228, 376, 425, 462, 548, 549, 550, 554, 555, 556, 579, 597, 600, 603, 673, 674, 675, 676, 681, 682, 760, 788, 867, 875, 880, 912, 918, 948, 957, 962, 994, 1001, 1039, 1071, 1091, 1106, 1170, 1224, 1225, 1301, 1308, 1325, 1329, 1331, 1332, 1402, 1403, 1409, 1413, 1414, 1415, 1416, 1417, 1420, 1421, 1422, 1423, 1426], "variabl": [8, 95, 133, 375, 532, 542, 620, 621, 734, 798, 1040, 1041, 1042, 1043, 1045, 1127, 1129, 1160, 1171, 1332, 1417, 1421, 1422, 1423, 1429], "formula_to_str": 8, "_to_str": 8, "root": [8, 68, 85, 294, 295, 340, 389, 391, 392, 396, 451, 462, 561, 579, 611, 673, 675, 680, 706, 730, 732, 741, 762, 793, 1122, 1123, 1131, 1132, 1151, 1153, 1241, 1277, 1278, 1329, 1371, 1372, 1402, 1415, 1416, 1417, 1421, 1422, 1432, 1434], "children": [8, 462, 579, 1151, 1161, 1278, 1371, 1372, 1387], "otherwis": [8, 93, 111, 147, 150, 172, 179, 185, 186, 199, 218, 231, 250, 251, 285, 298, 299, 304, 307, 308, 312, 316, 317, 323, 324, 325, 326, 327, 328, 331, 332, 345, 355, 360, 395, 396, 397, 398, 399, 400, 412, 413, 414, 420, 421, 424, 427, 428, 464, 465, 466, 472, 481, 490, 492, 496, 497, 498, 500, 501, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 523, 557, 564, 565, 570, 574, 576, 586, 588, 590, 599, 603, 618, 620, 621, 635, 665, 675, 689, 690, 691, 698, 700, 701, 736, 737, 738, 739, 753, 850, 869, 875, 876, 888, 895, 914, 918, 919, 926, 931, 936, 950, 957, 958, 970, 977, 982, 996, 1001, 1002, 1009, 1071, 1094, 1127, 1141, 1143, 1171, 1191, 1203, 1223, 1276, 1288, 1289, 1290, 1313, 1315, 1318, 1348, 1362, 1363, 1382, 1387, 1388, 1418, 1422, 1436], "child": [8, 1153, 1278, 1387], "must": [8, 11, 94, 95, 96, 100, 101, 104, 111, 152, 153, 159, 162, 172, 205, 207, 208, 215, 216, 217, 220, 231, 232, 233, 253, 254, 258, 259, 260, 261, 262, 263, 265, 268, 269, 270, 272, 274, 277, 282, 286, 298, 299, 307, 308, 316, 317, 318, 319, 320, 325, 326, 329, 331, 332, 344, 363, 364, 365, 380, 384, 387, 393, 412, 413, 414, 415, 427, 431, 442, 473, 474, 475, 476, 477, 547, 548, 549, 550, 551, 552, 553, 555, 557, 558, 559, 560, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 579, 580, 581, 582, 586, 587, 588, 589, 590, 591, 595, 599, 601, 603, 604, 605, 606, 617, 628, 629, 634, 635, 637, 638, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 673, 674, 675, 676, 682, 692, 694, 700, 701, 709, 723, 736, 737, 738, 739, 791, 798, 855, 856, 859, 869, 893, 894, 900, 901, 904, 914, 930, 936, 940, 975, 976, 982, 986, 1013, 1040, 1041, 1042, 1043, 1066, 1074, 1088, 1105, 1139, 1143, 1152, 1168, 1171, 1179, 1182, 1192, 1194, 1196, 1199, 1203, 1205, 1215, 1219, 1223, 1225, 1241, 1245, 1246, 1276, 1281, 1282, 1283, 1284, 1285, 1301, 1302, 1304, 1313, 1315, 1316, 1317, 1318, 1321, 1339, 1343, 1344, 1345, 1346, 1365, 1367, 1368, 1369, 1370, 1371, 1372, 1382, 1402, 1403, 1404, 1416, 1436], "NOT": [8, 111, 200, 551, 552, 553, 750, 889, 927, 971, 1010], "util": [8, 15, 37, 45, 46, 94, 98, 103, 104, 230, 231, 232, 317, 376, 425, 427, 428, 431, 462, 498, 680, 681, 760, 1047, 1127, 1248, 1305, 1307, 1309, 1316, 1325, 1326, 1327, 1331, 1411, 1415, 1416, 1420, 1422, 1425, 1428, 1434], "arbitrary_el": [8, 1401, 1422], "nb": [8, 1337, 1340], "left": [8, 72, 116, 184, 312, 313, 323, 325, 326, 387, 561, 562, 586, 618, 690, 691, 741, 1109, 1140, 1142, 1152, 1185, 1212, 1286, 1361, 1364, 1387, 1413], "right": [8, 72, 111, 112, 116, 153, 207, 323, 327, 387, 429, 430, 502, 561, 562, 586, 587, 589, 590, 617, 618, 690, 691, 741, 856, 937, 983, 1140, 1142, 1152, 1161, 1163, 1185, 1212, 1219, 1221, 1276, 1286, 1387, 1388], "littl": [8, 95, 106, 299, 308], "mislead": 8, "That": [8, 98, 106, 133, 166, 213, 222, 228, 296, 387, 438, 467, 527, 537, 557, 590, 659, 673, 674, 675, 676, 693, 706, 719, 793, 864, 909, 945, 991, 1049, 1168, 1216, 1302, 1396, 1413, 1418], "okai": 8, "becaus": [8, 11, 55, 70, 95, 100, 102, 103, 104, 113, 133, 162, 216, 217, 221, 256, 312, 380, 389, 391, 392, 396, 413, 414, 429, 496, 500, 501, 502, 512, 571, 587, 589, 617, 618, 634, 654, 936, 982, 1041, 1242, 1279, 1302, 1309, 1332, 1351, 1356, 1413, 1416, 1425, 1434], "AND": [8, 111, 600, 750, 764], "OR": [8, 111, 158, 176, 189, 858, 871, 879, 903, 939, 949, 952, 961, 985, 995], "symmetr": [8, 146, 149, 238, 547, 588, 595, 763, 1179, 1198, 1241, 1252, 1256, 1257, 1262, 1264, 1275, 1326, 1327, 1395], "It": [8, 53, 57, 59, 93, 94, 95, 98, 100, 102, 103, 105, 108, 111, 113, 116, 133, 173, 185, 208, 215, 216, 217, 230, 231, 232, 250, 261, 262, 263, 265, 279, 311, 317, 325, 326, 328, 345, 348, 349, 353, 355, 414, 416, 417, 418, 419, 420, 421, 431, 440, 442, 454, 459, 466, 482, 498, 502, 510, 532, 542, 547, 561, 562, 567, 568, 569, 584, 590, 596, 597, 600, 602, 603, 617, 621, 630, 631, 632, 654, 660, 661, 665, 673, 676, 694, 719, 720, 721, 762, 763, 764, 793, 798, 870, 875, 894, 915, 918, 930, 951, 957, 976, 997, 1001, 1013, 1015, 1016, 1021, 1040, 1041, 1042, 1043, 1057, 1120, 1127, 1129, 1176, 1180, 1206, 1207, 1212, 1213, 1216, 1223, 1229, 1233, 1240, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256, 1257, 1259, 1260, 1264, 1267, 1269, 1270, 1275, 1281, 1282, 1283, 1286, 1302, 1303, 1329, 1330, 1332, 1334, 1349, 1390, 1391, 1402, 1404, 1407, 1411, 1413, 1416, 1417, 1418, 1420, 1421, 1422, 1436], "just": [8, 100, 103, 104, 105, 106, 185, 200, 340, 376, 441, 466, 561, 562, 579, 662, 663, 664, 694, 793, 875, 889, 918, 927, 948, 957, 962, 971, 994, 1001, 1010, 1045, 1123, 1128, 1132, 1235, 1284, 1285, 1302, 1334, 1402, 1413, 1415], "operand": 8, "predict": [8, 569, 570, 571, 572, 573, 574, 575, 576, 593, 594, 760, 1331, 1411, 1415, 1421], "henc": [8, 169, 190, 523, 867, 880, 912, 948, 962, 994, 1062, 1127, 1128, 1129, 1208, 1391], "doe": [8, 78, 94, 95, 100, 102, 103, 104, 105, 115, 116, 133, 148, 154, 155, 166, 169, 190, 208, 209, 228, 229, 230, 231, 232, 233, 294, 309, 341, 342, 344, 345, 354, 359, 375, 384, 387, 412, 416, 428, 452, 471, 496, 497, 498, 499, 500, 501, 502, 504, 505, 508, 509, 511, 512, 513, 514, 536, 546, 551, 552, 553, 566, 568, 585, 586, 588, 591, 603, 614, 628, 629, 680, 693, 695, 696, 700, 701, 719, 720, 723, 724, 725, 726, 727, 728, 764, 864, 867, 880, 894, 909, 912, 930, 945, 948, 962, 976, 991, 994, 1013, 1041, 1046, 1069, 1073, 1075, 1084, 1105, 1106, 1108, 1109, 1110, 1112, 1117, 1179, 1181, 1183, 1198, 1213, 1228, 1229, 1233, 1235, 1240, 1247, 1302, 1306, 1309, 1332, 1339, 1340, 1347, 1348, 1350, 1357, 1359, 1360, 1361, 1362, 1363, 1364, 1377, 1385, 1386, 1389, 1391, 1402, 1413, 1414, 1415, 1419, 1426, 1436], "necessarili": [8, 100, 343, 453, 485, 561, 562, 643, 645, 1041, 1225], "behav": [8, 89, 104, 160, 191, 201, 221, 353, 860, 881, 890, 905, 941, 963, 972, 987, 1235, 1302, 1404, 1413], "everi": [8, 11, 58, 89, 94, 110, 113, 121, 145, 158, 162, 178, 212, 213, 221, 222, 230, 231, 232, 236, 244, 265, 288, 296, 301, 325, 326, 345, 354, 382, 399, 439, 441, 442, 452, 464, 473, 474, 475, 476, 477, 479, 485, 486, 493, 514, 518, 567, 608, 616, 617, 621, 634, 635, 637, 638, 665, 687, 689, 690, 719, 720, 793, 858, 903, 939, 985, 1055, 1056, 1057, 1073, 1074, 1075, 1088, 1089, 1105, 1106, 1108, 1109, 1110, 1111, 1112, 1113, 1114, 1117, 1118, 1119, 1120, 1154, 1168, 1201, 1222, 1223, 1263, 1270, 1284, 1285, 1302, 1416], "left_subformula": 8, "right_subformula": 8, "in_degre": [8, 167, 189, 493, 680, 865, 879, 946, 961, 1183, 1213, 1214, 1413, 1415, 1416, 1436], "ha": [8, 11, 17, 45, 68, 89, 92, 94, 95, 96, 98, 100, 101, 102, 103, 104, 106, 108, 111, 113, 117, 121, 128, 153, 162, 166, 167, 174, 175, 176, 185, 189, 199, 208, 213, 215, 216, 220, 221, 227, 228, 230, 231, 232, 233, 236, 239, 240, 241, 242, 243, 244, 245, 248, 250, 253, 270, 272, 273, 274, 275, 276, 277, 283, 290, 292, 294, 295, 296, 301, 306, 311, 325, 327, 333, 345, 354, 357, 358, 365, 366, 367, 375, 380, 382, 383, 385, 386, 387, 388, 393, 395, 396, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 426, 429, 430, 431, 441, 452, 460, 462, 468, 469, 470, 473, 474, 475, 476, 477, 478, 479, 482, 493, 494, 495, 496, 497, 498, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 524, 566, 568, 579, 580, 583, 592, 595, 607, 609, 612, 613, 624, 625, 626, 630, 631, 632, 634, 635, 636, 637, 638, 640, 648, 649, 651, 654, 659, 660, 684, 690, 692, 694, 699, 713, 719, 720, 731, 732, 733, 741, 751, 788, 793, 856, 864, 865, 871, 875, 879, 888, 894, 901, 909, 910, 918, 926, 930, 937, 945, 946, 950, 952, 957, 961, 970, 976, 983, 991, 992, 996, 1001, 1009, 1013, 1043, 1046, 1048, 1069, 1071, 1073, 1075, 1078, 1083, 1087, 1101, 1102, 1104, 1105, 1106, 1108, 1125, 1136, 1151, 1160, 1166, 1168, 1171, 1182, 1186, 1191, 1199, 1201, 1202, 1203, 1204, 1205, 1213, 1216, 1217, 1221, 1223, 1228, 1240, 1245, 1249, 1250, 1254, 1255, 1260, 1265, 1267, 1270, 1273, 1275, 1276, 1278, 1281, 1282, 1283, 1284, 1285, 1287, 1288, 1289, 1290, 1291, 1292, 1295, 1297, 1299, 1302, 1306, 1332, 1334, 1336, 1339, 1340, 1359, 1360, 1377, 1378, 1385, 1387, 1390, 1402, 1403, 1404, 1407, 1412, 1413, 1414, 1415, 1416, 1418, 1422, 1423, 1425, 1432, 1434], "output": [8, 14, 17, 90, 94, 102, 103, 104, 110, 198, 288, 289, 347, 376, 382, 496, 500, 501, 511, 512, 577, 590, 679, 680, 693, 724, 1048, 1199, 1203, 1205, 1275, 1302, 1332, 1340, 1347, 1350, 1361, 1364, 1388, 1408, 1411, 1413, 1415, 1420, 1422, 1423, 1435, 1436], "two": [8, 11, 13, 17, 28, 35, 39, 44, 55, 56, 58, 59, 66, 68, 72, 89, 94, 96, 100, 101, 103, 106, 110, 113, 115, 116, 121, 133, 152, 172, 176, 185, 186, 189, 203, 208, 212, 213, 214, 215, 216, 217, 218, 221, 222, 227, 228, 231, 232, 233, 246, 250, 252, 253, 254, 258, 259, 261, 262, 263, 266, 270, 271, 272, 273, 274, 275, 276, 277, 283, 286, 287, 288, 290, 306, 312, 316, 317, 323, 328, 331, 332, 339, 343, 345, 347, 353, 354, 360, 361, 379, 382, 383, 385, 393, 413, 414, 421, 425, 430, 431, 432, 433, 444, 445, 446, 447, 449, 454, 455, 456, 459, 464, 473, 474, 475, 476, 477, 478, 482, 493, 496, 500, 501, 502, 504, 505, 508, 510, 511, 512, 513, 523, 547, 551, 552, 553, 557, 561, 562, 563, 564, 565, 566, 567, 568, 570, 571, 574, 576, 580, 586, 587, 588, 589, 590, 595, 600, 607, 609, 610, 612, 613, 617, 621, 628, 629, 631, 634, 635, 637, 638, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 673, 674, 675, 676, 677, 678, 682, 694, 696, 733, 734, 740, 741, 762, 763, 764, 782, 788, 793, 798, 855, 869, 871, 875, 876, 879, 892, 894, 900, 914, 918, 919, 928, 930, 936, 948, 950, 952, 957, 958, 961, 962, 974, 976, 982, 994, 996, 1001, 1002, 1011, 1013, 1022, 1023, 1024, 1025, 1039, 1040, 1042, 1043, 1059, 1087, 1091, 1101, 1103, 1104, 1109, 1110, 1111, 1112, 1117, 1119, 1140, 1152, 1153, 1155, 1157, 1158, 1162, 1180, 1191, 1192, 1199, 1200, 1201, 1202, 1203, 1204, 1205, 1210, 1213, 1216, 1217, 1221, 1223, 1224, 1249, 1250, 1259, 1277, 1278, 1281, 1282, 1300, 1301, 1302, 1329, 1330, 1332, 1334, 1365, 1366, 1369, 1402, 1403, 1404, 1406, 1411, 1413, 1414, 1415, 1416, 1419, 1420, 1422, 1434], "layer": [8, 37, 56, 62, 68, 104, 440, 707, 1041, 1112, 1429], "third": [8, 103, 106, 115, 250, 424, 469, 587, 589, 736, 738, 1223, 1232, 1268, 1269, 1332, 1416], "appear": [8, 84, 94, 96, 100, 101, 103, 180, 205, 231, 232, 239, 244, 247, 248, 278, 365, 366, 367, 380, 453, 454, 455, 457, 468, 472, 586, 587, 589, 590, 677, 681, 709, 732, 736, 738, 893, 975, 1039, 1045, 1091, 1105, 1142, 1156, 1158, 1160, 1163, 1165, 1193, 1194, 1283, 1288, 1329, 1330, 1351, 1354, 1355, 1356, 1390, 1416, 1422, 1423], "both": [8, 53, 56, 93, 94, 95, 101, 102, 103, 104, 116, 162, 165, 205, 215, 216, 217, 218, 241, 258, 259, 260, 265, 283, 287, 288, 290, 339, 360, 381, 385, 417, 419, 420, 421, 425, 429, 442, 472, 504, 508, 547, 577, 583, 600, 602, 603, 604, 605, 606, 607, 608, 609, 612, 613, 617, 623, 637, 638, 655, 656, 657, 678, 713, 722, 762, 763, 764, 784, 893, 975, 1023, 1039, 1069, 1078, 1083, 1087, 1091, 1100, 1123, 1132, 1150, 1171, 1195, 1198, 1205, 1213, 1216, 1217, 1219, 1221, 1288, 1302, 1332, 1334, 1364, 1369, 1370, 1395, 1402, 1404, 1411, 1422, 1425, 1426, 1434, 1436], "negat": 8, "sole": [8, 788, 1284, 1285, 1332], "fourth": [8, 231, 232, 1332, 1413], "digraph": [8, 10, 11, 17, 22, 26, 42, 46, 57, 62, 68, 70, 71, 83, 89, 102, 103, 116, 133, 152, 153, 157, 158, 159, 161, 163, 164, 166, 167, 169, 171, 172, 173, 176, 177, 186, 187, 188, 189, 190, 193, 194, 195, 196, 197, 199, 200, 203, 205, 208, 209, 217, 228, 230, 231, 232, 241, 247, 248, 300, 309, 315, 319, 320, 322, 329, 330, 336, 337, 338, 339, 341, 342, 344, 345, 390, 393, 395, 398, 399, 400, 401, 403, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 432, 433, 439, 452, 454, 455, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 483, 484, 494, 496, 497, 498, 499, 500, 501, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 515, 516, 520, 521, 525, 557, 568, 577, 578, 579, 590, 592, 615, 617, 625, 632, 638, 645, 646, 654, 658, 659, 660, 661, 665, 680, 690, 692, 695, 698, 699, 700, 701, 702, 703, 704, 708, 709, 710, 711, 713, 718, 719, 720, 721, 723, 724, 725, 726, 727, 728, 742, 743, 746, 747, 748, 749, 750, 751, 752, 754, 762, 791, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 906, 907, 908, 909, 910, 913, 914, 915, 917, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 937, 938, 939, 940, 942, 943, 944, 945, 951, 959, 960, 966, 967, 968, 969, 970, 971, 975, 976, 977, 978, 980, 981, 983, 984, 985, 986, 988, 989, 990, 991, 992, 997, 999, 1003, 1004, 1006, 1007, 1008, 1009, 1010, 1013, 1038, 1040, 1041, 1042, 1043, 1044, 1045, 1055, 1065, 1069, 1073, 1075, 1078, 1083, 1086, 1087, 1101, 1102, 1104, 1121, 1141, 1156, 1160, 1174, 1175, 1176, 1179, 1183, 1184, 1186, 1188, 1189, 1190, 1191, 1195, 1223, 1276, 1278, 1279, 1280, 1289, 1290, 1293, 1296, 1298, 1304, 1329, 1332, 1339, 1343, 1348, 1362, 1363, 1368, 1371, 1372, 1377, 1387, 1388, 1402, 1408, 1410, 1411, 1413, 1414, 1415, 1416, 1417, 1418, 1420, 1421, 1422, 1423, 1425, 1426, 1433, 1434, 1436], "add_nod": [8, 11, 27, 35, 70, 75, 90, 103, 158, 185, 247, 341, 342, 400, 424, 493, 494, 498, 506, 507, 510, 524, 525, 607, 609, 612, 613, 693, 798, 858, 875, 903, 918, 939, 957, 985, 1001, 1040, 1042, 1043, 1089, 1281, 1332, 1351, 1416, 1417, 1426, 1436], "get_node_attribut": [8, 40, 45, 72, 1219, 1413], "600": [8, 10, 12], "font_siz": [8, 17, 22, 26, 33, 36, 39, 46, 47, 1139, 1140, 1142], "22": [8, 36, 65, 67, 327, 348, 385, 386, 1277, 1329, 1412, 1417, 1421, 1431], "multipartite_layout": [8, 37, 62, 68, 1421, 1423, 1429], "subset_kei": [8, 37, 62, 68, 1112], "equal": [8, 37, 82, 145, 215, 216, 217, 231, 232, 239, 270, 272, 274, 277, 289, 298, 299, 301, 304, 307, 308, 311, 312, 313, 316, 317, 321, 324, 325, 326, 331, 332, 333, 375, 412, 413, 414, 415, 420, 421, 430, 473, 476, 478, 493, 496, 497, 498, 500, 501, 504, 505, 506, 507, 508, 509, 510, 511, 512, 527, 537, 547, 554, 555, 556, 557, 570, 574, 607, 625, 659, 673, 674, 675, 676, 689, 690, 691, 692, 723, 724, 742, 743, 755, 763, 793, 1115, 1119, 1168, 1171, 1204, 1210, 1236, 1245, 1277, 1286, 1297, 1313, 1315, 1318, 1407, 1408], "112": [8, 18, 1222], "plot_circuit": [8, 18], "southern": [9, 1271], "women": [9, 1271, 1407, 1415], "unipartit": [9, 116, 259, 260, 360], "properti": [9, 11, 19, 23, 34, 64, 87, 102, 103, 104, 113, 135, 160, 162, 167, 169, 176, 177, 180, 185, 189, 190, 191, 201, 285, 286, 287, 288, 289, 327, 365, 366, 367, 390, 478, 502, 547, 571, 621, 687, 860, 865, 867, 871, 872, 875, 879, 880, 881, 890, 905, 910, 912, 918, 941, 946, 948, 952, 953, 957, 961, 962, 963, 972, 987, 992, 994, 1001, 1088, 1089, 1125, 1140, 1142, 1199, 1208, 1223, 1225, 1275, 1289, 1290, 1332, 1334, 1391, 1407, 1414, 1415, 1416, 1417, 1422, 1426, 1436], "These": [9, 53, 59, 74, 80, 87, 94, 95, 106, 338, 387, 496, 514, 561, 673, 675, 734, 750, 781, 788, 1041, 1048, 1050, 1329, 1332, 1393, 1395, 1401, 1403, 1404, 1406, 1408, 1413, 1414, 1420, 1436], "were": [9, 66, 89, 100, 102, 105, 216, 217, 221, 290, 306, 412, 439, 462, 590, 965, 1005, 1205, 1402, 1404, 1408, 1411, 1414, 1415, 1416, 1422, 1425], "et": [9, 211, 227, 228, 316, 317, 323, 332, 336, 339, 347, 354, 360, 375, 382, 383, 425, 427, 428, 453, 571, 593, 594, 683, 684, 686, 695, 1208], "al": [9, 211, 227, 228, 316, 317, 323, 332, 336, 339, 347, 354, 360, 375, 382, 383, 425, 427, 428, 453, 571, 593, 594, 683, 684, 686, 695, 1208, 1416, 1422], "1930": [9, 1405], "thei": [9, 55, 59, 66, 72, 93, 94, 95, 98, 100, 101, 102, 103, 104, 105, 106, 108, 133, 152, 166, 208, 214, 221, 250, 286, 288, 289, 297, 298, 299, 302, 303, 307, 308, 309, 310, 353, 364, 376, 393, 398, 429, 453, 454, 455, 456, 466, 467, 473, 474, 475, 476, 477, 498, 506, 507, 510, 514, 548, 549, 550, 561, 562, 578, 585, 588, 590, 602, 606, 677, 678, 706, 719, 752, 762, 788, 855, 864, 894, 900, 909, 930, 936, 945, 965, 976, 982, 991, 1005, 1013, 1039, 1041, 1069, 1088, 1091, 1112, 1123, 1127, 1128, 1129, 1132, 1139, 1141, 1143, 1157, 1165, 1171, 1199, 1203, 1204, 1223, 1277, 1278, 1329, 1334, 1359, 1360, 1362, 1363, 1365, 1369, 1403, 1405, 1411, 1413, 1415, 1418, 1423, 1436], "repres": [9, 11, 27, 44, 53, 55, 58, 68, 93, 100, 108, 116, 231, 232, 266, 282, 284, 287, 288, 289, 292, 293, 340, 352, 363, 364, 365, 379, 380, 382, 383, 384, 387, 388, 393, 450, 454, 455, 457, 459, 462, 467, 468, 496, 497, 500, 501, 502, 504, 505, 508, 509, 511, 512, 523, 567, 579, 580, 581, 582, 588, 590, 611, 617, 620, 621, 658, 662, 666, 669, 678, 681, 693, 694, 697, 699, 700, 701, 702, 704, 730, 732, 733, 736, 738, 741, 754, 788, 793, 798, 1022, 1023, 1024, 1025, 1040, 1041, 1042, 1043, 1048, 1084, 1105, 1146, 1157, 1191, 1199, 1200, 1202, 1203, 1204, 1205, 1215, 1223, 1246, 1249, 1252, 1256, 1264, 1273, 1275, 1278, 1279, 1284, 1285, 1329, 1330, 1332, 1335, 1336, 1352, 1353, 1387, 1388, 1396, 1402, 1415], "observ": [9, 14, 133, 224, 1423, 1436], "attend": 9, "14": [9, 11, 17, 20, 26, 39, 45, 65, 67, 72, 230, 231, 232, 348, 385, 386, 407, 408, 503, 621, 692, 1156, 1248, 1256, 1268, 1415, 1417, 1436], "event": [9, 26, 100, 101, 111, 1171, 1235, 1306], "18": [9, 45, 65, 67, 94, 325, 326, 347, 348, 385, 386, 620, 1175, 1255, 1261, 1264, 1266, 1269, 1275, 1402, 1415, 1425, 1426, 1430, 1436], "bipartit": [9, 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, 291, 352, 353, 360, 379, 441, 442, 445, 583, 590, 760, 1046, 1109, 1157, 1209, 1210, 1211, 1271, 1331, 1404, 1407, 1408, 1409, 1410, 1415, 1416, 1420, 1422, 1426, 1430, 1434], "biadjac": [9, 283, 284, 1409, 1415], "7": [9, 12, 13, 15, 20, 26, 36, 45, 47, 64, 65, 66, 67, 69, 90, 100, 102, 103, 116, 126, 152, 159, 171, 172, 193, 208, 233, 269, 298, 300, 315, 323, 329, 334, 335, 341, 342, 344, 348, 364, 376, 382, 393, 405, 412, 415, 416, 417, 425, 426, 427, 428, 443, 447, 448, 485, 498, 503, 510, 513, 514, 557, 583, 588, 620, 621, 632, 654, 660, 665, 673, 676, 682, 697, 705, 708, 709, 710, 732, 749, 752, 763, 798, 855, 859, 868, 869, 883, 894, 900, 904, 913, 914, 917, 922, 930, 936, 940, 949, 976, 982, 986, 995, 999, 1013, 1040, 1042, 1043, 1045, 1055, 1056, 1088, 1103, 1107, 1154, 1218, 1248, 1254, 1256, 1257, 1261, 1264, 1266, 1279, 1329, 1332, 1336, 1345, 1346, 1351, 1354, 1355, 1356, 1388, 1390, 1401, 1403, 1411, 1412, 1414, 1417, 1418, 1419, 1420, 1421, 1422, 1434, 1436], "12": [9, 11, 20, 26, 45, 51, 56, 59, 65, 66, 67, 90, 92, 94, 230, 231, 232, 266, 347, 348, 382, 383, 394, 401, 407, 408, 409, 451, 488, 503, 518, 570, 574, 576, 608, 618, 1055, 1056, 1057, 1139, 1142, 1156, 1250, 1251, 1255, 1260, 1263, 1269, 1341, 1415, 1417, 1421, 1436], "9": [9, 11, 12, 13, 20, 26, 36, 45, 47, 64, 65, 66, 67, 69, 83, 90, 102, 103, 112, 116, 126, 233, 294, 296, 341, 342, 344, 348, 349, 358, 376, 382, 407, 408, 426, 440, 451, 496, 498, 503, 506, 507, 510, 547, 568, 583, 588, 678, 708, 709, 710, 763, 1103, 1107, 1154, 1156, 1200, 1205, 1218, 1223, 1241, 1252, 1261, 1273, 1279, 1289, 1290, 1329, 1332, 1334, 1388, 1405, 1412, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434, 1435, 1436], "11": [9, 26, 34, 45, 65, 66, 67, 69, 90, 103, 111, 116, 158, 211, 240, 241, 298, 299, 304, 307, 308, 324, 348, 394, 401, 407, 408, 409, 415, 417, 419, 424, 503, 516, 519, 608, 620, 682, 723, 740, 858, 903, 939, 985, 1055, 1056, 1057, 1103, 1156, 1293, 1412, 1419, 1422, 1423, 1428, 1433, 1434, 1435, 1436], "13": [9, 11, 39, 45, 60, 65, 67, 90, 92, 157, 230, 231, 232, 345, 348, 503, 705, 857, 902, 938, 984, 1156, 1198, 1415, 1429, 1436], "16": [9, 20, 32, 45, 46, 65, 67, 71, 230, 231, 232, 348, 349, 389, 391, 392, 396, 455, 510, 513, 514, 521, 573, 594, 608, 750, 751, 752, 1112, 1211, 1262, 1277, 1292, 1329, 1415, 1420, 1436], "17": [9, 22, 45, 65, 67, 104, 230, 231, 232, 298, 348, 510, 682, 695, 1414, 1415, 1436], "friend": [9, 547, 1416, 1421], "member": [9, 93, 94, 95, 101, 113, 316, 318, 319, 320, 332, 393, 485, 486, 588, 693, 1228, 1273, 1412], "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, 133], "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, 95, 1171, 1202, 1203, 1204], "50": [9, 26, 31, 35, 41, 51, 55, 56, 57, 58, 65, 66, 273, 313, 1120, 1199, 1203, 1204, 1257, 1303, 1308], "45": [9, 59, 65, 111, 227, 301, 411, 1181], "57": [9, 65], "46": [9, 65, 236, 566, 621, 1270], "24": [9, 20, 38, 65, 67, 69, 104, 348, 385, 386, 498, 507, 510, 705, 1218, 1235, 1250, 1268, 1277, 1412], "32": [9, 65, 67, 69, 210, 212, 213, 348, 385, 386, 566, 705, 1412, 1420], "36": [9, 22, 65, 69, 348, 754, 1156, 1268, 1277, 1359, 1360, 1385, 1412], "31": [9, 65, 67, 230, 231, 232, 261, 262, 263, 290, 348, 385, 386, 411, 705, 1232, 1241, 1412], "40": [9, 51, 65, 81, 102, 298, 301, 557, 674, 1179, 1246, 1277], "38": [9, 65, 690, 1277], "33": [9, 59, 65, 67, 69, 94, 348, 385, 386, 502, 516, 705, 1273, 1277, 1412, 1423], "37": [9, 57, 65, 69, 304, 312, 313, 324, 325, 326, 498, 510, 1042, 1043, 1277, 1402, 1412, 1417], "43": [9, 65, 325, 326, 608, 1250, 1277], "34": [9, 65, 69, 333, 510, 764, 1277, 1412], "algorithm": [9, 13, 15, 16, 45, 53, 55, 89, 94, 95, 96, 97, 103, 104, 108, 110, 111, 112, 113, 115, 116, 118, 121, 122, 123, 126, 128, 129, 133, 134, 137, 142, 152, 211, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 227, 228, 229, 230, 231, 232, 233, 236, 250, 252, 253, 254, 255, 256, 257, 259, 261, 262, 263, 264, 265, 266, 267, 268, 273, 276, 278, 279, 281, 283, 285, 286, 287, 288, 289, 290, 291, 294, 297, 298, 299, 300, 302, 303, 304, 307, 308, 309, 310, 312, 313, 316, 321, 323, 324, 325, 326, 327, 328, 331, 332, 333, 334, 335, 339, 341, 342, 343, 344, 345, 347, 348, 349, 354, 360, 363, 364, 368, 373, 374, 375, 376, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 391, 392, 396, 401, 407, 408, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 423, 424, 426, 427, 428, 429, 430, 431, 432, 434, 435, 437, 439, 442, 451, 453, 454, 455, 456, 457, 462, 466, 468, 470, 483, 484, 485, 490, 496, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 514, 515, 516, 518, 521, 522, 523, 529, 539, 548, 549, 550, 554, 555, 556, 557, 558, 559, 560, 566, 568, 571, 579, 583, 584, 585, 591, 593, 594, 595, 602, 616, 618, 620, 621, 626, 627, 628, 629, 630, 631, 632, 634, 635, 637, 638, 641, 654, 655, 659, 660, 661, 662, 665, 666, 669, 673, 674, 675, 676, 678, 679, 680, 682, 683, 684, 685, 688, 692, 693, 694, 695, 697, 698, 699, 700, 701, 702, 703, 704, 713, 719, 723, 724, 731, 733, 734, 736, 737, 738, 739, 740, 751, 766, 767, 770, 772, 777, 778, 782, 788, 791, 792, 793, 855, 900, 936, 982, 1014, 1041, 1045, 1046, 1108, 1109, 1110, 1112, 1117, 1119, 1120, 1131, 1132, 1161, 1171, 1174, 1175, 1183, 1184, 1185, 1186, 1187, 1191, 1192, 1193, 1194, 1199, 1201, 1206, 1207, 1208, 1211, 1213, 1215, 1216, 1222, 1229, 1230, 1232, 1233, 1234, 1236, 1237, 1238, 1240, 1241, 1245, 1266, 1275, 1281, 1282, 1283, 1304, 1308, 1325, 1326, 1327, 1329, 1331, 1334, 1373, 1374, 1394, 1402, 1403, 1404, 1409, 1410, 1411, 1412, 1415, 1416, 1417, 1419, 1420, 1421, 1422, 1423, 1425, 1426, 1428, 1431, 1433, 1434, 1436], "davis_southern_women_graph": [9, 89, 264], "top": [9, 35, 53, 68, 107, 112, 113, 116, 126, 261, 273, 285, 352, 383, 672, 677, 772, 1109, 1140, 1142, 1258, 1405, 1408, 1416, 1421, 1422, 1425], "bottom": [9, 92, 116, 261, 273, 275, 285, 286, 287, 288, 289, 352, 383, 1140, 1142, 1161, 1413, 1425], "biadjacency_matrix": [9, 284], "onto": [9, 285, 286, 287, 288, 289, 561, 562, 1129], "projected_graph": [9, 116, 285, 286, 287, 289, 353], "keep": [9, 93, 94, 95, 116, 205, 347, 348, 349, 364, 379, 389, 391, 392, 396, 585, 600, 695, 696, 893, 975, 1120, 1213, 1216, 1284, 1285, 1302, 1382, 1403, 1420, 1423], "co": [9, 27, 95, 100, 145, 754, 1332], "occur": [9, 94, 96, 101, 231, 232, 278, 279, 281, 385, 583, 584, 585, 590, 1046, 1120, 1123, 1132, 1288, 1302], "count": [9, 186, 238, 239, 243, 244, 246, 298, 299, 311, 316, 332, 362, 388, 445, 570, 599, 621, 751, 755, 876, 919, 946, 952, 958, 961, 1002, 1063, 1185, 1284, 1285, 1415, 1416, 1425], "share": [9, 55, 59, 93, 95, 113, 166, 200, 215, 216, 217, 222, 279, 286, 288, 289, 295, 360, 361, 378, 420, 421, 462, 464, 482, 571, 580, 693, 734, 864, 889, 909, 927, 945, 971, 991, 1010, 1223, 1334], "contact": [9, 93, 690, 1201, 1332], "weighted_projected_graph": [9, 285, 286, 287, 288, 1426], "648": 9, "plot_davis_club": [9, 18], "retain": [10, 103, 111, 231, 285, 286, 287, 288, 289, 1103, 1193, 1301], "pattern": [10, 55, 94, 104, 237, 242, 245, 249, 387, 496, 521, 557, 673, 674, 675, 676, 692, 693, 695, 764, 788, 1039, 1091, 1396, 1422], "add": [10, 11, 27, 35, 42, 46, 50, 53, 62, 72, 89, 90, 92, 94, 95, 102, 103, 106, 107, 116, 152, 153, 154, 155, 157, 158, 159, 165, 208, 223, 224, 230, 283, 286, 343, 376, 413, 414, 425, 430, 432, 433, 452, 462, 583, 584, 585, 591, 616, 617, 620, 621, 656, 692, 703, 719, 720, 798, 852, 855, 856, 857, 858, 859, 894, 897, 900, 901, 902, 903, 904, 930, 933, 936, 937, 938, 939, 940, 976, 979, 982, 983, 984, 985, 986, 987, 1013, 1040, 1041, 1042, 1043, 1045, 1052, 1055, 1056, 1057, 1103, 1127, 1129, 1160, 1171, 1178, 1191, 1213, 1216, 1223, 1225, 1239, 1240, 1242, 1308, 1332, 1359, 1360, 1362, 1363, 1385, 1386, 1391, 1402, 1403, 1404, 1407, 1413, 1415, 1416, 1417, 1418, 1420, 1421, 1422, 1423, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434, 1436], "compressor": [10, 692, 788], "do": [10, 56, 76, 89, 93, 94, 95, 97, 100, 102, 103, 106, 107, 108, 110, 112, 116, 134, 166, 185, 200, 203, 205, 231, 232, 239, 244, 278, 279, 281, 364, 382, 412, 413, 414, 420, 421, 460, 461, 469, 472, 591, 600, 634, 692, 694, 736, 737, 738, 739, 793, 798, 864, 875, 889, 892, 893, 909, 918, 927, 928, 929, 945, 956, 957, 971, 974, 975, 991, 1000, 1001, 1010, 1011, 1012, 1040, 1041, 1042, 1043, 1045, 1064, 1085, 1105, 1171, 1183, 1195, 1199, 1213, 1216, 1222, 1223, 1233, 1278, 1334, 1402, 1410, 1411, 1416, 1420, 1436], "would": [10, 93, 94, 96, 97, 101, 102, 103, 104, 105, 106, 108, 290, 306, 416, 417, 418, 419, 424, 430, 581, 585, 590, 634, 681, 692, 695, 719, 720, 753, 1223, 1242, 1301, 1302, 1306, 1309, 1332, 1425, 1426], "result": [10, 11, 26, 72, 93, 96, 102, 104, 110, 111, 113, 143, 166, 210, 219, 221, 231, 232, 256, 270, 272, 274, 277, 284, 285, 286, 287, 288, 289, 290, 300, 301, 306, 325, 326, 332, 346, 356, 376, 382, 383, 384, 387, 388, 393, 413, 414, 418, 420, 442, 466, 468, 469, 492, 496, 500, 501, 511, 512, 513, 514, 566, 567, 568, 586, 587, 589, 603, 611, 617, 628, 629, 631, 678, 680, 692, 694, 706, 712, 719, 788, 793, 864, 909, 945, 987, 991, 1041, 1045, 1085, 1097, 1101, 1102, 1105, 1106, 1108, 1115, 1116, 1117, 1119, 1127, 1137, 1138, 1144, 1145, 1146, 1147, 1148, 1156, 1158, 1160, 1163, 1165, 1166, 1169, 1181, 1183, 1186, 1207, 1228, 1231, 1245, 1284, 1285, 1287, 1302, 1305, 1309, 1314, 1332, 1334, 1337, 1340, 1365, 1411, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1425, 1426, 1434, 1435, 1436], "fewer": [10, 422, 423, 683, 685, 692, 694, 695, 696, 764, 788, 1219, 1221], "compress": [10, 26, 269, 514, 579, 692, 788, 1119, 1248, 1339, 1340, 1345, 1346, 1350, 1356, 1363, 1364, 1377, 1378, 1382], "suptitl": [10, 16], "original_graph": [10, 16, 692], "white_nod": 10, "red_nod": 10, "250": [10, 33, 1171], "white": [10, 22, 26, 83, 84, 128, 215, 216, 217, 221, 429, 1404, 1407, 1415], "add_nodes_from": [10, 16, 17, 37, 71, 72, 83, 90, 116, 157, 166, 200, 208, 237, 238, 249, 266, 268, 269, 425, 427, 428, 471, 557, 692, 798, 857, 864, 889, 894, 902, 909, 927, 930, 938, 945, 971, 976, 984, 991, 1010, 1013, 1040, 1042, 1043, 1068, 1200, 1223, 1297, 1413, 1415, 1422, 1436], "add_edges_from": [10, 16, 17, 37, 42, 71, 83, 90, 116, 133, 152, 159, 166, 200, 205, 208, 237, 249, 288, 329, 378, 424, 425, 427, 428, 462, 471, 503, 513, 514, 574, 576, 590, 690, 692, 707, 708, 709, 711, 732, 744, 745, 798, 855, 859, 864, 889, 893, 894, 900, 904, 909, 927, 929, 930, 936, 940, 945, 958, 965, 966, 971, 975, 976, 982, 986, 991, 1002, 1005, 1006, 1010, 1012, 1013, 1040, 1042, 1043, 1073, 1088, 1097, 1141, 1160, 1223, 1293, 1297, 1332, 1413, 1416, 1436], "base_opt": [10, 16], "edgecolor": [10, 16, 22, 33, 35, 36, 39, 55, 59, 83, 84, 1143], "black": [10, 16, 22, 26, 66, 70, 94, 600, 1139, 1140, 1142, 1421, 1422, 1423, 1425, 1436], "ax1": [10, 16, 28, 51, 83], "number_of_edg": [10, 16, 26, 29, 199, 692, 888, 926, 970, 1009, 1062, 1160, 1277, 1415, 1416, 1436], "nonexp_graph": 10, "compression_nod": 10, "summar": [10, 16, 101, 102, 692, 693, 760, 793, 1331, 1334, 1387, 1422], "dedensifi": [10, 760], "threshold": [10, 58, 84, 113, 221, 230, 232, 382, 383, 692, 694, 697, 698, 760, 788, 1120, 1199, 1200, 1202, 1203, 1204, 1331, 1407, 1415, 1416, 1417, 1421, 1423], "copi": [10, 17, 39, 45, 94, 96, 107, 168, 197, 200, 203, 204, 205, 206, 285, 286, 287, 288, 289, 343, 390, 392, 394, 408, 435, 436, 437, 438, 439, 455, 462, 471, 523, 586, 587, 589, 598, 601, 604, 605, 607, 608, 609, 612, 613, 615, 616, 635, 638, 692, 866, 887, 889, 892, 893, 911, 927, 928, 929, 947, 966, 969, 971, 974, 975, 993, 1006, 1010, 1011, 1012, 1038, 1041, 1060, 1064, 1066, 1069, 1085, 1086, 1125, 1189, 1195, 1223, 1229, 1233, 1257, 1276, 1300, 1301, 1302, 1412, 1413, 1415, 1416, 1417, 1418, 1421, 1422, 1431, 1434], "nonexp_node_color": 10, "nonexp_node_s": 10, "yellow": [10, 16, 600, 762, 1436], "nonexp_po": 10, "75": [10, 35, 240, 261, 300, 315, 357, 358, 388, 684, 1175, 1176, 1177, 1179, 1413, 1417, 1436], "c_node": [10, 692], "spot": 10, "274": [10, 18], "plot_dedensif": [10, 18], "153": [11, 457], "curiou": 11, "let": [11, 56, 59, 94, 98, 102, 104, 218, 258, 281, 283, 300, 301, 314, 323, 373, 374, 385, 588, 621, 764, 1045, 1225, 1284, 1285, 1332, 1434], "defin": [11, 25, 53, 59, 70, 98, 113, 128, 214, 223, 224, 240, 241, 261, 262, 263, 264, 286, 290, 312, 317, 331, 336, 337, 347, 348, 349, 358, 387, 388, 392, 426, 427, 428, 431, 434, 435, 436, 437, 438, 439, 451, 466, 467, 468, 471, 496, 497, 500, 501, 502, 504, 505, 508, 509, 511, 512, 521, 569, 571, 572, 573, 575, 576, 577, 579, 588, 616, 617, 621, 623, 627, 654, 673, 675, 676, 678, 686, 687, 688, 689, 690, 691, 730, 732, 740, 753, 754, 755, 764, 793, 798, 1040, 1041, 1042, 1043, 1048, 1050, 1074, 1084, 1101, 1127, 1128, 1129, 1153, 1160, 1176, 1178, 1201, 1203, 1286, 1292, 1293, 1294, 1302, 1326, 1327, 1332, 1350, 1359, 1360, 1365, 1369, 1385, 1404, 1411, 1416, 1417, 1421, 1436], "an": [11, 13, 16, 25, 26, 32, 35, 39, 42, 45, 47, 50, 53, 55, 56, 59, 64, 67, 68, 72, 76, 77, 78, 89, 92, 93, 94, 95, 96, 97, 100, 101, 102, 103, 104, 105, 108, 111, 113, 115, 116, 117, 121, 122, 128, 129, 133, 142, 152, 153, 158, 159, 161, 166, 167, 168, 169, 171, 176, 180, 181, 182, 185, 189, 190, 192, 193, 194, 195, 196, 199, 200, 202, 205, 207, 208, 209, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 227, 228, 230, 231, 232, 233, 236, 239, 240, 241, 244, 250, 251, 252, 256, 257, 265, 267, 268, 270, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 292, 293, 294, 295, 296, 298, 299, 300, 302, 303, 307, 308, 309, 310, 312, 313, 316, 317, 319, 320, 321, 323, 325, 326, 327, 328, 331, 332, 334, 343, 344, 345, 347, 348, 349, 350, 351, 352, 353, 355, 358, 359, 364, 365, 366, 367, 368, 372, 375, 376, 377, 379, 380, 381, 382, 383, 385, 386, 387, 389, 390, 391, 392, 394, 396, 397, 402, 404, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 429, 430, 431, 433, 434, 435, 439, 440, 441, 442, 451, 452, 453, 457, 458, 459, 462, 464, 468, 469, 470, 471, 473, 474, 475, 476, 477, 479, 482, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 518, 519, 521, 522, 523, 524, 525, 526, 527, 532, 536, 537, 542, 546, 547, 557, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 579, 580, 581, 582, 586, 588, 590, 591, 592, 595, 596, 597, 598, 599, 600, 603, 606, 607, 609, 612, 613, 617, 618, 620, 621, 626, 628, 629, 633, 634, 635, 637, 638, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 680, 681, 682, 683, 684, 685, 686, 688, 692, 693, 694, 696, 697, 698, 699, 703, 705, 706, 707, 708, 709, 710, 718, 719, 721, 723, 724, 725, 726, 727, 728, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 745, 750, 754, 762, 763, 764, 769, 777, 784, 793, 798, 803, 808, 812, 816, 820, 824, 829, 834, 839, 844, 849, 851, 852, 853, 855, 856, 858, 859, 861, 864, 865, 866, 867, 868, 871, 873, 874, 875, 879, 880, 882, 883, 884, 885, 886, 888, 889, 891, 893, 894, 896, 897, 898, 900, 901, 903, 904, 906, 909, 910, 911, 912, 913, 916, 917, 918, 922, 923, 924, 925, 926, 927, 929, 930, 932, 933, 934, 936, 937, 939, 940, 942, 945, 946, 947, 948, 949, 950, 952, 954, 955, 956, 957, 961, 962, 964, 965, 966, 967, 968, 970, 971, 973, 975, 976, 978, 979, 980, 982, 983, 985, 986, 988, 991, 992, 993, 994, 995, 996, 998, 999, 1000, 1001, 1005, 1006, 1007, 1008, 1009, 1010, 1012, 1013, 1015, 1016, 1021, 1023, 1039, 1040, 1041, 1042, 1043, 1045, 1046, 1048, 1049, 1052, 1053, 1054, 1064, 1065, 1069, 1071, 1077, 1078, 1084, 1085, 1087, 1088, 1089, 1090, 1091, 1093, 1097, 1101, 1102, 1103, 1104, 1105, 1106, 1108, 1118, 1120, 1125, 1127, 1128, 1129, 1139, 1141, 1143, 1149, 1150, 1152, 1155, 1156, 1157, 1158, 1160, 1161, 1163, 1165, 1166, 1169, 1172, 1173, 1181, 1183, 1184, 1185, 1187, 1188, 1191, 1192, 1193, 1194, 1198, 1199, 1200, 1201, 1202, 1203, 1204, 1205, 1208, 1211, 1212, 1213, 1214, 1215, 1216, 1217, 1218, 1219, 1222, 1223, 1224, 1228, 1230, 1231, 1233, 1234, 1235, 1236, 1238, 1240, 1241, 1242, 1245, 1248, 1250, 1256, 1265, 1268, 1269, 1273, 1275, 1276, 1277, 1278, 1279, 1281, 1282, 1283, 1284, 1285, 1287, 1288, 1293, 1294, 1297, 1300, 1301, 1302, 1306, 1308, 1309, 1325, 1326, 1327, 1329, 1330, 1332, 1334, 1335, 1337, 1339, 1340, 1342, 1347, 1350, 1358, 1368, 1369, 1371, 1377, 1383, 1384, 1385, 1386, 1387, 1388, 1389, 1391, 1395, 1402, 1403, 1404, 1406, 1407, 1408, 1411, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1421, 1422, 1423, 1425, 1426, 1433, 1434, 1436], "process": [11, 14, 53, 77, 93, 94, 95, 97, 98, 99, 103, 105, 181, 223, 227, 233, 275, 333, 340, 375, 385, 407, 408, 442, 457, 466, 467, 468, 594, 626, 693, 762, 788, 873, 916, 954, 998, 1048, 1103, 1127, 1128, 1129, 1181, 1183, 1186, 1222, 1225, 1228, 1231, 1251, 1286, 1296, 1301, 1302, 1305, 1307, 1391, 1404, 1416, 1417, 1421, 1422, 1423, 1428, 1436], "follow": [11, 26, 45, 50, 53, 54, 66, 68, 84, 87, 92, 93, 94, 95, 96, 98, 100, 101, 102, 103, 104, 109, 111, 112, 129, 133, 152, 162, 172, 184, 208, 214, 228, 230, 231, 232, 244, 281, 306, 340, 345, 348, 353, 364, 375, 380, 382, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 442, 454, 455, 467, 468, 498, 504, 505, 506, 507, 508, 509, 510, 590, 600, 601, 604, 617, 638, 681, 750, 752, 762, 764, 793, 855, 869, 894, 900, 914, 930, 936, 950, 976, 982, 996, 1013, 1105, 1106, 1108, 1150, 1171, 1181, 1185, 1191, 1194, 1206, 1207, 1215, 1225, 1231, 1239, 1240, 1247, 1257, 1266, 1280, 1281, 1282, 1283, 1287, 1302, 1321, 1329, 1332, 1334, 1335, 1387, 1396, 1402, 1404, 1408, 1413, 1415, 1416, 1418, 1420, 1421, 1422, 1434, 1436], "given": [11, 13, 39, 45, 63, 65, 68, 92, 100, 102, 104, 113, 117, 142, 143, 145, 153, 159, 194, 198, 209, 212, 213, 228, 230, 236, 237, 249, 250, 261, 265, 267, 270, 272, 274, 275, 277, 280, 282, 284, 285, 286, 287, 288, 289, 321, 331, 333, 340, 346, 348, 353, 355, 359, 364, 365, 366, 367, 375, 380, 382, 383, 387, 441, 456, 457, 462, 464, 472, 479, 480, 482, 499, 513, 514, 515, 561, 562, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 578, 580, 581, 582, 590, 591, 592, 616, 617, 618, 624, 625, 661, 662, 663, 664, 678, 679, 680, 681, 683, 685, 686, 688, 692, 693, 695, 699, 700, 701, 702, 704, 705, 706, 708, 709, 710, 711, 730, 731, 732, 733, 734, 741, 750, 755, 763, 784, 788, 856, 859, 884, 901, 904, 923, 937, 940, 966, 983, 986, 1006, 1049, 1088, 1089, 1097, 1104, 1105, 1141, 1150, 1157, 1168, 1181, 1182, 1183, 1184, 1185, 1186, 1187, 1195, 1205, 1206, 1207, 1212, 1213, 1214, 1215, 1216, 1227, 1228, 1246, 1275, 1279, 1280, 1282, 1301, 1306, 1308, 1321, 1329, 1359, 1360, 1385, 1386, 1387, 1388, 1403, 1404, 1415], "digit": [11, 71, 100], "base": [11, 16, 39, 44, 56, 59, 70, 94, 95, 101, 102, 103, 104, 108, 129, 133, 200, 204, 206, 213, 217, 221, 230, 297, 298, 302, 303, 304, 309, 310, 311, 312, 313, 323, 324, 325, 326, 327, 331, 332, 339, 345, 348, 349, 364, 373, 375, 376, 382, 383, 384, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 425, 427, 428, 429, 430, 432, 433, 451, 466, 468, 496, 500, 501, 502, 511, 512, 547, 557, 566, 568, 571, 576, 583, 616, 618, 662, 669, 682, 690, 693, 706, 708, 709, 710, 712, 713, 714, 715, 716, 717, 719, 734, 740, 760, 763, 764, 788, 793, 798, 889, 927, 936, 937, 971, 982, 983, 1010, 1039, 1040, 1041, 1044, 1046, 1085, 1091, 1188, 1235, 1241, 1259, 1273, 1302, 1326, 1327, 1329, 1332, 1391, 1395, 1399, 1401, 1404, 1411, 1412, 1413, 1415, 1416, 1417, 1418, 1420, 1421, 1430, 1434], "obtain": [11, 92, 166, 208, 283, 347, 348, 349, 382, 385, 389, 390, 391, 392, 396, 467, 513, 608, 620, 621, 658, 724, 744, 745, 762, 798, 864, 894, 909, 930, 945, 976, 991, 1013, 1040, 1042, 1043, 1170, 1259, 1278, 1284, 1285, 1329, 1332, 1362, 1363, 1411, 1436], "seri": [11, 446, 618, 682, 1221, 1292], "finit": [11, 464, 496, 497, 500, 501, 504, 505, 508, 509, 511, 512, 516, 520, 1183, 1185, 1198, 1228], "end": [11, 26, 37, 53, 96, 102, 107, 154, 155, 207, 216, 228, 268, 269, 301, 334, 335, 344, 373, 374, 429, 616, 620, 621, 628, 629, 633, 634, 636, 637, 638, 641, 642, 652, 653, 654, 655, 656, 657, 662, 666, 669, 679, 680, 682, 736, 738, 1041, 1045, 1064, 1069, 1078, 1083, 1085, 1087, 1120, 1127, 1139, 1141, 1158, 1171, 1212, 1235, 1332, 1339, 1340, 1343, 1344, 1345, 1346, 1348, 1350, 1356, 1359, 1363, 1364, 1374, 1377, 1378, 1381, 1382, 1385, 1388, 1413, 1422], "In": [11, 17, 28, 44, 55, 58, 59, 89, 93, 94, 95, 96, 98, 100, 101, 102, 104, 111, 116, 128, 133, 134, 176, 185, 200, 218, 230, 231, 232, 236, 241, 258, 259, 260, 279, 284, 287, 289, 290, 300, 312, 313, 325, 326, 331, 352, 359, 380, 381, 382, 412, 415, 416, 417, 424, 431, 445, 449, 452, 460, 462, 496, 500, 501, 503, 512, 567, 570, 574, 576, 592, 593, 617, 621, 623, 654, 655, 656, 659, 660, 665, 672, 677, 678, 692, 693, 703, 705, 719, 720, 721, 732, 734, 742, 743, 744, 745, 763, 764, 769, 772, 791, 793, 798, 871, 875, 889, 918, 927, 956, 957, 971, 1000, 1001, 1010, 1040, 1041, 1042, 1043, 1045, 1046, 1069, 1103, 1104, 1120, 1160, 1174, 1205, 1209, 1212, 1213, 1214, 1216, 1222, 1223, 1228, 1232, 1237, 1239, 1247, 1301, 1302, 1306, 1326, 1327, 1332, 1334, 1356, 1387, 1403, 1407, 1408, 1413, 1414, 1415, 1416, 1417, 1418, 1422, 1423, 1436], "languag": [11, 93, 100, 111, 1045, 1330, 1347, 1348, 1350, 1389, 1390, 1391, 1420], "discret": [11, 105, 236, 250, 364, 411, 515, 519, 520, 620, 762, 1170, 1171, 1184, 1186, 1192, 1196, 1210, 1284, 1285, 1288, 1320, 1321, 1329, 1415], "global": [11, 104, 315, 343, 412, 479, 488, 489, 511, 594, 1048, 1275, 1302, 1307, 1310, 1311, 1334, 1416, 1418, 1420], "attractor": [11, 390], "map": [11, 35, 39, 53, 68, 102, 103, 104, 116, 126, 145, 146, 149, 167, 170, 198, 239, 244, 265, 352, 371, 393, 414, 418, 419, 420, 421, 425, 426, 427, 428, 433, 442, 462, 532, 533, 536, 542, 543, 546, 547, 561, 562, 563, 565, 590, 616, 672, 678, 680, 753, 754, 762, 764, 865, 910, 946, 949, 992, 995, 1015, 1016, 1021, 1022, 1041, 1042, 1043, 1048, 1139, 1141, 1143, 1223, 1275, 1301, 1302, 1312, 1316, 1323, 1324, 1330, 1331, 1367, 1368, 1402, 1411, 1415, 1417, 1421, 1422, 1434, 1436], "restrict": [11, 103, 129, 355, 793, 1041, 1085, 1413], "For": [11, 55, 68, 89, 93, 94, 96, 98, 100, 102, 103, 104, 106, 108, 111, 116, 126, 129, 133, 144, 152, 159, 160, 161, 166, 169, 186, 190, 200, 201, 205, 227, 231, 232, 236, 239, 240, 241, 247, 248, 256, 260, 283, 298, 299, 300, 302, 303, 305, 307, 308, 309, 310, 312, 313, 315, 316, 317, 322, 323, 325, 326, 328, 330, 331, 332, 340, 348, 349, 358, 359, 360, 382, 387, 394, 397, 399, 400, 402, 404, 405, 406, 409, 412, 413, 414, 415, 416, 418, 419, 420, 421, 424, 431, 433, 434, 435, 436, 437, 438, 452, 455, 462, 481, 482, 490, 496, 497, 498, 500, 501, 504, 505, 508, 509, 511, 512, 524, 525, 526, 557, 567, 570, 574, 576, 587, 589, 600, 616, 617, 620, 621, 627, 635, 638, 643, 645, 661, 679, 680, 688, 689, 690, 693, 719, 720, 721, 735, 736, 737, 738, 739, 744, 745, 754, 755, 756, 764, 772, 777, 784, 788, 791, 793, 798, 855, 859, 860, 861, 864, 867, 876, 880, 889, 890, 893, 900, 904, 905, 906, 909, 912, 919, 927, 936, 940, 941, 942, 945, 948, 958, 962, 965, 971, 972, 982, 986, 987, 988, 991, 994, 1002, 1005, 1010, 1040, 1041, 1042, 1043, 1045, 1065, 1067, 1069, 1074, 1088, 1097, 1101, 1102, 1104, 1105, 1106, 1108, 1114, 1118, 1127, 1128, 1129, 1137, 1138, 1139, 1141, 1144, 1145, 1146, 1147, 1148, 1149, 1154, 1157, 1160, 1181, 1183, 1185, 1186, 1191, 1194, 1195, 1199, 1201, 1202, 1203, 1204, 1205, 1219, 1220, 1223, 1225, 1230, 1234, 1238, 1248, 1278, 1281, 1282, 1283, 1284, 1285, 1287, 1288, 1291, 1292, 1295, 1297, 1299, 1302, 1304, 1332, 1334, 1339, 1351, 1354, 1355, 1356, 1362, 1363, 1364, 1377, 1387, 1390, 1398, 1402, 1404, 1409, 1410, 1411, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434, 1435, 1436], "108": [11, 1222], "513": [11, 1407, 1415], "reach": [11, 100, 101, 315, 325, 329, 378, 385, 389, 391, 392, 396, 412, 413, 414, 420, 421, 496, 500, 501, 512, 566, 568, 628, 629, 634, 642, 645, 654, 695, 713, 760, 1194, 1213, 1216, 1387, 1388, 1416], "orbit": 11, "up": [11, 71, 81, 94, 95, 98, 100, 101, 102, 105, 106, 108, 133, 134, 348, 349, 379, 425, 429, 511, 532, 542, 579, 621, 654, 655, 659, 750, 1039, 1041, 1064, 1069, 1085, 1091, 1105, 1127, 1129, 1150, 1154, 1179, 1219, 1221, 1278, 1332, 1334, 1361, 1364, 1404, 1405, 1411, 1413, 1415, 1419, 1420, 1422, 1423, 1425, 1426, 1429, 1434, 1436], "reveal": [11, 713, 788], "cycl": [11, 39, 45, 96, 121, 215, 228, 229, 230, 231, 232, 233, 264, 294, 295, 296, 340, 343, 345, 360, 451, 452, 453, 454, 455, 459, 464, 465, 466, 468, 469, 470, 482, 498, 503, 506, 507, 510, 521, 586, 587, 589, 610, 630, 631, 632, 634, 654, 659, 660, 665, 699, 729, 744, 745, 760, 793, 1046, 1055, 1141, 1143, 1154, 1155, 1158, 1169, 1192, 1196, 1248, 1250, 1266, 1270, 1331, 1404, 1406, 1407, 1410, 1412, 1413, 1415, 1416, 1417, 1420, 1421, 1423, 1433], "requir": [11, 39, 66, 94, 95, 96, 100, 101, 102, 103, 105, 107, 108, 110, 112, 116, 166, 208, 292, 293, 294, 297, 302, 303, 309, 310, 317, 439, 478, 502, 522, 523, 617, 682, 700, 701, 702, 722, 731, 733, 788, 793, 798, 864, 894, 909, 930, 945, 976, 991, 1013, 1040, 1042, 1043, 1049, 1114, 1149, 1198, 1199, 1205, 1221, 1223, 1241, 1302, 1332, 1351, 1354, 1355, 1356, 1390, 1402, 1403, 1405, 1411, 1414, 1415, 1416, 1417, 1420, 1421, 1422, 1423, 1428, 1429, 1434, 1436], "less": [11, 35, 44, 100, 102, 129, 143, 145, 228, 290, 325, 326, 382, 383, 385, 386, 387, 424, 427, 428, 431, 466, 522, 523, 638, 675, 688, 733, 788, 1141, 1168, 1180, 1191, 1193, 1200, 1281, 1282, 1359, 1360, 1385, 1413, 1414, 1417, 1420, 1422, 1423], "smallest": [11, 32, 212, 222, 265, 364, 372, 378, 383, 442, 485, 492, 681, 731, 733, 1051, 1206, 1255, 1265, 1281, 1282, 1308, 1326, 1327, 1416], "177": [11, 298, 299, 307, 308, 331], "e": [11, 16, 17, 32, 35, 39, 47, 53, 62, 66, 68, 70, 72, 77, 83, 90, 92, 93, 94, 95, 96, 98, 100, 102, 103, 104, 105, 108, 111, 112, 113, 116, 128, 142, 145, 152, 153, 158, 159, 169, 171, 172, 178, 190, 193, 196, 208, 212, 218, 219, 222, 227, 234, 237, 242, 245, 249, 250, 268, 276, 279, 281, 283, 285, 289, 290, 291, 294, 296, 301, 302, 303, 306, 307, 308, 309, 310, 312, 313, 314, 323, 325, 326, 327, 328, 333, 334, 335, 341, 342, 343, 345, 347, 357, 358, 360, 363, 373, 374, 376, 380, 385, 387, 400, 407, 408, 431, 436, 451, 454, 455, 457, 469, 470, 471, 473, 474, 476, 477, 478, 481, 490, 492, 493, 494, 496, 498, 500, 501, 504, 505, 506, 507, 508, 509, 510, 511, 512, 519, 520, 567, 568, 577, 579, 584, 588, 590, 592, 595, 600, 604, 617, 618, 620, 621, 627, 628, 677, 679, 680, 688, 690, 693, 694, 695, 734, 736, 738, 764, 798, 852, 855, 856, 858, 859, 867, 868, 869, 880, 883, 886, 894, 897, 900, 901, 903, 904, 912, 913, 914, 922, 925, 930, 933, 936, 937, 939, 940, 948, 949, 950, 962, 965, 968, 976, 979, 982, 983, 985, 986, 987, 994, 995, 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1355, 1356, 1377, 1378, 1381, 1382, 1402, 1404, 1405, 1407, 1410, 1414, 1415, 1416, 1417, 1420, 1421, 1422, 1423, 1425, 1426, 1427, 1428, 1429, 1430, 1432, 1433, 1434, 1436], "softwar": [11, 92, 108, 112, 483, 484, 731, 733, 1436], "power": [11, 46, 95, 111, 208, 312, 313, 325, 373, 374, 522, 523, 566, 568, 694, 760, 894, 930, 976, 1013, 1046, 1171, 1181, 1243, 1244, 1261, 1322, 1325, 1404, 1415, 1416, 1436], "abov": [11, 93, 94, 101, 102, 103, 104, 111, 292, 293, 316, 317, 326, 332, 382, 385, 388, 455, 462, 493, 496, 500, 501, 504, 505, 511, 512, 523, 688, 694, 732, 764, 1041, 1105, 1127, 1128, 1129, 1154, 1171, 1191, 1225, 1240, 1280, 1284, 1285, 1306, 1408, 1413, 1416, 1426], "correspond": [11, 68, 102, 104, 145, 162, 168, 223, 224, 228, 229, 230, 231, 232, 233, 234, 235, 266, 267, 282, 312, 313, 325, 326, 333, 334, 352, 363, 364, 382, 393, 417, 419, 420, 421, 424, 462, 478, 484, 513, 516, 583, 585, 590, 611, 617, 618, 626, 630, 631, 632, 679, 680, 681, 730, 731, 733, 734, 744, 745, 750, 793, 852, 866, 897, 911, 933, 947, 979, 993, 1101, 1102, 1104, 1105, 1106, 1108, 1112, 1118, 1141, 1149, 1150, 1181, 1183, 1184, 1185, 1186, 1187, 1199, 1200, 1218, 1228, 1277, 1278, 1280, 1282, 1283, 1284, 1285, 1287, 1329, 1338, 1339, 1341, 1342, 1361, 1364, 1365, 1366, 1369, 1370, 1376, 1387, 1403, 1414, 1415], "below": [11, 14, 26, 93, 95, 100, 101, 112, 152, 207, 332, 385, 410, 412, 413, 414, 415, 416, 417, 419, 421, 431, 466, 493, 494, 496, 498, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 567, 617, 694, 798, 855, 900, 936, 982, 1040, 1042, 1043, 1120, 1150, 1181, 1183, 1223, 1228, 1248, 1281, 1282, 1283, 1302, 1355, 1402, 1411, 1413, 1426, 1436], "powersum": 11, "over": [11, 35, 39, 50, 72, 89, 95, 96, 100, 102, 103, 104, 110, 153, 158, 159, 160, 161, 169, 176, 177, 181, 182, 185, 189, 190, 191, 192, 196, 201, 202, 214, 215, 221, 231, 236, 292, 296, 300, 315, 316, 317, 321, 327, 331, 332, 347, 348, 349, 364, 365, 366, 367, 371, 375, 376, 387, 410, 411, 431, 479, 490, 491, 498, 499, 525, 528, 531, 535, 538, 541, 545, 600, 638, 680, 692, 705, 706, 707, 708, 709, 710, 712, 713, 721, 735, 736, 738, 740, 764, 851, 853, 856, 858, 859, 860, 861, 867, 871, 872, 873, 874, 875, 879, 880, 881, 882, 886, 890, 891, 896, 898, 901, 903, 904, 905, 906, 912, 916, 917, 918, 925, 932, 934, 937, 939, 940, 941, 942, 948, 953, 954, 955, 957, 962, 963, 964, 968, 972, 973, 978, 980, 983, 985, 986, 987, 988, 994, 998, 999, 1001, 1008, 1077, 1078, 1087, 1103, 1198, 1223, 1231, 1239, 1247, 1284, 1285, 1294, 1332, 1334, 1402, 1411, 1413, 1414, 1416, 1418, 1419, 1420, 1421, 1422, 1423, 1425, 1434, 1435, 1436], "converg": [11, 312, 325, 375, 566, 567, 568, 678, 1046, 1416, 1417], "singl": [11, 14, 59, 81, 94, 95, 100, 102, 103, 105, 108, 144, 152, 153, 157, 159, 167, 169, 176, 177, 181, 189, 190, 194, 221, 266, 275, 291, 294, 295, 300, 316, 323, 329, 333, 346, 355, 356, 393, 395, 426, 429, 445, 464, 466, 493, 496, 500, 501, 504, 505, 511, 512, 579, 586, 587, 589, 600, 623, 637, 662, 663, 664, 679, 680, 692, 707, 744, 745, 788, 793, 798, 855, 856, 857, 859, 865, 867, 871, 872, 873, 879, 880, 884, 900, 901, 902, 904, 910, 912, 916, 923, 936, 937, 938, 940, 946, 948, 952, 953, 954, 961, 962, 965, 966, 982, 983, 984, 986, 992, 994, 998, 1005, 1006, 1040, 1042, 1043, 1044, 1045, 1048, 1049, 1061, 1088, 1089, 1094, 1095, 1096, 1100, 1101, 1102, 1104, 1105, 1107, 1123, 1127, 1129, 1132, 1139, 1141, 1143, 1146, 1153, 1157, 1162, 1170, 1173, 1178, 1195, 1203, 1278, 1280, 1301, 1302, 1324, 1326, 1327, 1329, 1330, 1334, 1337, 1340, 1341, 1351, 1369, 1370, 1375, 1410, 1413, 1415, 1416, 1418, 1421, 1422], "fix": [11, 92, 94, 95, 96, 101, 107, 514, 695, 696, 711, 1120, 1275, 1403, 1405, 1409, 1411, 1412, 1416, 1417, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434], "appli": [11, 36, 53, 89, 93, 100, 221, 231, 232, 300, 323, 341, 342, 344, 360, 464, 511, 588, 590, 620, 627, 649, 762, 788, 793, 1039, 1045, 1088, 1089, 1091, 1097, 1141, 1143, 1170, 1194, 1203, 1248, 1275, 1288, 1302, 1329, 1362, 1363, 1403, 1413, 1416, 1434], "lead": [11, 100, 102, 231, 232, 385, 473, 474, 475, 476, 477, 569, 1181, 1183, 1228, 1332, 1414, 1436], "370": [11, 1250], "371": [11, 275], "407": [11, 348, 349], "modulo": [11, 588, 1196], "ad": [11, 17, 28, 42, 72, 89, 95, 96, 97, 98, 100, 101, 102, 103, 104, 106, 128, 142, 152, 153, 154, 155, 156, 158, 159, 207, 208, 228, 235, 275, 323, 333, 424, 536, 546, 581, 585, 603, 665, 692, 788, 793, 855, 856, 858, 859, 894, 900, 901, 903, 904, 930, 936, 937, 939, 940, 965, 976, 982, 983, 985, 986, 1005, 1013, 1055, 1056, 1066, 1101, 1103, 1104, 1127, 1128, 1129, 1188, 1189, 1190, 1192, 1235, 1239, 1240, 1242, 1278, 1284, 1285, 1330, 1332, 1335, 1404, 1405, 1407, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1419, 1420, 1421, 1422, 1423, 1425, 1426, 1428, 1433, 1434], "anoth": [11, 44, 58, 93, 95, 102, 104, 105, 108, 113, 316, 332, 600, 617, 695, 696, 706, 719, 742, 743, 744, 745, 764, 793, 798, 1040, 1042, 1043, 1088, 1181, 1219, 1221, 1225, 1334, 1413, 1420, 1428, 1436], "invari": [11, 608, 620, 621, 777, 1196], "subset": [11, 72, 102, 112, 113, 211, 212, 298, 299, 303, 308, 310, 424, 459, 485, 486, 567, 568, 583, 584, 585, 626, 688, 689, 764, 788, 793, 1112, 1157, 1168, 1301, 1404, 1407, 1415, 1420, 1422, 1436], "squar": [11, 15, 71, 327, 360, 1045, 1114, 1179, 1198, 1201, 1221, 1258, 1259, 1277, 1329], "certain": [11, 455, 616, 621, 680, 721, 1240, 1284, 1285], "itself": [11, 95, 100, 101, 102, 104, 301, 320, 348, 349, 350, 351, 355, 363, 364, 458, 463, 1049, 1127, 1128, 1129, 1170, 1223, 1332, 1387, 1388, 1418, 1436], "keyword": [11, 33, 95, 96, 104, 152, 153, 157, 158, 159, 185, 199, 208, 227, 291, 300, 321, 329, 376, 385, 504, 505, 508, 509, 617, 680, 741, 754, 798, 852, 855, 856, 857, 858, 859, 875, 888, 894, 897, 900, 901, 902, 903, 904, 918, 926, 930, 933, 936, 937, 938, 939, 940, 957, 970, 976, 979, 982, 983, 984, 985, 986, 1001, 1009, 1013, 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1332, 1338, 1339, 1342, 1343, 1344, 1348, 1351, 1354, 1355, 1356, 1362, 1363, 1364, 1376, 1377, 1390, 1394, 1398, 1402, 1404, 1413, 1415, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1426, 1434, 1436], "other": [16, 17, 25, 42, 44, 51, 53, 57, 58, 59, 84, 89, 92, 93, 94, 95, 98, 100, 101, 102, 103, 104, 105, 106, 108, 110, 111, 116, 133, 135, 166, 209, 215, 216, 217, 227, 231, 232, 233, 236, 257, 259, 265, 268, 269, 283, 289, 290, 295, 298, 299, 306, 317, 321, 323, 325, 326, 329, 354, 360, 368, 375, 398, 399, 430, 454, 455, 462, 464, 475, 493, 504, 505, 508, 509, 529, 539, 561, 562, 567, 590, 604, 634, 635, 637, 638, 643, 655, 662, 663, 664, 667, 668, 669, 670, 671, 677, 678, 690, 693, 703, 725, 726, 727, 728, 736, 737, 738, 739, 753, 754, 764, 791, 793, 798, 864, 909, 945, 950, 991, 996, 1040, 1041, 1042, 1043, 1045, 1057, 1105, 1106, 1117, 1119, 1129, 1139, 1151, 1153, 1157, 1160, 1171, 1180, 1186, 1192, 1200, 1201, 1203, 1204, 1228, 1235, 1275, 1284, 1285, 1287, 1292, 1295, 1297, 1299, 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1177, 1199, 1204, 1213, 1261, 1275, 1293, 1302, 1332, 1371, 1372, 1390, 1403, 1413, 1414, 1415, 1422, 1423, 1434, 1436], "relat": [16, 35, 68, 93, 94, 96, 100, 101, 116, 130, 133, 221, 231, 298, 368, 372, 588, 590, 621, 690, 764, 769, 797, 1208, 1211, 1275, 1329, 1404, 1411, 1415, 1422, 1425, 1434], "strong": [16, 399, 513, 514, 519, 612, 621, 693, 701, 760, 1417], "weak": [16, 400, 693, 760, 1434], "number_of_nod": [16, 26, 81, 157, 188, 312, 325, 339, 385, 566, 583, 854, 857, 878, 899, 902, 921, 935, 938, 960, 981, 984, 1004, 1160, 1277, 1436], "7482934": 16, "_": [16, 17, 27, 39, 94, 301, 335, 351, 358, 374, 407, 408, 427, 428, 504, 505, 508, 509, 571, 590, 632, 1222, 1358, 1360, 1384, 1386, 1420], "edge_type_visual_weight_lookup": 16, "edge_weight": [16, 384, 585], "node_attribut": [16, 693], "edge_attribut": [16, 284, 693, 1104], "summary_graph": [16, 693], "snap_aggreg": [16, 760, 1422], "prefix": [16, 68, 514, 692, 693, 1278, 1332, 1353, 1422, 1430], "aggreg": [16, 513, 514, 693, 788], "summary_po": 16, "8375428": 16, "edge_typ": 16, "get_edge_data": [16, 26, 1420], "207": [16, 18, 338, 740], "plot_snap": [16, 18], "support": [17, 53, 78, 93, 94, 97, 101, 102, 103, 104, 227, 309, 323, 341, 342, 344, 345, 358, 375, 412, 413, 414, 420, 421, 466, 496, 498, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 599, 628, 629, 634, 635, 637, 638, 692, 740, 764, 777, 788, 798, 1040, 1041, 1042, 1043, 1117, 1119, 1152, 1308, 1332, 1347, 1348, 1350, 1359, 1360, 1361, 1362, 1363, 1364, 1385, 1386, 1389, 1391, 1395, 1403, 1404, 1405, 1407, 1411, 1413, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434, 1435, 1436], "unsupport": 17, "contain": [17, 26, 35, 46, 66, 70, 72, 89, 100, 103, 105, 115, 116, 145, 152, 153, 158, 159, 166, 167, 168, 169, 173, 176, 177, 178, 181, 189, 190, 194, 196, 200, 208, 213, 215, 221, 227, 237, 238, 239, 241, 242, 244, 246, 249, 250, 253, 254, 256, 257, 258, 259, 260, 261, 265, 267, 268, 271, 278, 279, 281, 282, 291, 294, 295, 300, 316, 321, 323, 340, 346, 348, 349, 352, 354, 355, 356, 357, 359, 360, 362, 375, 379, 381, 382, 383, 390, 402, 410, 416, 417, 429, 434, 435, 439, 442, 459, 483, 484, 496, 497, 500, 501, 502, 504, 505, 508, 509, 511, 512, 514, 515, 516, 518, 551, 552, 566, 570, 574, 576, 591, 595, 598, 601, 604, 623, 626, 633, 634, 654, 658, 660, 662, 663, 664, 689, 690, 691, 697, 725, 726, 727, 728, 751, 788, 798, 855, 856, 858, 859, 864, 865, 866, 867, 870, 871, 872, 873, 879, 880, 884, 886, 889, 894, 900, 901, 903, 904, 909, 910, 911, 912, 915, 916, 923, 925, 927, 930, 936, 937, 939, 940, 945, 946, 947, 948, 951, 952, 953, 954, 961, 962, 966, 968, 971, 976, 982, 983, 985, 986, 991, 992, 993, 994, 997, 998, 1006, 1008, 1010, 1013, 1040, 1041, 1042, 1043, 1044, 1045, 1055, 1056, 1057, 1064, 1069, 1088, 1089, 1090, 1097, 1100, 1103, 1105, 1106, 1108, 1109, 1121, 1133, 1146, 1156, 1157, 1158, 1160, 1163, 1170, 1179, 1206, 1207, 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917, 927, 930, 932, 963, 971, 972, 976, 978, 999, 1010, 1013, 1040, 1042, 1043, 1097, 1332, 1413, 1420, 1426, 1434, 1436], "g_minus_h": 17, "strip": [17, 26, 70, 1221], "_node_color": 17, "_po": 17, "draw_networkx_edg": [17, 26, 27, 28, 29, 34, 36, 39, 40, 41, 42, 45, 47, 69, 84, 1136, 1139, 1140, 1142, 1143, 1420, 1422, 1434], "draw_networkx_label": [17, 26, 36, 39, 47, 72, 1136, 1139, 1140, 1141, 1143], "ncl": 17, "undirect": [17, 26, 35, 72, 94, 113, 178, 186, 205, 206, 210, 212, 213, 215, 216, 217, 218, 219, 220, 221, 222, 225, 228, 229, 230, 231, 232, 233, 238, 240, 241, 247, 248, 265, 268, 276, 278, 279, 281, 282, 294, 295, 296, 298, 299, 301, 314, 316, 319, 320, 322, 323, 330, 332, 333, 334, 335, 339, 340, 343, 347, 348, 349, 350, 351, 352, 354, 355, 373, 374, 381, 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, 416, 417, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 430, 432, 433, 439, 441, 442, 452, 465, 466, 467, 468, 469, 480, 481, 482, 483, 484, 487, 488, 489, 490, 492, 493, 494, 502, 561, 562, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 583, 584, 585, 592, 596, 597, 600, 602, 603, 607, 608, 609, 612, 613, 615, 617, 620, 621, 626, 627, 654, 660, 683, 684, 685, 686, 688, 689, 690, 691, 694, 696, 719, 720, 729, 732, 733, 734, 736, 737, 738, 739, 740, 744, 745, 755, 762, 763, 764, 769, 781, 793, 876, 893, 919, 929, 958, 975, 1002, 1012, 1039, 1041, 1059, 1063, 1091, 1093, 1101, 1104, 1118, 1127, 1128, 1129, 1139, 1141, 1152, 1172, 1173, 1179, 1181, 1188, 1190, 1193, 1195, 1196, 1197, 1199, 1202, 1203, 1204, 1205, 1208, 1212, 1213, 1223, 1225, 1236, 1249, 1250, 1253, 1256, 1257, 1258, 1260, 1265, 1279, 1281, 1282, 1284, 1285, 1288, 1304, 1329, 1332, 1333, 1339, 1347, 1348, 1350, 1357, 1358, 1359, 1360, 1377, 1383, 1384, 1385, 1386, 1387, 1389, 1391, 1397, 1398, 1404, 1410, 1411, 1413, 1415, 1417, 1420, 1423, 1426, 1436], "And": [17, 24, 48, 87, 94, 102, 108, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 469, 504, 505, 508, 509, 690, 1045, 1302, 1303, 1334, 1417, 1418, 1420, 1425, 1434], "specifi": [17, 25, 26, 63, 103, 152, 153, 158, 159, 168, 185, 186, 194, 208, 223, 224, 227, 233, 237, 239, 241, 242, 244, 245, 247, 248, 249, 261, 265, 267, 268, 269, 270, 272, 274, 277, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 300, 306, 311, 312, 321, 325, 328, 331, 340, 350, 351, 355, 358, 359, 376, 379, 412, 413, 414, 415, 416, 417, 420, 421, 435, 437, 438, 442, 444, 445, 446, 447, 449, 450, 451, 460, 475, 493, 496, 497, 500, 501, 512, 520, 554, 555, 556, 557, 566, 567, 568, 577, 579, 586, 590, 599, 603, 606, 610, 611, 637, 638, 662, 673, 674, 675, 676, 678, 688, 693, 694, 706, 707, 708, 709, 710, 712, 713, 714, 715, 716, 717, 718, 723, 724, 753, 762, 855, 856, 858, 859, 866, 875, 876, 884, 894, 900, 901, 903, 904, 911, 918, 919, 923, 930, 936, 937, 939, 940, 947, 949, 950, 957, 958, 965, 966, 976, 982, 983, 985, 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106, 108, 128, 385, 628, 629, 719, 720, 1045], "g_ex": 17, "m": [17, 26, 29, 31, 32, 64, 66, 68, 92, 94, 97, 103, 107, 111, 113, 129, 182, 192, 202, 210, 212, 213, 220, 228, 232, 236, 237, 239, 240, 241, 242, 244, 245, 249, 258, 259, 260, 264, 273, 275, 276, 279, 281, 283, 285, 294, 295, 297, 301, 302, 303, 309, 310, 316, 317, 318, 332, 340, 343, 345, 347, 354, 357, 358, 363, 364, 372, 382, 385, 387, 414, 431, 433, 434, 435, 453, 464, 481, 496, 500, 501, 511, 512, 513, 514, 521, 547, 557, 571, 584, 586, 587, 589, 590, 608, 616, 621, 627, 654, 660, 661, 686, 688, 693, 694, 708, 750, 751, 763, 764, 777, 874, 882, 891, 955, 964, 973, 1063, 1157, 1161, 1163, 1175, 1181, 1183, 1185, 1187, 1205, 1207, 1208, 1209, 1210, 1211, 1213, 1214, 1215, 1216, 1217, 1219, 1221, 1222, 1224, 1225, 1226, 1228, 1229, 1232, 1235, 1236, 1237, 1239, 1240, 1241, 1246, 1262, 1271, 1275, 1277, 1284, 1285, 1286, 1293, 1294, 1298, 1329, 1395, 1415, 1418, 1436], "node_color_list": 17, "nc": [17, 57], "spectral_layout": [17, 44, 1147, 1408, 1415], "subgraphs_of_g_ex": 17, "removed_edg": 17, "node_color_list_c": 17, "One": [17, 53, 56, 102, 103, 104, 116, 348, 547, 561, 562, 681, 686, 763, 1183, 1192, 1278, 1321, 1332, 1413, 1436], "g_ex_r": 17, "compos": [17, 270, 271, 272, 273, 274, 275, 276, 277, 602, 606, 760, 1409, 1415, 1416, 1426, 1432, 1434], "previous": [17, 92, 109, 113, 323, 616, 1188, 1189, 1190, 1404, 1416, 1426], "store": [17, 26, 40, 54, 55, 56, 58, 68, 87, 94, 98, 102, 103, 111, 159, 220, 221, 284, 291, 347, 348, 349, 433, 472, 473, 474, 475, 476, 477, 496, 497, 500, 501, 504, 505, 508, 509, 511, 512, 587, 589, 617, 662, 666, 669, 721, 735, 741, 764, 788, 798, 859, 904, 940, 986, 1040, 1041, 1042, 1043, 1045, 1049, 1088, 1089, 1104, 1105, 1107, 1171, 1176, 1199, 1202, 1203, 1204, 1205, 1219, 1221, 1284, 1300, 1302, 1336, 1339, 1340, 1351, 1354, 1355, 1356, 1365, 1366, 1369, 1370, 1371, 1372, 1377, 1390, 1396, 1398, 1403, 1413, 1423], "assert": [17, 68, 89, 103, 1420, 1423, 1433, 1436], "is_isomorph": [17, 586, 587, 589, 590, 610, 673, 692, 741, 760, 763, 764, 1408, 1415], "700": [17, 18, 47], "plot_subgraph": [17, 18, 1423], "27": [18, 65, 67, 69, 103, 227, 236, 267, 302, 303, 309, 310, 328, 348, 360, 385, 386, 437, 438, 455, 705, 1264, 1301, 1342, 1412], "401": [18, 70], "auto_examples_algorithm": 18, "03": [18, 22, 26, 60, 73, 86, 113, 218, 275, 301], "read": [19, 23, 26, 41, 53, 55, 56, 58, 59, 66, 76, 87, 94, 95, 101, 106, 116, 160, 166, 168, 191, 201, 268, 585, 620, 798, 860, 864, 866, 881, 890, 905, 909, 911, 941, 945, 947, 949, 963, 972, 987, 991, 993, 995, 1015, 1016, 1021, 1022, 1023, 1024, 1025, 1038, 1039, 1040, 1041, 1042, 1043, 1045, 1046, 1064, 1069, 1085, 1086, 1091, 1124, 1149, 1150, 1276, 1302, 1331, 1332, 1335, 1336, 1339, 1343, 1344, 1348, 1349, 1351, 1354, 1355, 1356, 1357, 1358, 1360, 1362, 1363, 1373, 1374, 1377, 1381, 1383, 1384, 1386, 1389, 1390, 1391, 1394, 1395, 1396, 1397, 1398, 1403, 1404, 1406, 1407, 1410, 1411, 1413, 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1426, 1434, 1436], "mail": [46, 93, 94, 95, 100, 101, 104, 105, 107, 1402, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434, 1435], "target_nam": 46, "target_addr": 46, "227": 46, "208": [46, 48, 81, 113], "plot_unix_email": [46, 48], "elarg": 47, "esmal": 47, "font_famili": [47, 1139, 1140, 1142], "san": [47, 133, 734, 1139, 1140, 1142, 1245], "serif": [47, 1139, 1140, 1142], "edge_label": [47, 68, 1127, 1128, 1129, 1140], "get_edge_attribut": [47, 1088, 1413], "draw_networkx_edge_label": [47, 68, 1136, 1139, 1141, 1142, 1143, 1422], "090": [47, 48], "plot_weighted_graph": [47, 48], "04": [48, 327], "314": 48, "auto_examples_draw": 48, "javascript": [49, 52, 87, 1365, 1369, 1371, 1408, 1415, 1419, 1422], "igraph": [49, 52, 87, 1422], "json": [50, 59, 1331, 1365, 1367, 1368, 1369, 1370, 1371, 1392, 1408, 1411, 1415, 1416, 1420, 1421], "d3": [50, 1393, 1408, 1415], "need": [50, 55, 58, 59, 74, 77, 80, 82, 84, 85, 87, 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1361, 1364, 1369, 1374, 1377, 1378, 1381, 1382, 1390, 1415, 1416, 1421], "angular": [53, 56], "inform": [53, 67, 93, 94, 100, 101, 102, 103, 104, 108, 112, 113, 122, 133, 160, 166, 201, 203, 205, 221, 227, 231, 232, 250, 302, 303, 304, 309, 310, 315, 324, 325, 326, 327, 340, 407, 408, 440, 455, 457, 482, 490, 502, 514, 566, 568, 570, 574, 575, 576, 585, 594, 616, 621, 626, 693, 777, 784, 788, 798, 860, 864, 890, 892, 893, 905, 909, 928, 929, 941, 945, 972, 974, 975, 987, 991, 1011, 1012, 1040, 1042, 1043, 1045, 1115, 1147, 1149, 1191, 1212, 1220, 1222, 1223, 1224, 1225, 1273, 1286, 1296, 1302, 1362, 1379, 1381, 1382, 1389, 1391, 1397, 1398, 1402, 1403, 1413, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434, 1435, 1436], "angl": [53, 56, 1117, 1119, 1127, 1128, 1129], "instead": [53, 94, 95, 102, 103, 104, 107, 142, 166, 171, 283, 321, 340, 368, 372, 392, 394, 401, 407, 408, 409, 413, 414, 418, 419, 420, 421, 426, 427, 429, 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1355, 1387, 1388, 1413], "centroid": [55, 58, 59], "libpys": [55, 56, 58, 59], "cg": [55, 103, 297, 302, 303, 304, 309, 310, 324, 590], "voronoi_fram": 55, "contextili": [55, 56, 58], "add_basemap": [55, 56, 58], "geopackag": [55, 56, 57, 58], "sqlite": [55, 58], "reli": [55, 58, 100, 104, 364, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 504, 505, 508, 509, 1402, 1416, 1420, 1434], "fiona": [55, 58], "level": [55, 58, 102, 104, 105, 107, 112, 113, 116, 126, 166, 221, 323, 336, 338, 376, 382, 383, 389, 391, 392, 396, 425, 429, 642, 693, 772, 788, 864, 909, 945, 991, 1015, 1016, 1021, 1022, 1023, 1024, 1025, 1097, 1111, 1161, 1208, 1213, 1214, 1242, 1302, 1329, 1334, 1405, 1408, 1416, 1421, 1422, 1423], "interfac": [55, 58, 59, 76, 77, 97, 99, 100, 102, 103, 108, 110, 111, 185, 431, 498, 675, 760, 763, 764, 782, 875, 918, 957, 1001, 1045, 1047, 1332, 1334, 1402, 1405, 1407, 1411, 1413, 1414, 1415, 1418, 1422, 1423, 1434, 1436], "kind": [55, 58, 59, 93, 94, 95, 100, 209, 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588, 595, 732, 741, 1423], "asmatrix": 96, "wrapper": [96, 1122, 1131, 1302, 1414, 1422], "google_matrix": [96, 568, 1423, 1434], "futurewarn": [96, 1422, 1423], "attrmatrix": [96, 1434], "reflect": [96, 100, 104, 200, 297, 302, 303, 304, 309, 310, 324, 468, 889, 927, 971, 1010, 1064, 1069, 1085, 1088, 1089, 1332, 1415, 1416, 1429], "ndarrai": [96, 108, 567, 631, 1101, 1105, 1284, 1395, 1414, 1423, 1434], "distance_measur": [96, 218, 1420], "extrema_bound": [96, 1425, 1434], "maxcardin": [96, 583, 585, 1425, 1434], "min_weight_match": [96, 760, 1425, 1434], "scale_free_graph": [96, 1422, 1429], "nx_pydot": [96, 1044, 1045, 1130, 1131, 1132, 1133, 1134, 1405, 1417, 1434, 1436], "5723": [96, 1434], "node_link": [96, 1416, 1431, 1434], "node_link_graph": [96, 1369, 1392], "forest_str": [96, 1422], "write_network_text": [96, 1279, 1392], "1rc1": [97, 111, 1331, 1426], "dev0": [97, 111, 1331], "feb": [97, 111, 1331], "2023": [97, 111, 1331, 1434], "about": [97, 100, 101, 102, 104, 106, 112, 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217, 412, 415, 1415], "obsolet": [100, 268, 1343, 1415, 1416], "never": [100, 185, 390, 610, 875, 918, 957, 1001, 1242], "meant": [100, 292, 293, 633, 1223, 1332, 1422, 1426], "concret": [100, 101], "think": [100, 103, 231, 232, 300, 763, 1436], "bodi": [100, 1249], "briefli": 100, "sentenc": [100, 101], "substant": 100, "pipermail": 100, "2018": [100, 316, 332, 439, 762, 1415, 1417, 1418], "june": [100, 693, 1261, 1407, 1411, 1415, 1428, 1429], "078345": 100, "verg": 100, "chanc": [100, 231, 1240, 1302], "period": [100, 1217, 1218, 1219, 1221, 1303, 1412, 1415, 1421], "beyond": [100, 108, 385, 1216, 1242], "fine": 100, "shouldn": [100, 103], "rigid": 100, "compromis": 100, "followup": [100, 1422], "notifi": [100, 1423], "celebratori": 100, "emoji": 100, "again": [100, 430, 763, 1223, 1412, 1416, 1420, 1425], "unusu": [100, 1402], "disagr": [100, 101], "escal": [100, 101], "controversi": [100, 108], "ultim": 100, "practic": [100, 211, 221, 483, 484, 496, 621, 655, 1334, 1414], "precis": [100, 313, 570, 574, 583, 1275, 1404, 1418], "natur": [100, 103, 110, 378, 445, 468, 587, 589, 620, 755, 1160, 1223, 1231, 1247, 1302, 1332, 1402, 1419], "utf": [100, 268, 269, 1339, 1340, 1343, 1344, 1345, 1346, 1347, 1350, 1361, 1364, 1374, 1377, 1378, 1381, 1382, 1395, 1415], "restructuredtext": 100, "restructuredtextprim": 100, "dd": [100, 105, 1097], "mmm": 100, "yyyi": [100, 105], "dom": 100, "ain": 100, "separ": [100, 103, 106, 107, 153, 158, 159, 196, 215, 216, 259, 266, 267, 268, 269, 300, 323, 345, 429, 430, 456, 466, 760, 856, 858, 859, 886, 901, 903, 904, 925, 937, 939, 940, 968, 983, 985, 986, 1008, 1048, 1115, 1119, 1199, 1201, 1222, 1331, 1337, 1338, 1339, 1340, 1341, 1342, 1343, 1344, 1345, 1346, 1375, 1376, 1377, 1378, 1404, 1415, 1416, 1421, 1422, 1434, 1436], "older": 100, "brows": 100, "colgat": [101, 111], "deadlock": 101, "websit": [101, 107, 1171, 1390, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434, 1435], "ongo": [101, 1414], "trust": [101, 1389, 1391], "cast": [101, 102, 1421, 1431], "vote": [101, 339, 1421], "therebi": 101, "adher": 101, "nomin": 101, "lazi": [101, 327, 1289, 1290], "unanim": 101, "agreement": [101, 1208], "initi": [101, 103, 142, 231, 232, 283, 316, 325, 326, 340, 375, 379, 380, 468, 497, 513, 514, 527, 537, 617, 694, 721, 735, 798, 852, 897, 933, 979, 1040, 1042, 1043, 1105, 1108, 1111, 1120, 1191, 1192, 1193, 1194, 1229, 1233, 1240, 1284, 1285, 1302, 1308, 1329, 1403, 1404, 1415, 1420, 1421, 1422, 1423], "voic": 101, "smooth": 101, "strateg": 101, "plan": [101, 106, 1403, 1414, 1416, 1422], "fund": [101, 1423, 1434], "theirs": 101, "pursu": 101, "pictur": [101, 1127, 1128, 1129], "perspect": [101, 105, 1201, 1332], "timefram": 101, "entiti": [101, 1351, 1354, 1355, 1356, 1390, 1436], "occasion": [101, 231], "seek": [101, 764, 1358, 1360, 1384, 1386, 1395], "tri": [101, 113, 345, 382, 933, 979, 1042, 1043, 1181, 1187, 1231, 1243, 1244, 1413], "distinguish": [101, 936, 965, 982, 1005, 1043], "fundament": [101, 108, 111, 340, 451, 620, 621, 1223, 1422], "flaw": 101, "forward": [101, 106, 218, 452, 713, 719, 720], "typo": [101, 1405, 1415, 1416, 1417, 1420, 1421, 1422, 1423, 1425, 1426, 1428, 1430, 1434], "land": 101, "outlin": [101, 250, 338, 464, 1416], "templat": [101, 1422], "taken": [101, 102, 146, 149, 208, 445, 452, 719, 720, 751, 763, 894, 930, 976, 1013, 1120, 1418], "suffici": [101, 102, 1332], "scikit": [101, 104, 110], "expos": [102, 376, 1414], "nodeview": [102, 185, 393, 600, 601, 603, 604, 605, 606, 697, 875, 918, 957, 1001, 1039, 1091, 1355, 1368, 1413, 1416], "nodedataview": [102, 185, 393, 593, 594, 602, 875, 918, 957, 1001, 1223, 1436], "edgeview": [102, 592, 593, 594, 600, 601, 602, 603, 604, 605, 606, 614, 626, 772, 912, 1039, 1091, 1101, 1413, 1422], "edgedataview": [102, 169, 190, 867, 880, 912, 948, 962, 994, 1101, 1223, 1368, 1421, 1436], "semant": [102, 533, 543, 764, 1412, 1414], "inher": [102, 221, 429], "impli": [102, 111, 133, 221, 313, 315, 329, 457, 468, 513, 514, 547, 1302], "element": [102, 103, 231, 232, 271, 292, 293, 312, 352, 373, 393, 459, 466, 520, 561, 562, 580, 581, 582, 588, 642, 658, 673, 675, 677, 679, 730, 732, 741, 751, 754, 1039, 1041, 1051, 1052, 1053, 1054, 1090, 1091, 1141, 1143, 1179, 1212, 1217, 1218, 1223, 1243, 1244, 1246, 1255, 1278, 1283, 1284, 1285, 1288, 1293, 1294, 1302, 1308, 1309, 1317, 1324, 1329, 1361, 1364, 1367, 1368, 1414], "intend": [102, 105, 108, 112, 329, 569, 1041, 1045, 1275, 1302, 1402], "impos": [102, 104, 547, 793], "due": [102, 103, 110, 232, 265, 442, 583, 585, 628, 629, 1223, 1414, 1421, 1423, 1432, 1434], "bit": [102, 210, 212, 213, 455, 513, 514, 788, 1351, 1354, 1355, 1356, 1390, 1420, 1434], "lot": [102, 106, 454, 1332, 1414], "screen": 102, "instinct": 102, "error": [102, 103, 153, 158, 159, 196, 281, 289, 297, 312, 325, 416, 424, 473, 474, 475, 476, 477, 491, 499, 503, 506, 507, 510, 558, 559, 560, 566, 568, 583, 586, 655, 662, 669, 677, 678, 798, 856, 858, 859, 886, 901, 903, 904, 925, 937, 939, 940, 968, 983, 985, 986, 1008, 1040, 1046, 1120, 1150, 1405, 1410, 1413, 1415, 1416, 1420, 1421, 1422, 1423, 1426, 1428, 1434], "definit": [102, 133, 236, 239, 244, 290, 292, 293, 304, 324, 344, 358, 400, 437, 439, 466, 469, 551, 552, 553, 610, 620, 621, 622, 627, 678, 687, 689, 702, 737, 739, 793, 1198, 1199, 1203, 1223, 1241, 1293, 1332, 1415, 1422, 1436], "coupl": [102, 103, 133, 1263, 1411, 1413], "realis": 102, "But": [102, 103, 108, 144, 171, 239, 244, 257, 278, 279, 282, 298, 299, 585, 798, 868, 913, 1015, 1016, 1021, 1022, 1023, 1024, 1025, 1040, 1042, 1043, 1097, 1334, 1402, 1434], "seem": [102, 103, 299, 308, 793, 1240], "eas": [102, 108, 1418], "idiom": [102, 160, 191, 201, 860, 881, 890, 905, 941, 963, 972, 987, 1302, 1403, 1413, 1420], "subscript": [102, 152, 160, 201, 798, 855, 860, 890, 900, 905, 936, 941, 972, 982, 987, 1040, 1042, 1043, 1403, 1436], "repr": [102, 1353, 1422], "4950": [102, 1423], "traceback": [102, 452, 466, 586, 654, 660, 1308, 1309], "recent": [102, 439, 452, 466, 586, 654, 660, 966, 1006, 1308, 1309, 1420], "typeerror": [102, 384, 466, 1212, 1308, 1413], "opaqu": 102, "ambigu": [102, 104, 116, 253, 254, 466, 764, 1046, 1415], "ambigi": 102, "counter": [102, 154, 359], "nativ": [102, 110], "caveat": 102, "nodes_it": [102, 1413, 1416], "toward": [102, 687, 1416, 1422, 1434], "inner": [102, 231, 232, 382, 798, 1015, 1016, 1021, 1022, 1023, 1024, 1025, 1040, 1042, 1043, 1089], "synonym": 102, "primarili": [102, 1436], "becam": [102, 1420], "concept": [102, 133, 221, 311, 429, 690, 1046], "intuit": [102, 110], "On": [102, 106, 157, 218, 295, 298, 299, 307, 308, 316, 382, 407, 408, 516, 517, 520, 595, 857, 902, 938, 984, 1186, 1208, 1230, 1234, 1238], "front": [102, 621, 1039, 1091], "constuct": 102, "indx": 102, "desir": [102, 103, 143, 144, 205, 348, 349, 424, 427, 428, 600, 631, 649, 893, 975, 1088, 1097, 1105, 1106, 1108, 1127, 1128, 1156, 1158, 1163, 1165, 1166, 1169, 1171, 1193, 1224, 1226, 1227, 1240, 1287, 1362, 1363, 1423, 1436], "prelimanari": 102, "impelement": 102, "4086": 102, "rid": [102, 1422], "getitem": 102, "dunder": [102, 108, 1302, 1422], "isinst": [102, 104, 466, 1089, 1420, 1421, 1422], "_node": [102, 1431], "exclus": [102, 451, 478], "necess": 102, "unhash": [102, 1413], "impel": 102, "insipir": 102, "colon": [102, 1430], "syntax": [102, 103, 172, 798, 869, 914, 950, 996, 1040, 1042, 1043, 1129, 1302, 1390, 1391, 1419, 1421], "introspect": 102, "neither": [102, 111, 306, 429, 627, 637, 638, 673, 674, 675, 676, 678, 702, 750], "downsid": 102, "drawback": 102, "discover": 102, "complic": [102, 1302, 1332], "nix": 102, "background": 102, "pertain": 102, "arguabl": [102, 103], "overrid": [102, 673, 674, 675, 676, 1127, 1128, 1129, 1420], "mix": [102, 237, 238, 239, 242, 243, 244, 245, 246, 249, 447, 760, 1103, 1347, 1348, 1350, 1361, 1362, 1363, 1364, 1389, 1391, 1402, 1415, 1416, 1420], "pervas": 102, "unforeseen": 102, "preced": [102, 153, 158, 466, 600, 705, 856, 858, 901, 903, 937, 939, 983, 985, 1048, 1369, 1370], "un": [102, 466, 734, 1416, 1422], "sliceabl": 102, "notabl": [102, 1045], "dict_kei": [102, 1309, 1423], "dict_valu": [102, 381, 1413, 1422], "cpython": [102, 108, 431, 498, 1041, 1411, 1422], "consider": [102, 104, 325, 326, 348, 349, 355, 527, 537, 557, 673, 674, 675, 676, 734, 762, 1174, 1422], "cours": [102, 106, 218, 620, 1332, 1436], "action": [102, 107, 1045, 1422, 1426, 1434], "allevi": 102, "dig": 102, "enough": [102, 470, 511, 1171, 1387], "satisfactorili": 102, "reconsid": [102, 1421], "went": [102, 504], "ahead": 102, "4300": [102, 1422], "4304": [102, 1422], "path_edg": 103, "former": [103, 104, 793], "stylist": 103, "creation": [103, 108, 111, 250, 276, 790, 1160, 1176, 1230, 1234, 1236, 1238, 1331, 1408, 1413, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434, 1435], "cleaner": [103, 1410, 1415], "creativ": [103, 466, 468], "demand": [103, 498, 499, 503, 506, 507, 510], "had": [103, 654, 1223, 1302, 1418, 1425], "node_iter": 103, "isn": [103, 348, 349, 722, 1337, 1340, 1415, 1423, 1434], "leav": [103, 232, 390, 502, 510, 586, 587, 588, 589, 680, 1151, 1161, 1302, 1413, 1418, 1436], "dg": [103, 208, 323, 457, 458, 459, 460, 461, 463, 464, 466, 467, 468, 469, 470, 471, 894, 930, 976, 1013, 1044, 1413, 1436], "mdg": [103, 208, 894, 930, 976, 1013, 1429], "customgraph": 103, "elist": [103, 1332], "isol": [103, 357, 382, 437, 493, 494, 524, 526, 623, 737, 739, 760, 1224, 1331, 1336, 1407, 1410, 1415, 1416, 1426], "ekei": [103, 208, 894, 930, 936, 976, 982, 1013, 1087, 1107], "protocol": [103, 1413], "hashabl": [103, 145, 152, 157, 172, 181, 268, 547, 548, 549, 550, 763, 798, 855, 857, 869, 873, 900, 902, 914, 916, 936, 938, 949, 950, 954, 965, 982, 984, 995, 996, 998, 1005, 1040, 1041, 1042, 1043, 1090, 1213, 1284, 1285, 1301, 1316, 1330, 1332, 1339, 1343, 1344, 1436], "logic": [103, 104, 221, 762, 764, 1304, 1415, 1416, 1428, 1434], "denot": [103, 115, 213, 220, 300, 301, 323, 569, 570, 571, 572, 573, 574, 575, 610, 621, 689, 690, 691, 692, 693, 1127, 1128, 1129, 1180], "multiedg": [103, 555, 936, 982, 1042, 1043, 1088, 1332, 1362, 1363, 1402, 1415, 1421, 1423], "attrdict": [103, 158, 858, 903, 939, 985, 1415], "edge_kei": [103, 491, 1042, 1043, 1103, 1107, 1422], "networkxinvalidedgelist": 103, "flexibl": [103, 111, 469, 1332, 1390, 1391, 1404, 1410, 1415, 1416, 1420, 1436], "wheel": [103, 107, 1169, 1267, 1420, 1430, 1434], "spoke": 103, "wheel_graph": [103, 343, 673, 674, 676], "star": [103, 261, 301, 617, 628, 629, 781, 1057, 1157, 1166, 1229, 1233, 1403, 1413, 1415, 1416, 1420], "mycustomgraph": 103, "configuration_model_graph": 103, "deg_sequ": [103, 517, 519, 520, 1181, 1182, 1183, 1184, 1186, 1228], "graph_build": 103, "py_random_st": [103, 104, 1302, 1305, 1414, 1434], "extended_barabasi_albert_graph": 103, "node_and_edge_build": 103, "ladder_graph": 103, "incompat": [103, 1205, 1411, 1412, 1415], "thrust": 103, "incept": 103, "attach": [103, 215, 275, 359, 571, 573, 623, 1039, 1091, 1125, 1188, 1191, 1229, 1233, 1235, 1332, 1436], "presum": [103, 1303], "rewritten": [103, 1404, 1411, 1415], "gradual": 103, "accomplish": [103, 110, 1171], "wrap": [103, 1048, 1050, 1127, 1129, 1302, 1307, 1310], "custom_graph": 103, "ichain": 103, "tripl": [103, 115, 250, 251, 713, 1420], "overli": 103, "empty_graph": [103, 755, 1060, 1164, 1303, 1329, 1415, 1418, 1419], "3036": 103, "1393": 103, "canon": [103, 686, 732, 1421], "huge": 103, "path_edgelist": 103, "disallow": [103, 798, 1040, 1042, 1043, 1193, 1426], "2022": [104, 106, 695, 1423, 1424, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433], "pseudo": [104, 105, 678, 1326, 1327, 1414, 1416], "nep19": 104, "legaci": [104, 1404, 1411, 1417], "randomst": [104, 1103, 1114, 1120, 1305, 1307, 1310, 1311, 1334, 1414, 1418], "statist": [104, 111, 129, 275, 360, 385, 387, 440, 1228, 1334, 1414], "strategi": [104, 124, 223, 364, 368, 372, 455], "engin": [104, 108, 731, 733, 1421], "modern": [104, 111, 1414], "prng": 104, "np_random_st": [104, 1307, 1414, 1423], "random_st": [104, 209, 214, 218, 223, 224, 228, 231, 232, 272, 273, 275, 276, 297, 298, 307, 370, 375, 379, 380, 382, 383, 591, 627, 683, 684, 685, 686, 688, 694, 695, 696, 703, 724, 740, 749, 1170, 1171, 1174, 1175, 1176, 1177, 1179, 1181, 1183, 1185, 1187, 1188, 1189, 1190, 1191, 1192, 1193, 1194, 1199, 1201, 1202, 1203, 1204, 1205, 1208, 1209, 1210, 1211, 1216, 1228, 1229, 1230, 1231, 1232, 1233, 1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244, 1245, 1246, 1247, 1275, 1279, 1281, 1282, 1283, 1302, 1305, 1307, 1310, 1311, 1325, 1334, 1423, 1434], "mtrand": 104, "12345": [104, 1307, 1414], "rng": [104, 1044, 1103, 1305, 1307, 1334, 1414, 1418], "default_rng": [104, 1044, 1414, 1423], "_gener": 104, "stream": [104, 1414], "slight": 104, "guarante": [104, 128, 134, 185, 211, 216, 217, 236, 282, 312, 340, 382, 424, 467, 499, 503, 506, 507, 510, 513, 514, 551, 552, 553, 566, 568, 591, 655, 662, 669, 724, 730, 732, 875, 918, 957, 1001, 1103, 1122, 1123, 1126, 1187, 1247, 1300, 1414], "upheld": 104, "exact": [104, 126, 211, 216, 217, 239, 270, 272, 274, 277, 673, 674, 675, 676, 693, 782, 1181, 1183, 1228, 1411, 1414], "instanti": [104, 1302, 1403, 1436], "num": 104, "uniform": [104, 567, 568, 627, 740, 1187, 1199, 1211, 1242, 1245, 1325, 1418, 1421], "92961609": 104, "31637555": 104, "18391881": 104, "20456028": 104, "56772503": 104, "5955447": 104, "96451452": 104, "6531771": 104, "74890664": 104, "65356987": 104, "22733602": 104, "31675834": 104, "79736546": 104, "67625467": 104, "39110955": 104, "33281393": 104, "59830875": 104, "18673419": 104, "67275604": 104, "94180287": 104, "recov": [104, 359, 731, 733, 1278, 1353, 1354, 1355, 1411, 1414, 1429], "create_random_st": [104, 1305], "randint": [104, 1103], "create_py_random_st": [104, 1307, 1421, 1425], "attributeerror": 104, "compatibl": 104, "pythonrandominterfac": [104, 1307, 1310], "_rand": 104, "implicitli": 104, "16988": 104, "14042": 104, "higher": [104, 259, 298, 300, 305, 307, 315, 317, 321, 322, 323, 329, 330, 333, 380, 522, 523, 618, 705, 1063, 1191, 1240], "constraint": [104, 618, 690, 691, 695, 696, 760, 793, 1422], "releat": 104, "slep": 104, "quit": [104, 468, 1085, 1171, 1240, 1402, 1436], "encapsul": 104, "valueerror": [104, 227, 281, 348, 349, 385, 424, 427, 428, 472, 586, 596, 597, 598, 599, 610, 634, 635, 637, 638, 662, 663, 664, 688, 751, 754, 1105, 1110, 1117, 1119, 1120, 1191, 1212, 1280, 1309, 1317, 1325, 1351, 1352, 1353, 1354, 1355, 1356, 1357, 1359, 1360, 1385, 1422], "captur": [104, 1422], "reorgan": [104, 1422], "quo": 104, "perpetu": [104, 333], "toggl": 104, "backend": [104, 1014, 1331, 1422, 1434], "pkg": 104, "_random_backend": 104, "bullet": [104, 105, 1421], "regard": [104, 105, 106, 1413, 1417, 1421], "mm": 105, "achiev": [105, 302, 303, 309, 310, 382, 514, 1413, 1436], 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341, 385, 588, 590, 754, 1171, 1339, 1343, 1344, 1421], "abc": [145, 547, 1160, 1212, 1309, 1421, 1422], "interchang": [145, 364], "bool": [146, 147, 149, 150, 166, 169, 172, 177, 185, 190, 197, 205, 209, 233, 238, 239, 243, 244, 246, 250, 251, 259, 266, 267, 268, 269, 273, 276, 287, 288, 289, 292, 295, 296, 297, 298, 299, 300, 302, 303, 306, 307, 308, 309, 310, 311, 315, 316, 323, 325, 326, 327, 328, 329, 332, 345, 352, 357, 364, 395, 396, 397, 398, 399, 400, 441, 456, 464, 465, 469, 481, 482, 490, 491, 493, 496, 500, 501, 511, 512, 515, 516, 517, 518, 519, 520, 522, 523, 524, 547, 564, 566, 580, 581, 582, 583, 590, 615, 616, 618, 619, 624, 625, 627, 642, 654, 665, 675, 681, 687, 692, 698, 700, 701, 702, 706, 710, 721, 725, 726, 727, 728, 730, 732, 735, 736, 737, 738, 739, 740, 742, 743, 744, 745, 864, 867, 869, 872, 875, 880, 887, 893, 909, 912, 914, 918, 929, 933, 945, 948, 950, 953, 957, 962, 969, 975, 979, 991, 994, 996, 1001, 1042, 1043, 1048, 1060, 1071, 1073, 1074, 1075, 1087, 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1411], "342": [152, 855, 900, 936, 982, 1261], "ebunch_to_add": [153, 159, 856, 859, 901, 904, 937, 940, 983, 986], "add_weighted_edges_from": [153, 230, 231, 232, 327, 510, 583, 632, 659, 661, 723, 856, 901, 937, 983, 1073, 1332, 1413, 1416, 1436], "runtimeerror": [153, 158, 159, 196, 466, 467, 468, 856, 858, 859, 886, 901, 903, 904, 925, 937, 939, 940, 968, 983, 985, 986, 1008], "happen": [153, 158, 159, 196, 382, 586, 856, 858, 859, 886, 901, 903, 904, 925, 937, 939, 940, 968, 983, 985, 986, 1008, 1412, 1413, 1434], "iterator_of_edg": [153, 159, 856, 859, 901, 904, 937, 940, 983, 986], "wn2898": [153, 856, 901, 937, 983], "wrong": [153, 158, 159, 724, 856, 858, 859, 901, 903, 904, 937, 939, 940, 983, 985, 986, 1415, 1420, 1425, 1434], "start_nod": [154, 155, 156], "end_nod": [154, 155, 156], "reference_neighbor": [154, 155], "half": [154, 155, 156, 165, 178, 184, 207, 298, 299, 617, 655], "clockwis": [154, 155, 170, 183, 198, 617], "networkxexcept": [154, 155, 162, 333, 590, 595, 726, 728, 1046, 1113, 1144, 1186, 1331], "add_half_edge_cw": [154, 156, 165, 617], "connect_compon": [154, 155, 156, 617], "add_half_edge_first": [154, 155, 165, 617], "add_half_edge_ccw": [155, 156, 165, 617], "node_for_ad": [157, 857, 902, 938, 984], "mutabl": [157, 857, 902, 938, 984, 1064, 1069, 1085, 1088, 1089], "hash": [157, 513, 514, 760, 857, 902, 938, 984, 1330, 1331, 1423, 1436], "hello": [157, 158, 857, 858, 902, 903, 938, 939, 984, 985, 1309], "k3": [157, 158, 857, 858, 902, 903, 938, 939, 984, 985, 1223], "utm": [157, 857, 902, 938, 984], "382871": [157, 857, 902, 938, 984], "3972649": [157, 857, 902, 938, 984], "nodes_for_ad": [158, 858, 903, 939, 985], "iterator_of_nod": [158, 196, 858, 886, 903, 925, 939, 968, 985, 1008], "datadict": [160, 191, 201, 208, 736, 738, 860, 881, 890, 894, 905, 930, 941, 963, 972, 976, 1013, 1087, 1318, 1332], "foovalu": [160, 191, 201, 860, 881, 890, 905, 941, 972], "nbrdict": [161, 861, 906, 942, 988, 1022, 1097], "fulfil": [162, 617], "cw": [162, 617], "ccw": [162, 617], "planar": [162, 616, 618, 619, 760, 1113, 1144, 1249, 1252, 1253, 1255, 1331, 1418, 1419], "first_nbr": [162, 617], "invalid": [162, 617, 1422], "alter": [164, 863, 908, 944, 990], "afterward": 165, "as_view": [166, 203, 205, 864, 892, 893, 909, 928, 929, 945, 974, 975, 991, 1011, 1012, 1092, 1093], "shallow": [166, 203, 205, 285, 286, 287, 288, 289, 864, 892, 893, 909, 928, 929, 945, 974, 975, 991, 1011, 1012, 1403], "deepcopi": [166, 203, 205, 864, 892, 893, 909, 928, 929, 945, 974, 975, 991, 1011, 1012, 1418], "__class__": [166, 200, 864, 889, 909, 927, 945, 971, 991, 1010, 1413, 1416, 1418, 1419, 1420], "fresh": [166, 864, 909, 945, 991, 1413], "inspir": [166, 231, 232, 344, 683, 864, 909, 945, 991, 1232, 1329, 1413], "deep": [166, 203, 205, 864, 892, 893, 909, 928, 929, 945, 974, 975, 991, 1011, 1012, 1271, 1403], "degreeview": [167, 865, 910, 946, 952, 992, 1413, 1436], "didegreeview": [167, 865], "outedgeview": [169, 190, 469, 470, 615, 749, 752, 867, 880, 1038, 1086, 1413, 1427], "ddict": [169, 177, 185, 190, 867, 872, 875, 880, 912, 918, 948, 953, 957, 962, 994, 1001], "in_edg": [169, 190, 867, 880, 948, 962, 1413, 1415, 1416], "out_edg": [169, 867, 948, 1065, 1413, 1415, 1416, 1436], "quietli": [169, 190, 867, 880, 912, 948, 962, 994, 1090, 1436], "outedgedataview": [169, 190, 867, 880, 1413, 1420], "set_data": 170, "edge_dict": [171, 868, 913, 949, 995], "safe": [171, 868, 913, 1413, 1421], "edge_ind": [172, 869, 914, 950, 996], "data_dictionari": [172, 869, 914], "simpler": [173, 185, 870, 875, 915, 918, 951, 957, 997, 1001, 1415, 1416, 1426], "indegreeview": [176, 871, 1413], "deg": [176, 189, 244, 260, 358, 363, 687, 871, 879, 952, 961, 1171, 1185, 1228, 1413], "inedgeview": [177, 872, 1413], "inedgedataview": [177, 872], "silent": [181, 194, 196, 321, 873, 884, 886, 916, 923, 925, 954, 966, 968, 998, 1006, 1008, 1088, 1089, 1133, 1359, 1360, 1365, 1369, 1415, 1422], "niter": [181, 683, 684, 685, 686, 853, 873, 898, 916, 934, 954, 980, 998, 1423], "__iter__": [181, 873, 916, 954, 998, 1309], "nodedata": [185, 875, 918, 957, 1001], "5pm": [185, 798, 875, 918, 957, 1001, 1040, 1042, 1043, 1403, 1436], "Not": [185, 381, 434, 435, 436, 437, 438, 439, 440, 478, 875, 918, 957, 1001, 1120, 1222], "nedg": [186, 590, 876, 919, 958, 1002], "__len__": [187, 188, 877, 878, 920, 921, 959, 960, 1003, 1004], "outdegreeview": [189, 879], "Will": [194, 364, 607, 609, 612, 884, 923, 966, 1006, 1413, 1423], "get_data": [198, 618], "inplac": [200, 692, 889, 927, 971, 1010, 1069, 1402], "reduct": [200, 471, 620, 788, 889, 927, 971, 1010, 1069, 1326, 1327, 1422, 1423], "sg": [200, 889, 927, 971, 1010], "largest_wcc": [200, 889, 927, 971, 1010], "is_multigraph": [200, 760, 889, 927, 971, 1010, 1160, 1421], "keydict": [200, 208, 889, 894, 927, 930, 971, 976, 1010, 1013, 1042, 1043], "contrast": [203, 205, 302, 303, 309, 310, 892, 893, 928, 929, 974, 975, 1011, 1012, 1069, 1239, 1247, 1436], "reciproc": [205, 300, 321, 323, 358, 413, 432, 449, 478, 622, 760, 893, 975, 1331, 1425, 1434], "mark_half_edg": 207, "li": [207, 621, 672, 677, 687, 777, 1213, 1216, 1434], "straightforward": [208, 894, 930, 976, 1013], "slightli": [208, 328, 439, 522, 523, 583, 894, 930, 976, 1013, 1171, 1332, 1413, 1416, 1421, 1423, 1434], "singleton": [208, 358, 590, 894, 930, 976, 1013, 1224, 1257, 1416], "preserve_attr": [209, 725, 726, 727, 728], "optimum": [209, 232, 585, 722, 724, 793, 1404, 1415], "arboresc": [209, 462, 721, 722, 724, 726, 728, 742, 745, 760, 1278, 1404, 1415], "span": [209, 227, 228, 229, 296, 510, 620, 621, 626, 721, 722, 724, 726, 728, 734, 735, 736, 737, 738, 739, 740, 760, 1403, 1406, 1415, 1416, 1429], "max_ind_cliqu": 210, "networkxnotimpl": [210, 211, 212, 213, 221, 225, 228, 294, 295, 296, 319, 320, 322, 330, 345, 381, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 405, 406, 407, 408, 409, 424, 426, 427, 428, 429, 431, 457, 459, 460, 461, 462, 470, 483, 484, 502, 591, 592, 610, 682, 734, 1046, 1222, 1281, 1282, 1304, 1331, 1359, 1360, 1385, 1416, 1417], "boppana": [210, 212, 213], "halld\u00f3rsson": [210, 212, 213], "1992": [210, 212, 213, 519, 520, 1416], "exclud": [210, 212, 213, 216, 217, 262, 263, 455, 690, 721, 725, 726, 727, 728, 735, 753, 1039, 1041, 1091, 1223, 1421], "180": [210, 212, 213, 239, 1434], "196": [210, 212, 213], "heurist": [211, 221, 229, 234, 235, 379, 382, 383, 429, 496, 511, 628, 629, 654, 665, 705, 760, 1179, 1326, 1327, 1331, 1404, 1417, 1421, 1422], "max_cliqu": 211, "rigor": 211, "pattabiraman": 211, "bharath": 211, "massiv": [211, 218], "421": 211, "448": 211, "1080": [211, 298, 299, 307, 308, 331], "15427951": 211, "986778": 211, "apx": [212, 213], "subseteq": [212, 281, 290, 620, 677], "omega": [212, 760, 784, 1423], "maximum_cliqu": 212, "1007": [212, 227, 297, 302, 303, 304, 309, 310, 324, 325, 326, 343, 433, 453, 500, 576, 1150, 1187], "bf01994876": 212, "iset": 213, "trial": [214, 231, 232, 1201, 1243, 1244], "estim": [214, 225, 298, 307, 314, 566, 627, 628, 629, 784, 1286, 1416], "coeffici": [214, 249, 261, 262, 263, 264, 290, 357, 358, 360, 572, 620, 621, 627, 684, 686, 780, 784, 1406, 1407, 1408, 1415, 1422], "fraction": [214, 258, 260, 287, 290, 298, 300, 305, 307, 316, 318, 319, 320, 322, 323, 328, 330, 332, 358, 360, 361, 521, 1127, 1129, 1171, 1240], "schank": 214, "thoma": [214, 753, 1416, 1418, 1422], "dorothea": [214, 1174], "wagner": [214, 431, 760, 1174, 1411, 1415], "universit\u00e4t": 214, "karlsruh": 214, "fakult\u00e4t": 214, "f\u00fcr": 214, "informatik": [214, 414], "5445": 214, "ir": [214, 608], "1000001239": 214, "erdos_renyi_graph": [214, 1230, 1238, 1332, 1415, 1436], "cutoff": [215, 216, 311, 328, 385, 412, 413, 414, 420, 421, 496, 497, 500, 501, 512, 639, 640, 642, 643, 644, 645, 646, 649, 650, 651, 658, 662, 663, 664, 669, 670, 671, 679, 680, 1240, 1407, 1411, 1415, 1422, 1425, 1433, 1434], "distinct": [215, 216, 256, 282, 289, 354, 393, 454, 455, 462, 580, 597, 610, 620, 702, 703, 736, 737, 738, 739, 791, 1156, 1250, 1277, 1329, 1332, 1334, 1404, 1426], "nonadjac": [215, 216, 482, 586, 587, 589], "cutset": [215, 216, 416, 417, 418, 419, 429, 430, 502, 508, 760], "menger": [215, 216, 217], "theorem": [215, 216, 217, 221, 236, 282, 312, 313, 323, 413, 508, 509, 516, 519, 520, 620, 1196, 1211], "local_node_connect": [215, 217, 410, 411, 412, 413, 415], "node_connect": [215, 216, 411, 412, 413, 414, 416, 417, 418, 419, 421, 429, 430, 1411], "dougla": [215, 216, 217, 221, 1422, 1434], "035": [215, 216, 217, 221], "eclect": [215, 216, 217], "ss": [215, 216, 217], "uci": [215, 216, 217, 469, 706, 708, 709, 710, 712, 736, 738], "drwhite": [215, 216, 217], "pprint": [215, 348, 579, 713], "all_pairs_node_connect": [216, 217, 1411, 1433], "bf": [216, 217, 218, 365, 590, 706, 708, 709, 710, 719, 1406, 1410, 1415, 1418, 1421, 1422, 1434], "lose": [216, 798, 1040, 1042, 1043], "accuraci": [216, 313, 788], "platon": [216, 217, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 1251, 1254, 1260, 1263, 1267, 1269], "octahedr": [216, 217, 1263], "octahedral_graph": [216, 217], "vari": [218, 239, 244, 375, 380, 571, 697], "sweep": [218, 1421], "dsweep": 218, "a_1": [218, 479, 1127, 1128, 1129], "a_2": 218, "magnien": [218, 261, 262, 263, 290], "cl\u00e9menc": [218, 261, 262, 263, 290], "matthieu": [218, 261, 262, 263, 275, 290], "latapi": [218, 261, 262, 263, 275, 290], "michel": 218, "habib": 218, "empir": 218, "tight": 218, "jea": 218, "0904": 218, "2728": 218, "crescenzi": 218, "pierluigi": 218, "roberto": 218, "grossi": 218, "leonardo": 218, "lanzi": 218, "andrea": [218, 1171, 1422], "marino": 218, "symposium": [218, 621, 1192, 1201, 1245], "berlin": [218, 522, 523, 1422], "heidelberg": [218, 522, 523], "ut": 218, "ee": [218, 314], "mtat": 218, "238": 218, "2014_fall": 218, "domin": [219, 220, 312, 412, 416, 483, 484, 485, 486, 760, 1331, 1404, 1409, 1415, 1416], "opt": [219, 222, 1434], "min_weight_dominating_set": 220, "vazirani": [220, 222], "vijai": [220, 222, 519], "min_dens": 221, "95": [221, 327, 592, 1289, 1290, 1390], "nest": [221, 429, 730, 732, 793, 1041, 1048, 1064, 1097, 1302, 1314, 1354, 1361, 1362, 1363, 1364, 1391, 1415], "forth": [221, 429], "relax": [221, 228, 1177, 1422], "narrow": [221, 1171], "whitnei": 221, "bicompon": [221, 389, 391, 392, 396], "ferraro": [221, 429], "cohes": [221, 429, 439], "1503": [221, 429], "04476v1": [221, 429], "santaf": 221, "ind": 221, "embedded": [221, 306, 429], "sociolog": [221, 429, 750], "103": [221, 429, 1228, 1294, 1298], "2307": [221, 298, 1261], "3088904": 221, "petersen": [221, 429, 763, 1257, 1262, 1265], "triconnect": [221, 429], "apxa": 221, "petersen_graph": [221, 382, 429, 494, 763, 1122, 1123, 1436], "fo": 222, "initial_cut": 223, "highest": [223, 270, 274, 277, 339, 359, 376, 389, 391, 392, 396, 430, 511, 690, 705, 1186], "suppli": [223, 257, 278, 279, 281, 282, 596, 1203, 1326, 1327, 1332, 1351, 1354, 1355, 1356, 1390, 1417, 1422], "cut_valu": 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"shortest_path": [[637, "shortest-path"]], "shortest_path_length": [[638, "shortest-path-length"]], "all_pairs_shortest_path": [[639, "all-pairs-shortest-path"]], "all_pairs_shortest_path_length": [[640, "all-pairs-shortest-path-length"]], "bidirectional_shortest_path": [[641, "bidirectional-shortest-path"]], "predecessor": [[642, "predecessor"]], "single_source_shortest_path": [[643, "single-source-shortest-path"]], "single_source_shortest_path_length": [[644, "single-source-shortest-path-length"]], "single_target_shortest_path": [[645, "single-target-shortest-path"]], "single_target_shortest_path_length": [[646, "single-target-shortest-path-length"]], "all_pairs_bellman_ford_path": [[647, "all-pairs-bellman-ford-path"]], "all_pairs_bellman_ford_path_length": [[648, "all-pairs-bellman-ford-path-length"]], "all_pairs_dijkstra": [[649, "all-pairs-dijkstra"]], "all_pairs_dijkstra_path": [[650, "all-pairs-dijkstra-path"]], "all_pairs_dijkstra_path_length": [[651, 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"bfs_layers": [[707, "bfs-layers"]], "bfs_predecessors": [[708, "bfs-predecessors"]], "bfs_successors": [[709, "bfs-successors"]], "bfs_tree": [[710, "bfs-tree"]], "descendants_at_distance": [[711, "descendants-at-distance"]], "dfs_edges": [[712, "dfs-edges"]], "dfs_labeled_edges": [[713, "dfs-labeled-edges"]], "dfs_postorder_nodes": [[714, "dfs-postorder-nodes"]], "dfs_predecessors": [[715, "dfs-predecessors"]], "dfs_preorder_nodes": [[716, "dfs-preorder-nodes"]], "dfs_successors": [[717, "dfs-successors"]], "dfs_tree": [[718, "dfs-tree"]], "edge_bfs": [[719, "edge-bfs"]], "edge_dfs": [[720, "edge-dfs"]], "networkx.algorithms.tree.branchings.ArborescenceIterator": [[721, "networkx-algorithms-tree-branchings-arborescenceiterator"]], "networkx.algorithms.tree.branchings.Edmonds": [[722, "networkx-algorithms-tree-branchings-edmonds"]], "branching_weight": [[723, "branching-weight"]], "greedy_branching": [[724, "greedy-branching"]], "maximum_branching": [[725, "maximum-branching"]], "maximum_spanning_arborescence": [[726, "maximum-spanning-arborescence"]], "minimum_branching": [[727, "minimum-branching"]], "minimum_spanning_arborescence": [[728, "minimum-spanning-arborescence"]], "NotATree": [[729, "notatree"]], "from_nested_tuple": [[730, "from-nested-tuple"]], "from_prufer_sequence": [[731, "from-prufer-sequence"]], "to_nested_tuple": [[732, "to-nested-tuple"]], "to_prufer_sequence": [[733, "to-prufer-sequence"]], "junction_tree": [[734, "junction-tree"]], "networkx.algorithms.tree.mst.SpanningTreeIterator": [[735, "networkx-algorithms-tree-mst-spanningtreeiterator"]], "maximum_spanning_edges": [[736, "maximum-spanning-edges"]], "maximum_spanning_tree": [[737, "maximum-spanning-tree"]], "minimum_spanning_edges": [[738, "minimum-spanning-edges"]], "minimum_spanning_tree": [[739, "minimum-spanning-tree"]], "random_spanning_tree": [[740, "random-spanning-tree"]], "join": [[741, "join"]], "is_arborescence": [[742, "is-arborescence"]], "is_branching": [[743, 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[[762, "module-networkx.algorithms.isomorphism.tree_isomorphism"]], "Advanced Interfaces": [[762, "advanced-interfaces"]], "ISMAGS Algorithm": [[763, "module-networkx.algorithms.isomorphism.ismags"]], "Notes": [[763, "notes"], [764, "notes"], [1045, "notes"]], "ISMAGS object": [[763, "ismags-object"]], "VF2 Algorithm": [[764, "module-networkx.algorithms.isomorphism.isomorphvf2"]], "Subgraph Isomorphism": [[764, "subgraph-isomorphism"]], "Graph Matcher": [[764, "graph-matcher"]], "DiGraph Matcher": [[764, "digraph-matcher"]], "Match helpers": [[764, "match-helpers"]], "Link Analysis": [[765, "link-analysis"]], "PageRank": [[765, "module-networkx.algorithms.link_analysis.pagerank_alg"]], "Hits": [[765, "module-networkx.algorithms.link_analysis.hits_alg"]], "Link Prediction": [[766, "module-networkx.algorithms.link_prediction"]], "Lowest Common Ancestor": [[767, "module-networkx.algorithms.lowest_common_ancestors"]], "Minors": [[769, "module-networkx.algorithms.minors"]], "Maximal 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"DiGraph.__contains__": [[850, "digraph-contains"]], "DiGraph.__getitem__": [[851, "digraph-getitem"]], "DiGraph.__init__": [[852, "digraph-init"]], "DiGraph.__iter__": [[853, "digraph-iter"]], "DiGraph.__len__": [[854, "digraph-len"]], "DiGraph.add_edge": [[855, "digraph-add-edge"]], "DiGraph.add_edges_from": [[856, "digraph-add-edges-from"]], "DiGraph.add_node": [[857, "digraph-add-node"]], "DiGraph.add_nodes_from": [[858, "digraph-add-nodes-from"]], "DiGraph.add_weighted_edges_from": [[859, "digraph-add-weighted-edges-from"]], "DiGraph.adj": [[860, "digraph-adj"]], "DiGraph.adjacency": [[861, "digraph-adjacency"]], "DiGraph.clear": [[862, "digraph-clear"]], "DiGraph.clear_edges": [[863, "digraph-clear-edges"]], "DiGraph.copy": [[864, "digraph-copy"]], "DiGraph.degree": [[865, "digraph-degree"]], "DiGraph.edge_subgraph": [[866, "digraph-edge-subgraph"]], "DiGraph.edges": [[867, "digraph-edges"]], "DiGraph.get_edge_data": [[868, "digraph-get-edge-data"]], "DiGraph.has_edge": [[869, "digraph-has-edge"]], "DiGraph.has_node": [[870, "digraph-has-node"]], "DiGraph.in_degree": [[871, "digraph-in-degree"]], "DiGraph.in_edges": [[872, "digraph-in-edges"]], "DiGraph.nbunch_iter": [[873, "digraph-nbunch-iter"]], "DiGraph.neighbors": [[874, "digraph-neighbors"]], "DiGraph.nodes": [[875, "digraph-nodes"]], "DiGraph.number_of_edges": [[876, "digraph-number-of-edges"]], "DiGraph.number_of_nodes": [[877, "digraph-number-of-nodes"]], "DiGraph.order": [[878, "digraph-order"]], "DiGraph.out_degree": [[879, "digraph-out-degree"]], "DiGraph.out_edges": [[880, "digraph-out-edges"]], "DiGraph.pred": [[881, "digraph-pred"]], "DiGraph.predecessors": [[882, "digraph-predecessors"]], "DiGraph.remove_edge": [[883, "digraph-remove-edge"]], "DiGraph.remove_edges_from": [[884, "digraph-remove-edges-from"]], "DiGraph.remove_node": [[885, "digraph-remove-node"]], "DiGraph.remove_nodes_from": [[886, "digraph-remove-nodes-from"]], "DiGraph.reverse": [[887, "digraph-reverse"]], "DiGraph.size": 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Applying classic graph operations, such as:": [[1436, "applying-classic-graph-operations-such-as"]], "2. Using a call to one of the classic small graphs, e.g.,": [[1436, "using-a-call-to-one-of-the-classic-small-graphs-e-g"]], "3. Using a (constructive) generator for a classic graph, e.g.,": [[1436, "using-a-constructive-generator-for-a-classic-graph-e-g"]], "4. Using a stochastic graph generator, e.g,": [[1436, "using-a-stochastic-graph-generator-e-g"]], "5. Reading a graph stored in a file using common graph formats": [[1436, "reading-a-graph-stored-in-a-file-using-common-graph-formats"]], "Analyzing graphs": [[1436, "analyzing-graphs"]], "Drawing graphs": [[1436, "drawing-graphs"]], "NX-Guides": [[1436, "nx-guides"]]}, "indexentries": {"module": [[113, "module-networkx.algorithms.approximation"], [113, "module-networkx.algorithms.approximation.clique"], [113, "module-networkx.algorithms.approximation.clustering_coefficient"], [113, "module-networkx.algorithms.approximation.connectivity"], [113, "module-networkx.algorithms.approximation.distance_measures"], [113, "module-networkx.algorithms.approximation.dominating_set"], [113, "module-networkx.algorithms.approximation.kcomponents"], [113, "module-networkx.algorithms.approximation.matching"], [113, "module-networkx.algorithms.approximation.maxcut"], [113, "module-networkx.algorithms.approximation.ramsey"], [113, "module-networkx.algorithms.approximation.steinertree"], [113, 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"module-networkx.algorithms.approximation.clustering_coefficient"]], "networkx.algorithms.approximation.connectivity": [[113, "module-networkx.algorithms.approximation.connectivity"]], "networkx.algorithms.approximation.distance_measures": [[113, "module-networkx.algorithms.approximation.distance_measures"]], "networkx.algorithms.approximation.dominating_set": [[113, "module-networkx.algorithms.approximation.dominating_set"]], "networkx.algorithms.approximation.kcomponents": [[113, "module-networkx.algorithms.approximation.kcomponents"]], "networkx.algorithms.approximation.matching": [[113, "module-networkx.algorithms.approximation.matching"]], "networkx.algorithms.approximation.maxcut": [[113, "module-networkx.algorithms.approximation.maxcut"]], "networkx.algorithms.approximation.ramsey": [[113, "module-networkx.algorithms.approximation.ramsey"]], "networkx.algorithms.approximation.steinertree": [[113, "module-networkx.algorithms.approximation.steinertree"]], "networkx.algorithms.approximation.traveling_salesman": [[113, "module-networkx.algorithms.approximation.traveling_salesman"]], "networkx.algorithms.approximation.treewidth": [[113, "module-networkx.algorithms.approximation.treewidth"]], "networkx.algorithms.approximation.vertex_cover": [[113, "module-networkx.algorithms.approximation.vertex_cover"]], "networkx.algorithms.assortativity": [[114, "module-networkx.algorithms.assortativity"]], "networkx.algorithms.asteroidal": [[115, "module-networkx.algorithms.asteroidal"]], "networkx.algorithms.bipartite": [[116, "module-networkx.algorithms.bipartite"]], "networkx.algorithms.bipartite.basic": [[116, "module-networkx.algorithms.bipartite.basic"]], "networkx.algorithms.bipartite.centrality": [[116, "module-networkx.algorithms.bipartite.centrality"]], "networkx.algorithms.bipartite.cluster": [[116, "module-networkx.algorithms.bipartite.cluster"]], "networkx.algorithms.bipartite.covering": [[116, 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"networkx.algorithms.chains": [[120, "module-networkx.algorithms.chains"]], "networkx.algorithms.chordal": [[121, "module-networkx.algorithms.chordal"]], "networkx.algorithms.clique": [[122, "module-networkx.algorithms.clique"]], "networkx.algorithms.cluster": [[123, "module-networkx.algorithms.cluster"]], "networkx.algorithms.coloring": [[124, "module-networkx.algorithms.coloring"]], "networkx.algorithms.communicability_alg": [[125, "module-networkx.algorithms.communicability_alg"]], "networkx.algorithms.community": [[126, "module-networkx.algorithms.community"]], "networkx.algorithms.community.asyn_fluid": [[126, "module-networkx.algorithms.community.asyn_fluid"]], "networkx.algorithms.community.centrality": [[126, "module-networkx.algorithms.community.centrality"]], "networkx.algorithms.community.community_utils": [[126, "module-networkx.algorithms.community.community_utils"]], "networkx.algorithms.community.kclique": [[126, "module-networkx.algorithms.community.kclique"]], "networkx.algorithms.community.kernighan_lin": [[126, "module-networkx.algorithms.community.kernighan_lin"]], "networkx.algorithms.community.label_propagation": [[126, "module-networkx.algorithms.community.label_propagation"]], "networkx.algorithms.community.louvain": [[126, "module-networkx.algorithms.community.louvain"]], "networkx.algorithms.community.lukes": [[126, "module-networkx.algorithms.community.lukes"]], "networkx.algorithms.community.modularity_max": [[126, "module-networkx.algorithms.community.modularity_max"]], "networkx.algorithms.community.quality": [[126, "module-networkx.algorithms.community.quality"]], "networkx.algorithms.components": [[127, "module-networkx.algorithms.components"]], "networkx.algorithms.connectivity": [[128, "module-networkx.algorithms.connectivity"]], "networkx.algorithms.connectivity.connectivity": [[128, "module-networkx.algorithms.connectivity.connectivity"]], "networkx.algorithms.connectivity.cuts": [[128, 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"networkx.algorithms.cuts": [[131, "module-networkx.algorithms.cuts"]], "networkx.algorithms.cycles": [[132, "module-networkx.algorithms.cycles"]], "networkx.algorithms.d_separation": [[133, "module-networkx.algorithms.d_separation"]], "networkx.algorithms.dag": [[134, "module-networkx.algorithms.dag"]], "networkx.algorithms.distance_measures": [[135, "module-networkx.algorithms.distance_measures"]], "networkx.algorithms.distance_regular": [[136, "module-networkx.algorithms.distance_regular"]], "networkx.algorithms.dominance": [[137, "module-networkx.algorithms.dominance"]], "networkx.algorithms.dominating": [[138, "module-networkx.algorithms.dominating"]], "networkx.algorithms.efficiency_measures": [[139, "module-networkx.algorithms.efficiency_measures"]], "networkx.algorithms.euler": [[140, "module-networkx.algorithms.euler"]], "networkx.algorithms.flow": [[141, "module-networkx.algorithms.flow"]], "construct() (edgecomponentauxgraph class method)": [[142, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.construct"]], "k_edge_components() (edgecomponentauxgraph method)": [[143, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.k_edge_components"]], "k_edge_subgraphs() (edgecomponentauxgraph method)": [[144, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.k_edge_subgraphs"]], "analyze_symmetry() (ismags method)": [[145, "networkx.algorithms.isomorphism.ISMAGS.analyze_symmetry"]], "find_isomorphisms() (ismags method)": [[146, "networkx.algorithms.isomorphism.ISMAGS.find_isomorphisms"]], "is_isomorphic() (ismags method)": [[147, "networkx.algorithms.isomorphism.ISMAGS.is_isomorphic"]], "isomorphisms_iter() (ismags method)": [[148, "networkx.algorithms.isomorphism.ISMAGS.isomorphisms_iter"]], "largest_common_subgraph() (ismags method)": [[149, "networkx.algorithms.isomorphism.ISMAGS.largest_common_subgraph"]], "subgraph_is_isomorphic() (ismags method)": [[150, "networkx.algorithms.isomorphism.ISMAGS.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (ismags method)": [[151, "networkx.algorithms.isomorphism.ISMAGS.subgraph_isomorphisms_iter"]], "add_edge() (planarembedding method)": [[152, "networkx.algorithms.planarity.PlanarEmbedding.add_edge"]], "add_edges_from() (planarembedding method)": [[153, "networkx.algorithms.planarity.PlanarEmbedding.add_edges_from"]], "add_half_edge_ccw() (planarembedding method)": [[154, "networkx.algorithms.planarity.PlanarEmbedding.add_half_edge_ccw"]], "add_half_edge_cw() (planarembedding method)": [[155, "networkx.algorithms.planarity.PlanarEmbedding.add_half_edge_cw"]], "add_half_edge_first() (planarembedding method)": [[156, "networkx.algorithms.planarity.PlanarEmbedding.add_half_edge_first"]], "add_node() (planarembedding method)": [[157, "networkx.algorithms.planarity.PlanarEmbedding.add_node"]], "add_nodes_from() (planarembedding method)": [[158, "networkx.algorithms.planarity.PlanarEmbedding.add_nodes_from"]], "add_weighted_edges_from() (planarembedding method)": [[159, "networkx.algorithms.planarity.PlanarEmbedding.add_weighted_edges_from"]], "adj (planarembedding property)": [[160, "networkx.algorithms.planarity.PlanarEmbedding.adj"]], "adjacency() (planarembedding method)": [[161, "networkx.algorithms.planarity.PlanarEmbedding.adjacency"]], "check_structure() (planarembedding method)": [[162, "networkx.algorithms.planarity.PlanarEmbedding.check_structure"]], "clear() (planarembedding method)": [[163, "networkx.algorithms.planarity.PlanarEmbedding.clear"]], "clear_edges() (planarembedding method)": [[164, "networkx.algorithms.planarity.PlanarEmbedding.clear_edges"]], "connect_components() (planarembedding method)": [[165, "networkx.algorithms.planarity.PlanarEmbedding.connect_components"]], "copy() (planarembedding method)": [[166, "networkx.algorithms.planarity.PlanarEmbedding.copy"]], "degree (planarembedding property)": [[167, "networkx.algorithms.planarity.PlanarEmbedding.degree"]], "edge_subgraph() (planarembedding method)": [[168, "networkx.algorithms.planarity.PlanarEmbedding.edge_subgraph"]], "edges (planarembedding property)": [[169, "networkx.algorithms.planarity.PlanarEmbedding.edges"]], "get_data() (planarembedding method)": [[170, "networkx.algorithms.planarity.PlanarEmbedding.get_data"]], "get_edge_data() (planarembedding method)": [[171, "networkx.algorithms.planarity.PlanarEmbedding.get_edge_data"]], "has_edge() (planarembedding method)": [[172, "networkx.algorithms.planarity.PlanarEmbedding.has_edge"]], "has_node() (planarembedding method)": [[173, "networkx.algorithms.planarity.PlanarEmbedding.has_node"]], "has_predecessor() (planarembedding method)": [[174, "networkx.algorithms.planarity.PlanarEmbedding.has_predecessor"]], "has_successor() (planarembedding method)": [[175, "networkx.algorithms.planarity.PlanarEmbedding.has_successor"]], "in_degree (planarembedding property)": [[176, "networkx.algorithms.planarity.PlanarEmbedding.in_degree"]], "in_edges (planarembedding property)": [[177, "networkx.algorithms.planarity.PlanarEmbedding.in_edges"]], "is_directed() (planarembedding method)": [[178, "networkx.algorithms.planarity.PlanarEmbedding.is_directed"]], "is_multigraph() (planarembedding method)": [[179, "networkx.algorithms.planarity.PlanarEmbedding.is_multigraph"]], "name (planarembedding property)": [[180, "networkx.algorithms.planarity.PlanarEmbedding.name"]], "nbunch_iter() (planarembedding method)": [[181, "networkx.algorithms.planarity.PlanarEmbedding.nbunch_iter"]], "neighbors() (planarembedding method)": [[182, "networkx.algorithms.planarity.PlanarEmbedding.neighbors"]], "neighbors_cw_order() (planarembedding method)": [[183, "networkx.algorithms.planarity.PlanarEmbedding.neighbors_cw_order"]], "next_face_half_edge() (planarembedding method)": [[184, "networkx.algorithms.planarity.PlanarEmbedding.next_face_half_edge"]], "nodes (planarembedding property)": [[185, "networkx.algorithms.planarity.PlanarEmbedding.nodes"]], "number_of_edges() (planarembedding method)": [[186, "networkx.algorithms.planarity.PlanarEmbedding.number_of_edges"]], "number_of_nodes() (planarembedding method)": [[187, "networkx.algorithms.planarity.PlanarEmbedding.number_of_nodes"]], "order() (planarembedding method)": [[188, "networkx.algorithms.planarity.PlanarEmbedding.order"]], "out_degree (planarembedding property)": [[189, "networkx.algorithms.planarity.PlanarEmbedding.out_degree"]], "out_edges (planarembedding property)": [[190, "networkx.algorithms.planarity.PlanarEmbedding.out_edges"]], "pred (planarembedding property)": [[191, "networkx.algorithms.planarity.PlanarEmbedding.pred"]], "predecessors() (planarembedding method)": [[192, "networkx.algorithms.planarity.PlanarEmbedding.predecessors"]], "remove_edge() (planarembedding method)": [[193, "networkx.algorithms.planarity.PlanarEmbedding.remove_edge"]], "remove_edges_from() (planarembedding method)": [[194, "networkx.algorithms.planarity.PlanarEmbedding.remove_edges_from"]], "remove_node() (planarembedding method)": [[195, "networkx.algorithms.planarity.PlanarEmbedding.remove_node"]], "remove_nodes_from() (planarembedding method)": [[196, "networkx.algorithms.planarity.PlanarEmbedding.remove_nodes_from"]], "reverse() (planarembedding method)": [[197, "networkx.algorithms.planarity.PlanarEmbedding.reverse"]], "set_data() (planarembedding method)": [[198, "networkx.algorithms.planarity.PlanarEmbedding.set_data"]], "size() (planarembedding method)": [[199, "networkx.algorithms.planarity.PlanarEmbedding.size"]], "subgraph() (planarembedding method)": [[200, "networkx.algorithms.planarity.PlanarEmbedding.subgraph"]], "succ (planarembedding property)": [[201, "networkx.algorithms.planarity.PlanarEmbedding.succ"]], "successors() (planarembedding method)": [[202, "networkx.algorithms.planarity.PlanarEmbedding.successors"]], "to_directed() (planarembedding method)": [[203, "networkx.algorithms.planarity.PlanarEmbedding.to_directed"]], "to_directed_class() (planarembedding method)": [[204, "networkx.algorithms.planarity.PlanarEmbedding.to_directed_class"]], "to_undirected() (planarembedding method)": [[205, "networkx.algorithms.planarity.PlanarEmbedding.to_undirected"]], "to_undirected_class() (planarembedding method)": [[206, "networkx.algorithms.planarity.PlanarEmbedding.to_undirected_class"]], "traverse_face() (planarembedding method)": [[207, "networkx.algorithms.planarity.PlanarEmbedding.traverse_face"]], "update() (planarembedding method)": [[208, "networkx.algorithms.planarity.PlanarEmbedding.update"]], "find_optimum() (edmonds method)": [[209, "networkx.algorithms.tree.branchings.Edmonds.find_optimum"]], "clique_removal() (in module networkx.algorithms.approximation.clique)": [[210, "networkx.algorithms.approximation.clique.clique_removal"]], "large_clique_size() (in module networkx.algorithms.approximation.clique)": [[211, "networkx.algorithms.approximation.clique.large_clique_size"]], "max_clique() (in module networkx.algorithms.approximation.clique)": [[212, "networkx.algorithms.approximation.clique.max_clique"]], "maximum_independent_set() (in module networkx.algorithms.approximation.clique)": [[213, "networkx.algorithms.approximation.clique.maximum_independent_set"]], "average_clustering() (in module networkx.algorithms.approximation.clustering_coefficient)": [[214, "networkx.algorithms.approximation.clustering_coefficient.average_clustering"]], "all_pairs_node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[215, "networkx.algorithms.approximation.connectivity.all_pairs_node_connectivity"]], "local_node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[216, "networkx.algorithms.approximation.connectivity.local_node_connectivity"]], "node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[217, "networkx.algorithms.approximation.connectivity.node_connectivity"]], "diameter() (in module networkx.algorithms.approximation.distance_measures)": [[218, "networkx.algorithms.approximation.distance_measures.diameter"]], "min_edge_dominating_set() (in module networkx.algorithms.approximation.dominating_set)": [[219, "networkx.algorithms.approximation.dominating_set.min_edge_dominating_set"]], "min_weighted_dominating_set() (in module networkx.algorithms.approximation.dominating_set)": [[220, "networkx.algorithms.approximation.dominating_set.min_weighted_dominating_set"]], "k_components() (in module networkx.algorithms.approximation.kcomponents)": [[221, "networkx.algorithms.approximation.kcomponents.k_components"]], "min_maximal_matching() (in module networkx.algorithms.approximation.matching)": [[222, "networkx.algorithms.approximation.matching.min_maximal_matching"]], "one_exchange() (in module networkx.algorithms.approximation.maxcut)": [[223, "networkx.algorithms.approximation.maxcut.one_exchange"]], "randomized_partitioning() (in module networkx.algorithms.approximation.maxcut)": [[224, "networkx.algorithms.approximation.maxcut.randomized_partitioning"]], "ramsey_r2() (in module networkx.algorithms.approximation.ramsey)": [[225, "networkx.algorithms.approximation.ramsey.ramsey_R2"]], "metric_closure() (in module networkx.algorithms.approximation.steinertree)": [[226, "networkx.algorithms.approximation.steinertree.metric_closure"]], "steiner_tree() (in module networkx.algorithms.approximation.steinertree)": [[227, "networkx.algorithms.approximation.steinertree.steiner_tree"]], "asadpour_atsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[228, "networkx.algorithms.approximation.traveling_salesman.asadpour_atsp"]], "christofides() (in module networkx.algorithms.approximation.traveling_salesman)": [[229, "networkx.algorithms.approximation.traveling_salesman.christofides"]], "greedy_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[230, "networkx.algorithms.approximation.traveling_salesman.greedy_tsp"]], "simulated_annealing_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[231, "networkx.algorithms.approximation.traveling_salesman.simulated_annealing_tsp"]], "threshold_accepting_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[232, "networkx.algorithms.approximation.traveling_salesman.threshold_accepting_tsp"]], "traveling_salesman_problem() (in module networkx.algorithms.approximation.traveling_salesman)": [[233, "networkx.algorithms.approximation.traveling_salesman.traveling_salesman_problem"]], "treewidth_min_degree() (in module networkx.algorithms.approximation.treewidth)": [[234, "networkx.algorithms.approximation.treewidth.treewidth_min_degree"]], "treewidth_min_fill_in() (in module networkx.algorithms.approximation.treewidth)": [[235, "networkx.algorithms.approximation.treewidth.treewidth_min_fill_in"]], "min_weighted_vertex_cover() (in module networkx.algorithms.approximation.vertex_cover)": [[236, "networkx.algorithms.approximation.vertex_cover.min_weighted_vertex_cover"]], "attribute_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[237, "networkx.algorithms.assortativity.attribute_assortativity_coefficient"]], "attribute_mixing_dict() (in module networkx.algorithms.assortativity)": [[238, "networkx.algorithms.assortativity.attribute_mixing_dict"]], "attribute_mixing_matrix() (in module networkx.algorithms.assortativity)": [[239, "networkx.algorithms.assortativity.attribute_mixing_matrix"]], "average_degree_connectivity() (in module networkx.algorithms.assortativity)": [[240, "networkx.algorithms.assortativity.average_degree_connectivity"]], "average_neighbor_degree() (in module networkx.algorithms.assortativity)": [[241, "networkx.algorithms.assortativity.average_neighbor_degree"]], "degree_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[242, "networkx.algorithms.assortativity.degree_assortativity_coefficient"]], "degree_mixing_dict() (in module networkx.algorithms.assortativity)": [[243, "networkx.algorithms.assortativity.degree_mixing_dict"]], "degree_mixing_matrix() (in module networkx.algorithms.assortativity)": [[244, "networkx.algorithms.assortativity.degree_mixing_matrix"]], "degree_pearson_correlation_coefficient() (in module networkx.algorithms.assortativity)": [[245, "networkx.algorithms.assortativity.degree_pearson_correlation_coefficient"]], "mixing_dict() (in module networkx.algorithms.assortativity)": [[246, "networkx.algorithms.assortativity.mixing_dict"]], "node_attribute_xy() (in module networkx.algorithms.assortativity)": [[247, "networkx.algorithms.assortativity.node_attribute_xy"]], "node_degree_xy() (in module networkx.algorithms.assortativity)": [[248, "networkx.algorithms.assortativity.node_degree_xy"]], "numeric_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[249, "networkx.algorithms.assortativity.numeric_assortativity_coefficient"]], "find_asteroidal_triple() (in module networkx.algorithms.asteroidal)": [[250, "networkx.algorithms.asteroidal.find_asteroidal_triple"]], "is_at_free() (in module networkx.algorithms.asteroidal)": [[251, "networkx.algorithms.asteroidal.is_at_free"]], "color() (in module networkx.algorithms.bipartite.basic)": [[252, "networkx.algorithms.bipartite.basic.color"]], "degrees() (in module networkx.algorithms.bipartite.basic)": [[253, "networkx.algorithms.bipartite.basic.degrees"]], "density() (in module networkx.algorithms.bipartite.basic)": [[254, "networkx.algorithms.bipartite.basic.density"]], "is_bipartite() (in module networkx.algorithms.bipartite.basic)": [[255, "networkx.algorithms.bipartite.basic.is_bipartite"]], "is_bipartite_node_set() (in module networkx.algorithms.bipartite.basic)": [[256, "networkx.algorithms.bipartite.basic.is_bipartite_node_set"]], "sets() (in module networkx.algorithms.bipartite.basic)": [[257, "networkx.algorithms.bipartite.basic.sets"]], "betweenness_centrality() (in module networkx.algorithms.bipartite.centrality)": [[258, "networkx.algorithms.bipartite.centrality.betweenness_centrality"]], "closeness_centrality() (in module networkx.algorithms.bipartite.centrality)": [[259, "networkx.algorithms.bipartite.centrality.closeness_centrality"]], "degree_centrality() (in module networkx.algorithms.bipartite.centrality)": [[260, "networkx.algorithms.bipartite.centrality.degree_centrality"]], "average_clustering() (in module networkx.algorithms.bipartite.cluster)": [[261, "networkx.algorithms.bipartite.cluster.average_clustering"]], "clustering() (in module networkx.algorithms.bipartite.cluster)": [[262, "networkx.algorithms.bipartite.cluster.clustering"]], "latapy_clustering() (in module networkx.algorithms.bipartite.cluster)": [[263, "networkx.algorithms.bipartite.cluster.latapy_clustering"]], "robins_alexander_clustering() (in module networkx.algorithms.bipartite.cluster)": [[264, "networkx.algorithms.bipartite.cluster.robins_alexander_clustering"]], "min_edge_cover() (in module networkx.algorithms.bipartite.covering)": [[265, "networkx.algorithms.bipartite.covering.min_edge_cover"]], "generate_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[266, "networkx.algorithms.bipartite.edgelist.generate_edgelist"]], "parse_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[267, "networkx.algorithms.bipartite.edgelist.parse_edgelist"]], "read_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[268, "networkx.algorithms.bipartite.edgelist.read_edgelist"]], "write_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[269, "networkx.algorithms.bipartite.edgelist.write_edgelist"]], "alternating_havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[270, "networkx.algorithms.bipartite.generators.alternating_havel_hakimi_graph"]], "complete_bipartite_graph() (in module networkx.algorithms.bipartite.generators)": [[271, "networkx.algorithms.bipartite.generators.complete_bipartite_graph"]], "configuration_model() (in module networkx.algorithms.bipartite.generators)": [[272, "networkx.algorithms.bipartite.generators.configuration_model"]], "gnmk_random_graph() (in module networkx.algorithms.bipartite.generators)": [[273, "networkx.algorithms.bipartite.generators.gnmk_random_graph"]], "havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[274, "networkx.algorithms.bipartite.generators.havel_hakimi_graph"]], "preferential_attachment_graph() (in module networkx.algorithms.bipartite.generators)": [[275, "networkx.algorithms.bipartite.generators.preferential_attachment_graph"]], "random_graph() (in module networkx.algorithms.bipartite.generators)": [[276, "networkx.algorithms.bipartite.generators.random_graph"]], "reverse_havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[277, "networkx.algorithms.bipartite.generators.reverse_havel_hakimi_graph"]], "eppstein_matching() (in module networkx.algorithms.bipartite.matching)": [[278, "networkx.algorithms.bipartite.matching.eppstein_matching"]], "hopcroft_karp_matching() (in module networkx.algorithms.bipartite.matching)": [[279, "networkx.algorithms.bipartite.matching.hopcroft_karp_matching"]], "maximum_matching() (in module networkx.algorithms.bipartite.matching)": [[280, "networkx.algorithms.bipartite.matching.maximum_matching"]], "minimum_weight_full_matching() (in module networkx.algorithms.bipartite.matching)": [[281, "networkx.algorithms.bipartite.matching.minimum_weight_full_matching"]], "to_vertex_cover() (in module networkx.algorithms.bipartite.matching)": [[282, "networkx.algorithms.bipartite.matching.to_vertex_cover"]], "biadjacency_matrix() (in module networkx.algorithms.bipartite.matrix)": [[283, "networkx.algorithms.bipartite.matrix.biadjacency_matrix"]], "from_biadjacency_matrix() (in module networkx.algorithms.bipartite.matrix)": [[284, "networkx.algorithms.bipartite.matrix.from_biadjacency_matrix"]], "collaboration_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[285, "networkx.algorithms.bipartite.projection.collaboration_weighted_projected_graph"]], "generic_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[286, "networkx.algorithms.bipartite.projection.generic_weighted_projected_graph"]], "overlap_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[287, "networkx.algorithms.bipartite.projection.overlap_weighted_projected_graph"]], "projected_graph() (in module networkx.algorithms.bipartite.projection)": [[288, "networkx.algorithms.bipartite.projection.projected_graph"]], "weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[289, "networkx.algorithms.bipartite.projection.weighted_projected_graph"]], "node_redundancy() (in module networkx.algorithms.bipartite.redundancy)": [[290, "networkx.algorithms.bipartite.redundancy.node_redundancy"]], "spectral_bipartivity() (in module networkx.algorithms.bipartite.spectral)": [[291, "networkx.algorithms.bipartite.spectral.spectral_bipartivity"]], "edge_boundary() (in module networkx.algorithms.boundary)": [[292, "networkx.algorithms.boundary.edge_boundary"]], "node_boundary() (in module networkx.algorithms.boundary)": [[293, "networkx.algorithms.boundary.node_boundary"]], "bridges() (in module networkx.algorithms.bridges)": [[294, "networkx.algorithms.bridges.bridges"]], "has_bridges() (in module networkx.algorithms.bridges)": [[295, "networkx.algorithms.bridges.has_bridges"]], "local_bridges() (in module networkx.algorithms.bridges)": [[296, "networkx.algorithms.bridges.local_bridges"]], "approximate_current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[297, "networkx.algorithms.centrality.approximate_current_flow_betweenness_centrality"]], "betweenness_centrality() (in module networkx.algorithms.centrality)": [[298, "networkx.algorithms.centrality.betweenness_centrality"]], "betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[299, "networkx.algorithms.centrality.betweenness_centrality_subset"]], "closeness_centrality() (in module networkx.algorithms.centrality)": [[300, "networkx.algorithms.centrality.closeness_centrality"]], "communicability_betweenness_centrality() (in module networkx.algorithms.centrality)": [[301, "networkx.algorithms.centrality.communicability_betweenness_centrality"]], "current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[302, "networkx.algorithms.centrality.current_flow_betweenness_centrality"]], "current_flow_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[303, "networkx.algorithms.centrality.current_flow_betweenness_centrality_subset"]], "current_flow_closeness_centrality() (in module networkx.algorithms.centrality)": [[304, "networkx.algorithms.centrality.current_flow_closeness_centrality"]], "degree_centrality() (in module networkx.algorithms.centrality)": [[305, "networkx.algorithms.centrality.degree_centrality"]], "dispersion() (in module networkx.algorithms.centrality)": [[306, "networkx.algorithms.centrality.dispersion"]], "edge_betweenness_centrality() (in module networkx.algorithms.centrality)": [[307, "networkx.algorithms.centrality.edge_betweenness_centrality"]], "edge_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[308, "networkx.algorithms.centrality.edge_betweenness_centrality_subset"]], "edge_current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[309, "networkx.algorithms.centrality.edge_current_flow_betweenness_centrality"]], "edge_current_flow_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[310, "networkx.algorithms.centrality.edge_current_flow_betweenness_centrality_subset"]], "edge_load_centrality() (in module networkx.algorithms.centrality)": [[311, "networkx.algorithms.centrality.edge_load_centrality"]], "eigenvector_centrality() (in module networkx.algorithms.centrality)": [[312, "networkx.algorithms.centrality.eigenvector_centrality"]], "eigenvector_centrality_numpy() (in module networkx.algorithms.centrality)": [[313, "networkx.algorithms.centrality.eigenvector_centrality_numpy"]], "estrada_index() (in module networkx.algorithms.centrality)": [[314, "networkx.algorithms.centrality.estrada_index"]], "global_reaching_centrality() (in module networkx.algorithms.centrality)": [[315, "networkx.algorithms.centrality.global_reaching_centrality"]], "group_betweenness_centrality() (in module networkx.algorithms.centrality)": [[316, "networkx.algorithms.centrality.group_betweenness_centrality"]], "group_closeness_centrality() (in module networkx.algorithms.centrality)": [[317, "networkx.algorithms.centrality.group_closeness_centrality"]], "group_degree_centrality() (in module networkx.algorithms.centrality)": [[318, "networkx.algorithms.centrality.group_degree_centrality"]], "group_in_degree_centrality() (in module networkx.algorithms.centrality)": [[319, "networkx.algorithms.centrality.group_in_degree_centrality"]], "group_out_degree_centrality() (in module networkx.algorithms.centrality)": [[320, "networkx.algorithms.centrality.group_out_degree_centrality"]], "harmonic_centrality() (in module networkx.algorithms.centrality)": [[321, "networkx.algorithms.centrality.harmonic_centrality"]], "in_degree_centrality() (in module networkx.algorithms.centrality)": [[322, "networkx.algorithms.centrality.in_degree_centrality"]], "incremental_closeness_centrality() (in module networkx.algorithms.centrality)": [[323, "networkx.algorithms.centrality.incremental_closeness_centrality"]], "information_centrality() (in module networkx.algorithms.centrality)": [[324, "networkx.algorithms.centrality.information_centrality"]], "katz_centrality() (in module networkx.algorithms.centrality)": [[325, "networkx.algorithms.centrality.katz_centrality"]], "katz_centrality_numpy() (in module networkx.algorithms.centrality)": [[326, "networkx.algorithms.centrality.katz_centrality_numpy"]], "laplacian_centrality() (in module networkx.algorithms.centrality)": [[327, "networkx.algorithms.centrality.laplacian_centrality"]], "load_centrality() (in module networkx.algorithms.centrality)": [[328, "networkx.algorithms.centrality.load_centrality"]], "local_reaching_centrality() (in module networkx.algorithms.centrality)": [[329, "networkx.algorithms.centrality.local_reaching_centrality"]], "out_degree_centrality() (in module networkx.algorithms.centrality)": [[330, "networkx.algorithms.centrality.out_degree_centrality"]], "percolation_centrality() (in module networkx.algorithms.centrality)": [[331, "networkx.algorithms.centrality.percolation_centrality"]], "prominent_group() (in module networkx.algorithms.centrality)": [[332, "networkx.algorithms.centrality.prominent_group"]], "second_order_centrality() (in module networkx.algorithms.centrality)": [[333, "networkx.algorithms.centrality.second_order_centrality"]], "subgraph_centrality() (in module networkx.algorithms.centrality)": [[334, "networkx.algorithms.centrality.subgraph_centrality"]], "subgraph_centrality_exp() (in module networkx.algorithms.centrality)": [[335, "networkx.algorithms.centrality.subgraph_centrality_exp"]], "trophic_differences() (in module networkx.algorithms.centrality)": [[336, "networkx.algorithms.centrality.trophic_differences"]], "trophic_incoherence_parameter() (in module networkx.algorithms.centrality)": [[337, "networkx.algorithms.centrality.trophic_incoherence_parameter"]], "trophic_levels() (in module networkx.algorithms.centrality)": [[338, "networkx.algorithms.centrality.trophic_levels"]], "voterank() (in module networkx.algorithms.centrality)": [[339, "networkx.algorithms.centrality.voterank"]], "chain_decomposition() (in module networkx.algorithms.chains)": [[340, "networkx.algorithms.chains.chain_decomposition"]], "chordal_graph_cliques() (in module networkx.algorithms.chordal)": [[341, "networkx.algorithms.chordal.chordal_graph_cliques"]], "chordal_graph_treewidth() (in module networkx.algorithms.chordal)": [[342, "networkx.algorithms.chordal.chordal_graph_treewidth"]], "complete_to_chordal_graph() (in module networkx.algorithms.chordal)": [[343, "networkx.algorithms.chordal.complete_to_chordal_graph"]], "find_induced_nodes() (in module networkx.algorithms.chordal)": [[344, "networkx.algorithms.chordal.find_induced_nodes"]], "is_chordal() (in module networkx.algorithms.chordal)": [[345, "networkx.algorithms.chordal.is_chordal"]], "cliques_containing_node() (in module networkx.algorithms.clique)": [[346, "networkx.algorithms.clique.cliques_containing_node"]], "enumerate_all_cliques() (in module networkx.algorithms.clique)": [[347, "networkx.algorithms.clique.enumerate_all_cliques"]], "find_cliques() (in module networkx.algorithms.clique)": [[348, "networkx.algorithms.clique.find_cliques"]], "find_cliques_recursive() (in module networkx.algorithms.clique)": [[349, "networkx.algorithms.clique.find_cliques_recursive"]], "graph_clique_number() (in module networkx.algorithms.clique)": [[350, "networkx.algorithms.clique.graph_clique_number"]], "graph_number_of_cliques() (in module networkx.algorithms.clique)": [[351, "networkx.algorithms.clique.graph_number_of_cliques"]], "make_clique_bipartite() (in module networkx.algorithms.clique)": [[352, "networkx.algorithms.clique.make_clique_bipartite"]], "make_max_clique_graph() (in module networkx.algorithms.clique)": [[353, "networkx.algorithms.clique.make_max_clique_graph"]], "max_weight_clique() (in module networkx.algorithms.clique)": [[354, "networkx.algorithms.clique.max_weight_clique"]], "node_clique_number() (in module networkx.algorithms.clique)": [[355, "networkx.algorithms.clique.node_clique_number"]], "number_of_cliques() (in module networkx.algorithms.clique)": [[356, "networkx.algorithms.clique.number_of_cliques"]], "average_clustering() (in module networkx.algorithms.cluster)": [[357, "networkx.algorithms.cluster.average_clustering"]], "clustering() (in module networkx.algorithms.cluster)": [[358, "networkx.algorithms.cluster.clustering"]], "generalized_degree() (in module networkx.algorithms.cluster)": [[359, "networkx.algorithms.cluster.generalized_degree"]], "square_clustering() (in module networkx.algorithms.cluster)": [[360, "networkx.algorithms.cluster.square_clustering"]], "transitivity() (in module networkx.algorithms.cluster)": [[361, "networkx.algorithms.cluster.transitivity"]], "triangles() (in module networkx.algorithms.cluster)": [[362, "networkx.algorithms.cluster.triangles"]], "equitable_color() (in module networkx.algorithms.coloring)": [[363, "networkx.algorithms.coloring.equitable_color"]], "greedy_color() (in module networkx.algorithms.coloring)": [[364, "networkx.algorithms.coloring.greedy_color"]], "strategy_connected_sequential() (in module networkx.algorithms.coloring)": [[365, "networkx.algorithms.coloring.strategy_connected_sequential"]], "strategy_connected_sequential_bfs() (in module networkx.algorithms.coloring)": [[366, "networkx.algorithms.coloring.strategy_connected_sequential_bfs"]], "strategy_connected_sequential_dfs() (in module networkx.algorithms.coloring)": [[367, "networkx.algorithms.coloring.strategy_connected_sequential_dfs"]], "strategy_independent_set() (in module networkx.algorithms.coloring)": [[368, "networkx.algorithms.coloring.strategy_independent_set"]], "strategy_largest_first() (in module networkx.algorithms.coloring)": [[369, "networkx.algorithms.coloring.strategy_largest_first"]], "strategy_random_sequential() (in module networkx.algorithms.coloring)": [[370, "networkx.algorithms.coloring.strategy_random_sequential"]], "strategy_saturation_largest_first() (in module networkx.algorithms.coloring)": [[371, "networkx.algorithms.coloring.strategy_saturation_largest_first"]], "strategy_smallest_last() (in module networkx.algorithms.coloring)": [[372, "networkx.algorithms.coloring.strategy_smallest_last"]], "communicability() (in module networkx.algorithms.communicability_alg)": [[373, "networkx.algorithms.communicability_alg.communicability"]], "communicability_exp() (in module networkx.algorithms.communicability_alg)": [[374, "networkx.algorithms.communicability_alg.communicability_exp"]], "asyn_fluidc() (in module networkx.algorithms.community.asyn_fluid)": [[375, "networkx.algorithms.community.asyn_fluid.asyn_fluidc"]], "girvan_newman() (in module networkx.algorithms.community.centrality)": [[376, "networkx.algorithms.community.centrality.girvan_newman"]], "is_partition() (in module networkx.algorithms.community.community_utils)": [[377, "networkx.algorithms.community.community_utils.is_partition"]], "k_clique_communities() (in module networkx.algorithms.community.kclique)": [[378, "networkx.algorithms.community.kclique.k_clique_communities"]], "kernighan_lin_bisection() (in module networkx.algorithms.community.kernighan_lin)": [[379, "networkx.algorithms.community.kernighan_lin.kernighan_lin_bisection"]], "asyn_lpa_communities() (in module networkx.algorithms.community.label_propagation)": [[380, "networkx.algorithms.community.label_propagation.asyn_lpa_communities"]], "label_propagation_communities() (in module networkx.algorithms.community.label_propagation)": [[381, "networkx.algorithms.community.label_propagation.label_propagation_communities"]], "louvain_communities() (in module networkx.algorithms.community.louvain)": [[382, "networkx.algorithms.community.louvain.louvain_communities"]], "louvain_partitions() (in module networkx.algorithms.community.louvain)": [[383, "networkx.algorithms.community.louvain.louvain_partitions"]], "lukes_partitioning() (in module networkx.algorithms.community.lukes)": [[384, "networkx.algorithms.community.lukes.lukes_partitioning"]], "greedy_modularity_communities() (in module networkx.algorithms.community.modularity_max)": [[385, "networkx.algorithms.community.modularity_max.greedy_modularity_communities"]], "naive_greedy_modularity_communities() (in module networkx.algorithms.community.modularity_max)": [[386, "networkx.algorithms.community.modularity_max.naive_greedy_modularity_communities"]], "modularity() (in module networkx.algorithms.community.quality)": [[387, "networkx.algorithms.community.quality.modularity"]], "partition_quality() (in module networkx.algorithms.community.quality)": [[388, "networkx.algorithms.community.quality.partition_quality"]], "articulation_points() (in module networkx.algorithms.components)": [[389, "networkx.algorithms.components.articulation_points"]], "attracting_components() (in module networkx.algorithms.components)": [[390, "networkx.algorithms.components.attracting_components"]], "biconnected_component_edges() (in module networkx.algorithms.components)": [[391, "networkx.algorithms.components.biconnected_component_edges"]], "biconnected_components() (in module networkx.algorithms.components)": [[392, "networkx.algorithms.components.biconnected_components"]], "condensation() (in module networkx.algorithms.components)": [[393, "networkx.algorithms.components.condensation"]], "connected_components() (in module networkx.algorithms.components)": [[394, "networkx.algorithms.components.connected_components"]], "is_attracting_component() (in module networkx.algorithms.components)": [[395, "networkx.algorithms.components.is_attracting_component"]], "is_biconnected() (in module networkx.algorithms.components)": [[396, "networkx.algorithms.components.is_biconnected"]], "is_connected() (in module networkx.algorithms.components)": [[397, "networkx.algorithms.components.is_connected"]], "is_semiconnected() (in module networkx.algorithms.components)": [[398, "networkx.algorithms.components.is_semiconnected"]], "is_strongly_connected() (in module networkx.algorithms.components)": [[399, "networkx.algorithms.components.is_strongly_connected"]], "is_weakly_connected() (in module networkx.algorithms.components)": [[400, "networkx.algorithms.components.is_weakly_connected"]], "kosaraju_strongly_connected_components() (in module networkx.algorithms.components)": [[401, "networkx.algorithms.components.kosaraju_strongly_connected_components"]], "node_connected_component() (in module networkx.algorithms.components)": [[402, "networkx.algorithms.components.node_connected_component"]], "number_attracting_components() (in module networkx.algorithms.components)": [[403, "networkx.algorithms.components.number_attracting_components"]], "number_connected_components() (in module networkx.algorithms.components)": [[404, "networkx.algorithms.components.number_connected_components"]], "number_strongly_connected_components() (in module networkx.algorithms.components)": [[405, "networkx.algorithms.components.number_strongly_connected_components"]], "number_weakly_connected_components() (in module networkx.algorithms.components)": [[406, "networkx.algorithms.components.number_weakly_connected_components"]], "strongly_connected_components() (in module networkx.algorithms.components)": [[407, "networkx.algorithms.components.strongly_connected_components"]], "strongly_connected_components_recursive() (in module networkx.algorithms.components)": [[408, "networkx.algorithms.components.strongly_connected_components_recursive"]], "weakly_connected_components() (in module networkx.algorithms.components)": [[409, "networkx.algorithms.components.weakly_connected_components"]], "all_pairs_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[410, "networkx.algorithms.connectivity.connectivity.all_pairs_node_connectivity"]], "average_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[411, "networkx.algorithms.connectivity.connectivity.average_node_connectivity"]], "edge_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[412, "networkx.algorithms.connectivity.connectivity.edge_connectivity"]], "local_edge_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[413, "networkx.algorithms.connectivity.connectivity.local_edge_connectivity"]], "local_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[414, "networkx.algorithms.connectivity.connectivity.local_node_connectivity"]], "node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[415, "networkx.algorithms.connectivity.connectivity.node_connectivity"]], "minimum_edge_cut() (in module networkx.algorithms.connectivity.cuts)": [[416, "networkx.algorithms.connectivity.cuts.minimum_edge_cut"]], "minimum_node_cut() (in module networkx.algorithms.connectivity.cuts)": [[417, "networkx.algorithms.connectivity.cuts.minimum_node_cut"]], "minimum_st_edge_cut() (in module networkx.algorithms.connectivity.cuts)": [[418, "networkx.algorithms.connectivity.cuts.minimum_st_edge_cut"]], "minimum_st_node_cut() (in module networkx.algorithms.connectivity.cuts)": [[419, "networkx.algorithms.connectivity.cuts.minimum_st_node_cut"]], "edge_disjoint_paths() (in module networkx.algorithms.connectivity.disjoint_paths)": [[420, "networkx.algorithms.connectivity.disjoint_paths.edge_disjoint_paths"]], "node_disjoint_paths() (in module networkx.algorithms.connectivity.disjoint_paths)": [[421, "networkx.algorithms.connectivity.disjoint_paths.node_disjoint_paths"]], "is_k_edge_connected() (in module networkx.algorithms.connectivity.edge_augmentation)": [[422, "networkx.algorithms.connectivity.edge_augmentation.is_k_edge_connected"]], "is_locally_k_edge_connected() (in module networkx.algorithms.connectivity.edge_augmentation)": [[423, "networkx.algorithms.connectivity.edge_augmentation.is_locally_k_edge_connected"]], "k_edge_augmentation() (in module networkx.algorithms.connectivity.edge_augmentation)": [[424, "networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation"]], "edgecomponentauxgraph (class in networkx.algorithms.connectivity.edge_kcomponents)": [[425, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph"]], "__init__() (edgecomponentauxgraph method)": [[425, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.__init__"]], "bridge_components() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[426, "networkx.algorithms.connectivity.edge_kcomponents.bridge_components"]], "k_edge_components() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[427, "networkx.algorithms.connectivity.edge_kcomponents.k_edge_components"]], "k_edge_subgraphs() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[428, "networkx.algorithms.connectivity.edge_kcomponents.k_edge_subgraphs"]], "k_components() (in module networkx.algorithms.connectivity.kcomponents)": [[429, "networkx.algorithms.connectivity.kcomponents.k_components"]], "all_node_cuts() (in module networkx.algorithms.connectivity.kcutsets)": [[430, "networkx.algorithms.connectivity.kcutsets.all_node_cuts"]], "stoer_wagner() (in module networkx.algorithms.connectivity.stoerwagner)": [[431, "networkx.algorithms.connectivity.stoerwagner.stoer_wagner"]], "build_auxiliary_edge_connectivity() (in module networkx.algorithms.connectivity.utils)": [[432, "networkx.algorithms.connectivity.utils.build_auxiliary_edge_connectivity"]], "build_auxiliary_node_connectivity() (in module networkx.algorithms.connectivity.utils)": [[433, "networkx.algorithms.connectivity.utils.build_auxiliary_node_connectivity"]], "core_number() (in module networkx.algorithms.core)": [[434, "networkx.algorithms.core.core_number"]], "k_core() (in module networkx.algorithms.core)": [[435, "networkx.algorithms.core.k_core"]], "k_corona() (in module networkx.algorithms.core)": [[436, "networkx.algorithms.core.k_corona"]], "k_crust() (in module networkx.algorithms.core)": [[437, "networkx.algorithms.core.k_crust"]], "k_shell() (in module networkx.algorithms.core)": [[438, "networkx.algorithms.core.k_shell"]], "k_truss() (in module networkx.algorithms.core)": [[439, "networkx.algorithms.core.k_truss"]], "onion_layers() (in module networkx.algorithms.core)": [[440, "networkx.algorithms.core.onion_layers"]], "is_edge_cover() (in module networkx.algorithms.covering)": [[441, "networkx.algorithms.covering.is_edge_cover"]], "min_edge_cover() (in module networkx.algorithms.covering)": [[442, "networkx.algorithms.covering.min_edge_cover"]], "boundary_expansion() (in module networkx.algorithms.cuts)": [[443, "networkx.algorithms.cuts.boundary_expansion"]], "conductance() (in module networkx.algorithms.cuts)": [[444, "networkx.algorithms.cuts.conductance"]], "cut_size() (in module networkx.algorithms.cuts)": [[445, "networkx.algorithms.cuts.cut_size"]], "edge_expansion() (in module networkx.algorithms.cuts)": [[446, "networkx.algorithms.cuts.edge_expansion"]], "mixing_expansion() (in module networkx.algorithms.cuts)": [[447, "networkx.algorithms.cuts.mixing_expansion"]], "node_expansion() (in module networkx.algorithms.cuts)": [[448, "networkx.algorithms.cuts.node_expansion"]], "normalized_cut_size() (in module networkx.algorithms.cuts)": [[449, "networkx.algorithms.cuts.normalized_cut_size"]], "volume() (in module networkx.algorithms.cuts)": [[450, "networkx.algorithms.cuts.volume"]], "cycle_basis() (in module networkx.algorithms.cycles)": [[451, "networkx.algorithms.cycles.cycle_basis"]], "find_cycle() (in module networkx.algorithms.cycles)": [[452, "networkx.algorithms.cycles.find_cycle"]], "minimum_cycle_basis() (in module networkx.algorithms.cycles)": [[453, "networkx.algorithms.cycles.minimum_cycle_basis"]], "recursive_simple_cycles() (in module networkx.algorithms.cycles)": [[454, "networkx.algorithms.cycles.recursive_simple_cycles"]], "simple_cycles() (in module networkx.algorithms.cycles)": [[455, "networkx.algorithms.cycles.simple_cycles"]], "d_separated() (in module networkx.algorithms.d_separation)": [[456, "networkx.algorithms.d_separation.d_separated"]], "all_topological_sorts() (in module networkx.algorithms.dag)": [[457, "networkx.algorithms.dag.all_topological_sorts"]], "ancestors() (in module networkx.algorithms.dag)": [[458, "networkx.algorithms.dag.ancestors"]], "antichains() (in module networkx.algorithms.dag)": [[459, "networkx.algorithms.dag.antichains"]], "dag_longest_path() (in module networkx.algorithms.dag)": [[460, "networkx.algorithms.dag.dag_longest_path"]], "dag_longest_path_length() (in module networkx.algorithms.dag)": [[461, "networkx.algorithms.dag.dag_longest_path_length"]], "dag_to_branching() (in module networkx.algorithms.dag)": [[462, "networkx.algorithms.dag.dag_to_branching"]], "descendants() (in module networkx.algorithms.dag)": [[463, "networkx.algorithms.dag.descendants"]], "is_aperiodic() (in module networkx.algorithms.dag)": [[464, "networkx.algorithms.dag.is_aperiodic"]], "is_directed_acyclic_graph() (in module networkx.algorithms.dag)": [[465, "networkx.algorithms.dag.is_directed_acyclic_graph"]], "lexicographical_topological_sort() (in module networkx.algorithms.dag)": [[466, "networkx.algorithms.dag.lexicographical_topological_sort"]], "topological_generations() (in module networkx.algorithms.dag)": [[467, "networkx.algorithms.dag.topological_generations"]], "topological_sort() (in module networkx.algorithms.dag)": [[468, "networkx.algorithms.dag.topological_sort"]], "transitive_closure() (in module networkx.algorithms.dag)": [[469, "networkx.algorithms.dag.transitive_closure"]], "transitive_closure_dag() (in module networkx.algorithms.dag)": [[470, "networkx.algorithms.dag.transitive_closure_dag"]], "transitive_reduction() (in module networkx.algorithms.dag)": [[471, "networkx.algorithms.dag.transitive_reduction"]], "barycenter() (in module networkx.algorithms.distance_measures)": [[472, "networkx.algorithms.distance_measures.barycenter"]], "center() (in module networkx.algorithms.distance_measures)": [[473, "networkx.algorithms.distance_measures.center"]], "diameter() (in module networkx.algorithms.distance_measures)": [[474, "networkx.algorithms.distance_measures.diameter"]], "eccentricity() (in module networkx.algorithms.distance_measures)": [[475, "networkx.algorithms.distance_measures.eccentricity"]], "periphery() (in module networkx.algorithms.distance_measures)": [[476, "networkx.algorithms.distance_measures.periphery"]], "radius() (in module networkx.algorithms.distance_measures)": [[477, "networkx.algorithms.distance_measures.radius"]], "resistance_distance() (in module networkx.algorithms.distance_measures)": [[478, "networkx.algorithms.distance_measures.resistance_distance"]], "global_parameters() (in module networkx.algorithms.distance_regular)": [[479, "networkx.algorithms.distance_regular.global_parameters"]], "intersection_array() (in module networkx.algorithms.distance_regular)": [[480, "networkx.algorithms.distance_regular.intersection_array"]], "is_distance_regular() (in module networkx.algorithms.distance_regular)": [[481, "networkx.algorithms.distance_regular.is_distance_regular"]], "is_strongly_regular() (in module networkx.algorithms.distance_regular)": [[482, "networkx.algorithms.distance_regular.is_strongly_regular"]], "dominance_frontiers() (in module networkx.algorithms.dominance)": [[483, "networkx.algorithms.dominance.dominance_frontiers"]], "immediate_dominators() (in module networkx.algorithms.dominance)": [[484, "networkx.algorithms.dominance.immediate_dominators"]], "dominating_set() (in module networkx.algorithms.dominating)": [[485, "networkx.algorithms.dominating.dominating_set"]], "is_dominating_set() (in module networkx.algorithms.dominating)": [[486, "networkx.algorithms.dominating.is_dominating_set"]], "efficiency() (in module networkx.algorithms.efficiency_measures)": [[487, "networkx.algorithms.efficiency_measures.efficiency"]], "global_efficiency() (in module networkx.algorithms.efficiency_measures)": [[488, "networkx.algorithms.efficiency_measures.global_efficiency"]], "local_efficiency() (in module networkx.algorithms.efficiency_measures)": [[489, "networkx.algorithms.efficiency_measures.local_efficiency"]], "eulerian_circuit() (in module networkx.algorithms.euler)": [[490, "networkx.algorithms.euler.eulerian_circuit"]], "eulerian_path() (in module networkx.algorithms.euler)": [[491, "networkx.algorithms.euler.eulerian_path"]], "eulerize() (in module networkx.algorithms.euler)": [[492, "networkx.algorithms.euler.eulerize"]], "has_eulerian_path() (in module networkx.algorithms.euler)": [[493, "networkx.algorithms.euler.has_eulerian_path"]], "is_eulerian() (in module networkx.algorithms.euler)": [[494, "networkx.algorithms.euler.is_eulerian"]], "is_semieulerian() (in module networkx.algorithms.euler)": [[495, "networkx.algorithms.euler.is_semieulerian"]], "boykov_kolmogorov() (in module networkx.algorithms.flow)": [[496, "networkx.algorithms.flow.boykov_kolmogorov"]], "build_residual_network() (in module networkx.algorithms.flow)": [[497, "networkx.algorithms.flow.build_residual_network"]], "capacity_scaling() (in module networkx.algorithms.flow)": [[498, "networkx.algorithms.flow.capacity_scaling"]], "cost_of_flow() (in module networkx.algorithms.flow)": [[499, "networkx.algorithms.flow.cost_of_flow"]], "dinitz() (in module networkx.algorithms.flow)": [[500, "networkx.algorithms.flow.dinitz"]], "edmonds_karp() (in module networkx.algorithms.flow)": [[501, "networkx.algorithms.flow.edmonds_karp"]], "gomory_hu_tree() (in module networkx.algorithms.flow)": [[502, "networkx.algorithms.flow.gomory_hu_tree"]], "max_flow_min_cost() (in module networkx.algorithms.flow)": [[503, "networkx.algorithms.flow.max_flow_min_cost"]], "maximum_flow() (in module networkx.algorithms.flow)": [[504, "networkx.algorithms.flow.maximum_flow"]], "maximum_flow_value() (in module networkx.algorithms.flow)": [[505, "networkx.algorithms.flow.maximum_flow_value"]], "min_cost_flow() (in module networkx.algorithms.flow)": [[506, "networkx.algorithms.flow.min_cost_flow"]], "min_cost_flow_cost() (in module networkx.algorithms.flow)": [[507, "networkx.algorithms.flow.min_cost_flow_cost"]], "minimum_cut() (in module networkx.algorithms.flow)": [[508, "networkx.algorithms.flow.minimum_cut"]], "minimum_cut_value() (in module networkx.algorithms.flow)": [[509, "networkx.algorithms.flow.minimum_cut_value"]], "network_simplex() (in module networkx.algorithms.flow)": [[510, "networkx.algorithms.flow.network_simplex"]], "preflow_push() (in module networkx.algorithms.flow)": [[511, "networkx.algorithms.flow.preflow_push"]], "shortest_augmenting_path() (in module networkx.algorithms.flow)": [[512, "networkx.algorithms.flow.shortest_augmenting_path"]], "weisfeiler_lehman_graph_hash() (in module networkx.algorithms.graph_hashing)": [[513, "networkx.algorithms.graph_hashing.weisfeiler_lehman_graph_hash"]], "weisfeiler_lehman_subgraph_hashes() (in module networkx.algorithms.graph_hashing)": [[514, "networkx.algorithms.graph_hashing.weisfeiler_lehman_subgraph_hashes"]], "is_digraphical() (in module networkx.algorithms.graphical)": [[515, "networkx.algorithms.graphical.is_digraphical"]], "is_graphical() (in module networkx.algorithms.graphical)": [[516, "networkx.algorithms.graphical.is_graphical"]], "is_multigraphical() (in module networkx.algorithms.graphical)": [[517, "networkx.algorithms.graphical.is_multigraphical"]], "is_pseudographical() (in module networkx.algorithms.graphical)": [[518, "networkx.algorithms.graphical.is_pseudographical"]], "is_valid_degree_sequence_erdos_gallai() (in module networkx.algorithms.graphical)": [[519, "networkx.algorithms.graphical.is_valid_degree_sequence_erdos_gallai"]], "is_valid_degree_sequence_havel_hakimi() (in module networkx.algorithms.graphical)": [[520, "networkx.algorithms.graphical.is_valid_degree_sequence_havel_hakimi"]], "flow_hierarchy() (in module networkx.algorithms.hierarchy)": [[521, "networkx.algorithms.hierarchy.flow_hierarchy"]], "is_kl_connected() (in module networkx.algorithms.hybrid)": [[522, "networkx.algorithms.hybrid.is_kl_connected"]], "kl_connected_subgraph() (in module networkx.algorithms.hybrid)": [[523, "networkx.algorithms.hybrid.kl_connected_subgraph"]], "is_isolate() (in module networkx.algorithms.isolate)": [[524, "networkx.algorithms.isolate.is_isolate"]], "isolates() (in module networkx.algorithms.isolate)": [[525, "networkx.algorithms.isolate.isolates"]], "number_of_isolates() (in module networkx.algorithms.isolate)": [[526, "networkx.algorithms.isolate.number_of_isolates"]], "__init__() (digraphmatcher method)": [[527, "networkx.algorithms.isomorphism.DiGraphMatcher.__init__"]], "candidate_pairs_iter() (digraphmatcher method)": [[528, "networkx.algorithms.isomorphism.DiGraphMatcher.candidate_pairs_iter"]], "initialize() (digraphmatcher method)": [[529, "networkx.algorithms.isomorphism.DiGraphMatcher.initialize"]], "is_isomorphic() (digraphmatcher method)": [[530, "networkx.algorithms.isomorphism.DiGraphMatcher.is_isomorphic"]], "isomorphisms_iter() (digraphmatcher method)": [[531, "networkx.algorithms.isomorphism.DiGraphMatcher.isomorphisms_iter"]], "match() (digraphmatcher method)": [[532, "networkx.algorithms.isomorphism.DiGraphMatcher.match"]], "semantic_feasibility() (digraphmatcher method)": [[533, "networkx.algorithms.isomorphism.DiGraphMatcher.semantic_feasibility"]], "subgraph_is_isomorphic() (digraphmatcher method)": [[534, "networkx.algorithms.isomorphism.DiGraphMatcher.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (digraphmatcher method)": [[535, "networkx.algorithms.isomorphism.DiGraphMatcher.subgraph_isomorphisms_iter"]], "syntactic_feasibility() (digraphmatcher method)": [[536, "networkx.algorithms.isomorphism.DiGraphMatcher.syntactic_feasibility"]], "__init__() (graphmatcher method)": [[537, "networkx.algorithms.isomorphism.GraphMatcher.__init__"]], "candidate_pairs_iter() (graphmatcher method)": [[538, "networkx.algorithms.isomorphism.GraphMatcher.candidate_pairs_iter"]], "initialize() (graphmatcher method)": [[539, "networkx.algorithms.isomorphism.GraphMatcher.initialize"]], "is_isomorphic() (graphmatcher method)": [[540, "networkx.algorithms.isomorphism.GraphMatcher.is_isomorphic"]], "isomorphisms_iter() (graphmatcher method)": [[541, "networkx.algorithms.isomorphism.GraphMatcher.isomorphisms_iter"]], "match() (graphmatcher method)": [[542, "networkx.algorithms.isomorphism.GraphMatcher.match"]], "semantic_feasibility() (graphmatcher method)": [[543, "networkx.algorithms.isomorphism.GraphMatcher.semantic_feasibility"]], "subgraph_is_isomorphic() (graphmatcher method)": [[544, "networkx.algorithms.isomorphism.GraphMatcher.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (graphmatcher method)": [[545, "networkx.algorithms.isomorphism.GraphMatcher.subgraph_isomorphisms_iter"]], "syntactic_feasibility() (graphmatcher method)": [[546, "networkx.algorithms.isomorphism.GraphMatcher.syntactic_feasibility"]], "ismags (class in networkx.algorithms.isomorphism)": [[547, "networkx.algorithms.isomorphism.ISMAGS"]], "__init__() (ismags method)": [[547, "networkx.algorithms.isomorphism.ISMAGS.__init__"]], "categorical_edge_match() (in module networkx.algorithms.isomorphism)": [[548, "networkx.algorithms.isomorphism.categorical_edge_match"]], "categorical_multiedge_match() (in module networkx.algorithms.isomorphism)": [[549, "networkx.algorithms.isomorphism.categorical_multiedge_match"]], "categorical_node_match() (in module networkx.algorithms.isomorphism)": [[550, "networkx.algorithms.isomorphism.categorical_node_match"]], "could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[551, "networkx.algorithms.isomorphism.could_be_isomorphic"]], "fast_could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[552, "networkx.algorithms.isomorphism.fast_could_be_isomorphic"]], "faster_could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[553, "networkx.algorithms.isomorphism.faster_could_be_isomorphic"]], "generic_edge_match() (in module networkx.algorithms.isomorphism)": [[554, "networkx.algorithms.isomorphism.generic_edge_match"]], "generic_multiedge_match() (in module networkx.algorithms.isomorphism)": [[555, "networkx.algorithms.isomorphism.generic_multiedge_match"]], "generic_node_match() (in module networkx.algorithms.isomorphism)": [[556, "networkx.algorithms.isomorphism.generic_node_match"]], "is_isomorphic() (in module networkx.algorithms.isomorphism)": [[557, "networkx.algorithms.isomorphism.is_isomorphic"]], "numerical_edge_match() (in module networkx.algorithms.isomorphism)": [[558, "networkx.algorithms.isomorphism.numerical_edge_match"]], "numerical_multiedge_match() (in module networkx.algorithms.isomorphism)": [[559, "networkx.algorithms.isomorphism.numerical_multiedge_match"]], "numerical_node_match() (in module networkx.algorithms.isomorphism)": [[560, "networkx.algorithms.isomorphism.numerical_node_match"]], "rooted_tree_isomorphism() (in module networkx.algorithms.isomorphism.tree_isomorphism)": [[561, "networkx.algorithms.isomorphism.tree_isomorphism.rooted_tree_isomorphism"]], "tree_isomorphism() (in module networkx.algorithms.isomorphism.tree_isomorphism)": [[562, "networkx.algorithms.isomorphism.tree_isomorphism.tree_isomorphism"]], "vf2pp_all_isomorphisms() (in module networkx.algorithms.isomorphism.vf2pp)": [[563, "networkx.algorithms.isomorphism.vf2pp.vf2pp_all_isomorphisms"]], "vf2pp_is_isomorphic() (in module networkx.algorithms.isomorphism.vf2pp)": [[564, "networkx.algorithms.isomorphism.vf2pp.vf2pp_is_isomorphic"]], "vf2pp_isomorphism() (in module networkx.algorithms.isomorphism.vf2pp)": [[565, "networkx.algorithms.isomorphism.vf2pp.vf2pp_isomorphism"]], "hits() (in module networkx.algorithms.link_analysis.hits_alg)": [[566, "networkx.algorithms.link_analysis.hits_alg.hits"]], "google_matrix() (in module networkx.algorithms.link_analysis.pagerank_alg)": [[567, "networkx.algorithms.link_analysis.pagerank_alg.google_matrix"]], "pagerank() (in module networkx.algorithms.link_analysis.pagerank_alg)": [[568, "networkx.algorithms.link_analysis.pagerank_alg.pagerank"]], "adamic_adar_index() (in module networkx.algorithms.link_prediction)": [[569, "networkx.algorithms.link_prediction.adamic_adar_index"]], "cn_soundarajan_hopcroft() (in module networkx.algorithms.link_prediction)": [[570, "networkx.algorithms.link_prediction.cn_soundarajan_hopcroft"]], "common_neighbor_centrality() (in module networkx.algorithms.link_prediction)": [[571, "networkx.algorithms.link_prediction.common_neighbor_centrality"]], "jaccard_coefficient() (in module networkx.algorithms.link_prediction)": [[572, "networkx.algorithms.link_prediction.jaccard_coefficient"]], "preferential_attachment() (in module networkx.algorithms.link_prediction)": [[573, "networkx.algorithms.link_prediction.preferential_attachment"]], "ra_index_soundarajan_hopcroft() (in module networkx.algorithms.link_prediction)": [[574, "networkx.algorithms.link_prediction.ra_index_soundarajan_hopcroft"]], "resource_allocation_index() (in module networkx.algorithms.link_prediction)": [[575, "networkx.algorithms.link_prediction.resource_allocation_index"]], "within_inter_cluster() (in module networkx.algorithms.link_prediction)": [[576, "networkx.algorithms.link_prediction.within_inter_cluster"]], "all_pairs_lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[577, "networkx.algorithms.lowest_common_ancestors.all_pairs_lowest_common_ancestor"]], "lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[578, "networkx.algorithms.lowest_common_ancestors.lowest_common_ancestor"]], "tree_all_pairs_lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[579, "networkx.algorithms.lowest_common_ancestors.tree_all_pairs_lowest_common_ancestor"]], "is_matching() (in module networkx.algorithms.matching)": [[580, "networkx.algorithms.matching.is_matching"]], "is_maximal_matching() (in module networkx.algorithms.matching)": [[581, "networkx.algorithms.matching.is_maximal_matching"]], "is_perfect_matching() (in module networkx.algorithms.matching)": [[582, "networkx.algorithms.matching.is_perfect_matching"]], "max_weight_matching() (in module networkx.algorithms.matching)": [[583, "networkx.algorithms.matching.max_weight_matching"]], "maximal_matching() (in module networkx.algorithms.matching)": [[584, "networkx.algorithms.matching.maximal_matching"]], "min_weight_matching() (in module networkx.algorithms.matching)": [[585, "networkx.algorithms.matching.min_weight_matching"]], "contracted_edge() (in module networkx.algorithms.minors)": [[586, "networkx.algorithms.minors.contracted_edge"]], "contracted_nodes() (in module networkx.algorithms.minors)": [[587, "networkx.algorithms.minors.contracted_nodes"]], "equivalence_classes() (in module networkx.algorithms.minors)": [[588, "networkx.algorithms.minors.equivalence_classes"]], "identified_nodes() (in module networkx.algorithms.minors)": [[589, "networkx.algorithms.minors.identified_nodes"]], "quotient_graph() (in module networkx.algorithms.minors)": [[590, "networkx.algorithms.minors.quotient_graph"]], "maximal_independent_set() (in module networkx.algorithms.mis)": [[591, "networkx.algorithms.mis.maximal_independent_set"]], "moral_graph() (in module networkx.algorithms.moral)": [[592, "networkx.algorithms.moral.moral_graph"]], "harmonic_function() (in module networkx.algorithms.node_classification)": [[593, "networkx.algorithms.node_classification.harmonic_function"]], "local_and_global_consistency() (in module networkx.algorithms.node_classification)": [[594, "networkx.algorithms.node_classification.local_and_global_consistency"]], "non_randomness() (in module networkx.algorithms.non_randomness)": [[595, "networkx.algorithms.non_randomness.non_randomness"]], "compose_all() (in module networkx.algorithms.operators.all)": [[596, "networkx.algorithms.operators.all.compose_all"]], "disjoint_union_all() (in module networkx.algorithms.operators.all)": [[597, "networkx.algorithms.operators.all.disjoint_union_all"]], "intersection_all() (in module networkx.algorithms.operators.all)": [[598, "networkx.algorithms.operators.all.intersection_all"]], "union_all() (in module networkx.algorithms.operators.all)": [[599, "networkx.algorithms.operators.all.union_all"]], "compose() (in module networkx.algorithms.operators.binary)": [[600, "networkx.algorithms.operators.binary.compose"]], "difference() (in module networkx.algorithms.operators.binary)": [[601, "networkx.algorithms.operators.binary.difference"]], "disjoint_union() (in module networkx.algorithms.operators.binary)": [[602, "networkx.algorithms.operators.binary.disjoint_union"]], "full_join() (in module networkx.algorithms.operators.binary)": [[603, "networkx.algorithms.operators.binary.full_join"]], "intersection() (in module networkx.algorithms.operators.binary)": [[604, "networkx.algorithms.operators.binary.intersection"]], "symmetric_difference() (in module networkx.algorithms.operators.binary)": [[605, "networkx.algorithms.operators.binary.symmetric_difference"]], "union() (in module networkx.algorithms.operators.binary)": [[606, "networkx.algorithms.operators.binary.union"]], "cartesian_product() (in module networkx.algorithms.operators.product)": [[607, "networkx.algorithms.operators.product.cartesian_product"]], "corona_product() (in module networkx.algorithms.operators.product)": [[608, "networkx.algorithms.operators.product.corona_product"]], "lexicographic_product() (in module networkx.algorithms.operators.product)": [[609, "networkx.algorithms.operators.product.lexicographic_product"]], "power() (in module networkx.algorithms.operators.product)": [[610, "networkx.algorithms.operators.product.power"]], "rooted_product() (in module networkx.algorithms.operators.product)": [[611, "networkx.algorithms.operators.product.rooted_product"]], "strong_product() (in module networkx.algorithms.operators.product)": [[612, "networkx.algorithms.operators.product.strong_product"]], "tensor_product() (in module networkx.algorithms.operators.product)": [[613, "networkx.algorithms.operators.product.tensor_product"]], "complement() (in module networkx.algorithms.operators.unary)": [[614, "networkx.algorithms.operators.unary.complement"]], "reverse() (in module networkx.algorithms.operators.unary)": [[615, "networkx.algorithms.operators.unary.reverse"]], "combinatorial_embedding_to_pos() (in module networkx.algorithms.planar_drawing)": [[616, "networkx.algorithms.planar_drawing.combinatorial_embedding_to_pos"]], "planarembedding (class in networkx.algorithms.planarity)": [[617, "networkx.algorithms.planarity.PlanarEmbedding"]], "__init__() (planarembedding method)": [[617, "networkx.algorithms.planarity.PlanarEmbedding.__init__"]], "check_planarity() (in module networkx.algorithms.planarity)": [[618, "networkx.algorithms.planarity.check_planarity"]], "is_planar() (in module networkx.algorithms.planarity)": [[619, "networkx.algorithms.planarity.is_planar"]], "chromatic_polynomial() (in module networkx.algorithms.polynomials)": [[620, "networkx.algorithms.polynomials.chromatic_polynomial"]], "tutte_polynomial() (in module networkx.algorithms.polynomials)": [[621, "networkx.algorithms.polynomials.tutte_polynomial"]], "overall_reciprocity() (in module networkx.algorithms.reciprocity)": [[622, "networkx.algorithms.reciprocity.overall_reciprocity"]], "reciprocity() (in module networkx.algorithms.reciprocity)": [[623, "networkx.algorithms.reciprocity.reciprocity"]], "is_k_regular() (in module networkx.algorithms.regular)": [[624, "networkx.algorithms.regular.is_k_regular"]], "is_regular() (in module networkx.algorithms.regular)": [[625, "networkx.algorithms.regular.is_regular"]], "k_factor() (in module networkx.algorithms.regular)": [[626, "networkx.algorithms.regular.k_factor"]], "rich_club_coefficient() (in module networkx.algorithms.richclub)": [[627, "networkx.algorithms.richclub.rich_club_coefficient"]], "astar_path() (in module networkx.algorithms.shortest_paths.astar)": [[628, "networkx.algorithms.shortest_paths.astar.astar_path"]], "astar_path_length() (in module networkx.algorithms.shortest_paths.astar)": [[629, "networkx.algorithms.shortest_paths.astar.astar_path_length"]], "floyd_warshall() (in module networkx.algorithms.shortest_paths.dense)": [[630, "networkx.algorithms.shortest_paths.dense.floyd_warshall"]], "floyd_warshall_numpy() (in module networkx.algorithms.shortest_paths.dense)": [[631, "networkx.algorithms.shortest_paths.dense.floyd_warshall_numpy"]], "floyd_warshall_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.dense)": [[632, "networkx.algorithms.shortest_paths.dense.floyd_warshall_predecessor_and_distance"]], "reconstruct_path() (in module networkx.algorithms.shortest_paths.dense)": [[633, "networkx.algorithms.shortest_paths.dense.reconstruct_path"]], "all_shortest_paths() (in module networkx.algorithms.shortest_paths.generic)": [[634, "networkx.algorithms.shortest_paths.generic.all_shortest_paths"]], "average_shortest_path_length() (in module networkx.algorithms.shortest_paths.generic)": [[635, "networkx.algorithms.shortest_paths.generic.average_shortest_path_length"]], "has_path() (in module networkx.algorithms.shortest_paths.generic)": [[636, "networkx.algorithms.shortest_paths.generic.has_path"]], "shortest_path() (in module networkx.algorithms.shortest_paths.generic)": [[637, "networkx.algorithms.shortest_paths.generic.shortest_path"]], "shortest_path_length() (in module networkx.algorithms.shortest_paths.generic)": [[638, "networkx.algorithms.shortest_paths.generic.shortest_path_length"]], "all_pairs_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[639, "networkx.algorithms.shortest_paths.unweighted.all_pairs_shortest_path"]], "all_pairs_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[640, "networkx.algorithms.shortest_paths.unweighted.all_pairs_shortest_path_length"]], "bidirectional_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[641, "networkx.algorithms.shortest_paths.unweighted.bidirectional_shortest_path"]], "predecessor() (in module networkx.algorithms.shortest_paths.unweighted)": [[642, "networkx.algorithms.shortest_paths.unweighted.predecessor"]], "single_source_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[643, "networkx.algorithms.shortest_paths.unweighted.single_source_shortest_path"]], "single_source_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[644, "networkx.algorithms.shortest_paths.unweighted.single_source_shortest_path_length"]], "single_target_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[645, "networkx.algorithms.shortest_paths.unweighted.single_target_shortest_path"]], "single_target_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[646, "networkx.algorithms.shortest_paths.unweighted.single_target_shortest_path_length"]], "all_pairs_bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[647, "networkx.algorithms.shortest_paths.weighted.all_pairs_bellman_ford_path"]], "all_pairs_bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[648, "networkx.algorithms.shortest_paths.weighted.all_pairs_bellman_ford_path_length"]], "all_pairs_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[649, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra"]], "all_pairs_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[650, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra_path"]], "all_pairs_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[651, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra_path_length"]], "bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[652, "networkx.algorithms.shortest_paths.weighted.bellman_ford_path"]], "bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[653, "networkx.algorithms.shortest_paths.weighted.bellman_ford_path_length"]], "bellman_ford_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.weighted)": [[654, "networkx.algorithms.shortest_paths.weighted.bellman_ford_predecessor_and_distance"]], "bidirectional_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[655, "networkx.algorithms.shortest_paths.weighted.bidirectional_dijkstra"]], "dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[656, "networkx.algorithms.shortest_paths.weighted.dijkstra_path"]], "dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[657, "networkx.algorithms.shortest_paths.weighted.dijkstra_path_length"]], "dijkstra_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.weighted)": [[658, "networkx.algorithms.shortest_paths.weighted.dijkstra_predecessor_and_distance"]], "find_negative_cycle() (in module networkx.algorithms.shortest_paths.weighted)": [[659, "networkx.algorithms.shortest_paths.weighted.find_negative_cycle"]], "goldberg_radzik() (in module networkx.algorithms.shortest_paths.weighted)": [[660, "networkx.algorithms.shortest_paths.weighted.goldberg_radzik"]], "johnson() (in module networkx.algorithms.shortest_paths.weighted)": [[661, "networkx.algorithms.shortest_paths.weighted.johnson"]], "multi_source_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[662, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra"]], "multi_source_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[663, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra_path"]], "multi_source_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[664, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra_path_length"]], "negative_edge_cycle() (in module networkx.algorithms.shortest_paths.weighted)": [[665, "networkx.algorithms.shortest_paths.weighted.negative_edge_cycle"]], "single_source_bellman_ford() (in module networkx.algorithms.shortest_paths.weighted)": [[666, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford"]], "single_source_bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[667, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford_path"]], "single_source_bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[668, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford_path_length"]], "single_source_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[669, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra"]], "single_source_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[670, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra_path"]], "single_source_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[671, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra_path_length"]], "generate_random_paths() (in module networkx.algorithms.similarity)": [[672, "networkx.algorithms.similarity.generate_random_paths"]], "graph_edit_distance() (in module networkx.algorithms.similarity)": [[673, "networkx.algorithms.similarity.graph_edit_distance"]], "optimal_edit_paths() (in module networkx.algorithms.similarity)": [[674, "networkx.algorithms.similarity.optimal_edit_paths"]], "optimize_edit_paths() (in module networkx.algorithms.similarity)": [[675, "networkx.algorithms.similarity.optimize_edit_paths"]], "optimize_graph_edit_distance() (in module networkx.algorithms.similarity)": [[676, "networkx.algorithms.similarity.optimize_graph_edit_distance"]], "panther_similarity() (in module networkx.algorithms.similarity)": [[677, "networkx.algorithms.similarity.panther_similarity"]], "simrank_similarity() (in module networkx.algorithms.similarity)": [[678, "networkx.algorithms.similarity.simrank_similarity"]], "all_simple_edge_paths() (in module networkx.algorithms.simple_paths)": [[679, "networkx.algorithms.simple_paths.all_simple_edge_paths"]], "all_simple_paths() (in module networkx.algorithms.simple_paths)": [[680, "networkx.algorithms.simple_paths.all_simple_paths"]], "is_simple_path() (in module networkx.algorithms.simple_paths)": [[681, "networkx.algorithms.simple_paths.is_simple_path"]], "shortest_simple_paths() (in module networkx.algorithms.simple_paths)": [[682, "networkx.algorithms.simple_paths.shortest_simple_paths"]], "lattice_reference() (in module networkx.algorithms.smallworld)": [[683, "networkx.algorithms.smallworld.lattice_reference"]], "omega() (in module networkx.algorithms.smallworld)": [[684, "networkx.algorithms.smallworld.omega"]], "random_reference() (in module networkx.algorithms.smallworld)": [[685, "networkx.algorithms.smallworld.random_reference"]], "sigma() (in module networkx.algorithms.smallworld)": [[686, "networkx.algorithms.smallworld.sigma"]], "s_metric() (in module networkx.algorithms.smetric)": [[687, "networkx.algorithms.smetric.s_metric"]], "spanner() (in module networkx.algorithms.sparsifiers)": [[688, "networkx.algorithms.sparsifiers.spanner"]], "constraint() (in module networkx.algorithms.structuralholes)": [[689, "networkx.algorithms.structuralholes.constraint"]], "effective_size() (in module networkx.algorithms.structuralholes)": [[690, "networkx.algorithms.structuralholes.effective_size"]], "local_constraint() (in module networkx.algorithms.structuralholes)": [[691, "networkx.algorithms.structuralholes.local_constraint"]], "dedensify() (in module networkx.algorithms.summarization)": [[692, "networkx.algorithms.summarization.dedensify"]], "snap_aggregation() (in module networkx.algorithms.summarization)": [[693, "networkx.algorithms.summarization.snap_aggregation"]], "connected_double_edge_swap() (in module networkx.algorithms.swap)": [[694, "networkx.algorithms.swap.connected_double_edge_swap"]], "directed_edge_swap() (in module networkx.algorithms.swap)": [[695, "networkx.algorithms.swap.directed_edge_swap"]], "double_edge_swap() (in module networkx.algorithms.swap)": [[696, "networkx.algorithms.swap.double_edge_swap"]], "find_threshold_graph() (in module networkx.algorithms.threshold)": [[697, "networkx.algorithms.threshold.find_threshold_graph"]], "is_threshold_graph() (in module networkx.algorithms.threshold)": [[698, "networkx.algorithms.threshold.is_threshold_graph"]], "hamiltonian_path() (in module networkx.algorithms.tournament)": [[699, "networkx.algorithms.tournament.hamiltonian_path"]], "is_reachable() (in module networkx.algorithms.tournament)": [[700, "networkx.algorithms.tournament.is_reachable"]], "is_strongly_connected() (in module networkx.algorithms.tournament)": [[701, "networkx.algorithms.tournament.is_strongly_connected"]], "is_tournament() (in module networkx.algorithms.tournament)": [[702, "networkx.algorithms.tournament.is_tournament"]], "random_tournament() (in module networkx.algorithms.tournament)": [[703, "networkx.algorithms.tournament.random_tournament"]], "score_sequence() (in module networkx.algorithms.tournament)": [[704, "networkx.algorithms.tournament.score_sequence"]], "bfs_beam_edges() (in module networkx.algorithms.traversal.beamsearch)": [[705, "networkx.algorithms.traversal.beamsearch.bfs_beam_edges"]], "bfs_edges() (in module networkx.algorithms.traversal.breadth_first_search)": [[706, "networkx.algorithms.traversal.breadth_first_search.bfs_edges"]], "bfs_layers() (in module networkx.algorithms.traversal.breadth_first_search)": [[707, "networkx.algorithms.traversal.breadth_first_search.bfs_layers"]], "bfs_predecessors() (in module networkx.algorithms.traversal.breadth_first_search)": [[708, "networkx.algorithms.traversal.breadth_first_search.bfs_predecessors"]], "bfs_successors() (in module networkx.algorithms.traversal.breadth_first_search)": [[709, "networkx.algorithms.traversal.breadth_first_search.bfs_successors"]], "bfs_tree() (in module networkx.algorithms.traversal.breadth_first_search)": [[710, "networkx.algorithms.traversal.breadth_first_search.bfs_tree"]], "descendants_at_distance() (in module networkx.algorithms.traversal.breadth_first_search)": [[711, "networkx.algorithms.traversal.breadth_first_search.descendants_at_distance"]], "dfs_edges() (in module networkx.algorithms.traversal.depth_first_search)": [[712, "networkx.algorithms.traversal.depth_first_search.dfs_edges"]], "dfs_labeled_edges() (in module networkx.algorithms.traversal.depth_first_search)": [[713, "networkx.algorithms.traversal.depth_first_search.dfs_labeled_edges"]], "dfs_postorder_nodes() (in module networkx.algorithms.traversal.depth_first_search)": [[714, "networkx.algorithms.traversal.depth_first_search.dfs_postorder_nodes"]], "dfs_predecessors() (in module networkx.algorithms.traversal.depth_first_search)": [[715, "networkx.algorithms.traversal.depth_first_search.dfs_predecessors"]], "dfs_preorder_nodes() (in module networkx.algorithms.traversal.depth_first_search)": [[716, "networkx.algorithms.traversal.depth_first_search.dfs_preorder_nodes"]], "dfs_successors() (in module networkx.algorithms.traversal.depth_first_search)": [[717, "networkx.algorithms.traversal.depth_first_search.dfs_successors"]], "dfs_tree() (in module networkx.algorithms.traversal.depth_first_search)": [[718, "networkx.algorithms.traversal.depth_first_search.dfs_tree"]], "edge_bfs() (in module networkx.algorithms.traversal.edgebfs)": [[719, "networkx.algorithms.traversal.edgebfs.edge_bfs"]], "edge_dfs() (in module networkx.algorithms.traversal.edgedfs)": [[720, "networkx.algorithms.traversal.edgedfs.edge_dfs"]], "arborescenceiterator (class in networkx.algorithms.tree.branchings)": [[721, "networkx.algorithms.tree.branchings.ArborescenceIterator"]], "__init__() (arborescenceiterator method)": [[721, "networkx.algorithms.tree.branchings.ArborescenceIterator.__init__"]], "edmonds (class in networkx.algorithms.tree.branchings)": [[722, "networkx.algorithms.tree.branchings.Edmonds"]], "__init__() (edmonds method)": [[722, "networkx.algorithms.tree.branchings.Edmonds.__init__"]], "branching_weight() (in module networkx.algorithms.tree.branchings)": [[723, "networkx.algorithms.tree.branchings.branching_weight"]], "greedy_branching() (in module networkx.algorithms.tree.branchings)": [[724, "networkx.algorithms.tree.branchings.greedy_branching"]], "maximum_branching() (in module networkx.algorithms.tree.branchings)": [[725, "networkx.algorithms.tree.branchings.maximum_branching"]], "maximum_spanning_arborescence() (in module networkx.algorithms.tree.branchings)": [[726, "networkx.algorithms.tree.branchings.maximum_spanning_arborescence"]], "minimum_branching() (in module networkx.algorithms.tree.branchings)": [[727, "networkx.algorithms.tree.branchings.minimum_branching"]], "minimum_spanning_arborescence() (in module networkx.algorithms.tree.branchings)": [[728, "networkx.algorithms.tree.branchings.minimum_spanning_arborescence"]], "notatree": [[729, "networkx.algorithms.tree.coding.NotATree"]], "from_nested_tuple() (in module networkx.algorithms.tree.coding)": [[730, "networkx.algorithms.tree.coding.from_nested_tuple"]], "from_prufer_sequence() (in module networkx.algorithms.tree.coding)": [[731, "networkx.algorithms.tree.coding.from_prufer_sequence"]], "to_nested_tuple() (in module networkx.algorithms.tree.coding)": [[732, "networkx.algorithms.tree.coding.to_nested_tuple"]], "to_prufer_sequence() (in module networkx.algorithms.tree.coding)": [[733, "networkx.algorithms.tree.coding.to_prufer_sequence"]], "junction_tree() (in module networkx.algorithms.tree.decomposition)": [[734, "networkx.algorithms.tree.decomposition.junction_tree"]], "spanningtreeiterator (class in networkx.algorithms.tree.mst)": [[735, "networkx.algorithms.tree.mst.SpanningTreeIterator"]], "__init__() (spanningtreeiterator method)": [[735, "networkx.algorithms.tree.mst.SpanningTreeIterator.__init__"]], "maximum_spanning_edges() (in module networkx.algorithms.tree.mst)": [[736, "networkx.algorithms.tree.mst.maximum_spanning_edges"]], "maximum_spanning_tree() (in module networkx.algorithms.tree.mst)": [[737, "networkx.algorithms.tree.mst.maximum_spanning_tree"]], "minimum_spanning_edges() (in module networkx.algorithms.tree.mst)": [[738, "networkx.algorithms.tree.mst.minimum_spanning_edges"]], "minimum_spanning_tree() (in module networkx.algorithms.tree.mst)": [[739, "networkx.algorithms.tree.mst.minimum_spanning_tree"]], "random_spanning_tree() (in module networkx.algorithms.tree.mst)": [[740, "networkx.algorithms.tree.mst.random_spanning_tree"]], "join() (in module networkx.algorithms.tree.operations)": [[741, "networkx.algorithms.tree.operations.join"]], "is_arborescence() (in module networkx.algorithms.tree.recognition)": [[742, "networkx.algorithms.tree.recognition.is_arborescence"]], "is_branching() (in module networkx.algorithms.tree.recognition)": [[743, "networkx.algorithms.tree.recognition.is_branching"]], "is_forest() (in module networkx.algorithms.tree.recognition)": [[744, "networkx.algorithms.tree.recognition.is_forest"]], "is_tree() (in module networkx.algorithms.tree.recognition)": [[745, "networkx.algorithms.tree.recognition.is_tree"]], "all_triads() (in module networkx.algorithms.triads)": [[746, "networkx.algorithms.triads.all_triads"]], "all_triplets() (in module networkx.algorithms.triads)": [[747, "networkx.algorithms.triads.all_triplets"]], "is_triad() (in module networkx.algorithms.triads)": [[748, "networkx.algorithms.triads.is_triad"]], "random_triad() (in module networkx.algorithms.triads)": [[749, "networkx.algorithms.triads.random_triad"]], "triad_type() (in module networkx.algorithms.triads)": [[750, "networkx.algorithms.triads.triad_type"]], "triadic_census() (in module networkx.algorithms.triads)": [[751, "networkx.algorithms.triads.triadic_census"]], "triads_by_type() (in module networkx.algorithms.triads)": [[752, "networkx.algorithms.triads.triads_by_type"]], "closeness_vitality() (in module networkx.algorithms.vitality)": [[753, "networkx.algorithms.vitality.closeness_vitality"]], "voronoi_cells() (in module networkx.algorithms.voronoi)": [[754, "networkx.algorithms.voronoi.voronoi_cells"]], "wiener_index() (in module networkx.algorithms.wiener)": [[755, "networkx.algorithms.wiener.wiener_index"]], "networkx.algorithms.graph_hashing": [[756, "module-networkx.algorithms.graph_hashing"]], "networkx.algorithms.graphical": [[757, "module-networkx.algorithms.graphical"]], "networkx.algorithms.hierarchy": [[758, "module-networkx.algorithms.hierarchy"]], "networkx.algorithms.hybrid": [[759, "module-networkx.algorithms.hybrid"]], "networkx.algorithms.isolate": [[761, "module-networkx.algorithms.isolate"]], "networkx.algorithms.isomorphism": [[762, "module-networkx.algorithms.isomorphism"]], "networkx.algorithms.isomorphism.tree_isomorphism": [[762, "module-networkx.algorithms.isomorphism.tree_isomorphism"]], "networkx.algorithms.isomorphism.vf2pp": [[762, "module-networkx.algorithms.isomorphism.vf2pp"]], "networkx.algorithms.isomorphism.ismags": [[763, "module-networkx.algorithms.isomorphism.ismags"]], "networkx.algorithms.isomorphism.isomorphvf2": [[764, "module-networkx.algorithms.isomorphism.isomorphvf2"]], "networkx.algorithms.link_analysis.hits_alg": [[765, "module-networkx.algorithms.link_analysis.hits_alg"]], "networkx.algorithms.link_analysis.pagerank_alg": [[765, "module-networkx.algorithms.link_analysis.pagerank_alg"]], "networkx.algorithms.link_prediction": [[766, "module-networkx.algorithms.link_prediction"]], "networkx.algorithms.lowest_common_ancestors": [[767, "module-networkx.algorithms.lowest_common_ancestors"]], "networkx.algorithms.matching": [[768, "module-networkx.algorithms.matching"]], "networkx.algorithms.minors": [[769, "module-networkx.algorithms.minors"]], "networkx.algorithms.mis": [[770, "module-networkx.algorithms.mis"]], "networkx.algorithms.moral": [[771, "module-networkx.algorithms.moral"]], "networkx.algorithms.node_classification": [[772, "module-networkx.algorithms.node_classification"]], "networkx.algorithms.non_randomness": [[773, "module-networkx.algorithms.non_randomness"]], "networkx.algorithms.operators.all": [[774, "module-networkx.algorithms.operators.all"]], "networkx.algorithms.operators.binary": [[774, "module-networkx.algorithms.operators.binary"]], "networkx.algorithms.operators.product": [[774, "module-networkx.algorithms.operators.product"]], "networkx.algorithms.operators.unary": [[774, "module-networkx.algorithms.operators.unary"]], "networkx.algorithms.planar_drawing": [[775, "module-networkx.algorithms.planar_drawing"]], "networkx.algorithms.planarity": [[776, "module-networkx.algorithms.planarity"]], "networkx.algorithms.polynomials": [[777, "module-networkx.algorithms.polynomials"]], "networkx.algorithms.reciprocity": [[778, "module-networkx.algorithms.reciprocity"]], "networkx.algorithms.regular": [[779, "module-networkx.algorithms.regular"]], "networkx.algorithms.richclub": [[780, "module-networkx.algorithms.richclub"]], "networkx.algorithms.shortest_paths.astar": [[781, "module-networkx.algorithms.shortest_paths.astar"]], "networkx.algorithms.shortest_paths.dense": [[781, "module-networkx.algorithms.shortest_paths.dense"]], "networkx.algorithms.shortest_paths.generic": [[781, "module-networkx.algorithms.shortest_paths.generic"]], "networkx.algorithms.shortest_paths.unweighted": [[781, "module-networkx.algorithms.shortest_paths.unweighted"]], "networkx.algorithms.shortest_paths.weighted": [[781, "module-networkx.algorithms.shortest_paths.weighted"]], "networkx.algorithms.similarity": [[782, "module-networkx.algorithms.similarity"]], "networkx.algorithms.simple_paths": [[783, "module-networkx.algorithms.simple_paths"]], "networkx.algorithms.smallworld": [[784, "module-networkx.algorithms.smallworld"]], "networkx.algorithms.smetric": [[785, "module-networkx.algorithms.smetric"]], "networkx.algorithms.sparsifiers": [[786, "module-networkx.algorithms.sparsifiers"]], "networkx.algorithms.structuralholes": [[787, "module-networkx.algorithms.structuralholes"]], "networkx.algorithms.summarization": [[788, "module-networkx.algorithms.summarization"]], "networkx.algorithms.swap": [[789, "module-networkx.algorithms.swap"]], "networkx.algorithms.threshold": [[790, "module-networkx.algorithms.threshold"]], "networkx.algorithms.tournament": [[791, "module-networkx.algorithms.tournament"]], "networkx.algorithms.traversal.beamsearch": [[792, "module-networkx.algorithms.traversal.beamsearch"]], "networkx.algorithms.traversal.breadth_first_search": [[792, "module-networkx.algorithms.traversal.breadth_first_search"]], "networkx.algorithms.traversal.depth_first_search": [[792, "module-networkx.algorithms.traversal.depth_first_search"]], "networkx.algorithms.traversal.edgebfs": [[792, "module-networkx.algorithms.traversal.edgebfs"]], "networkx.algorithms.traversal.edgedfs": [[792, "module-networkx.algorithms.traversal.edgedfs"]], "networkx.algorithms.tree.branchings": [[793, "module-networkx.algorithms.tree.branchings"]], "networkx.algorithms.tree.coding": [[793, "module-networkx.algorithms.tree.coding"]], "networkx.algorithms.tree.decomposition": [[793, "module-networkx.algorithms.tree.decomposition"]], "networkx.algorithms.tree.mst": [[793, "module-networkx.algorithms.tree.mst"]], "networkx.algorithms.tree.operations": [[793, "module-networkx.algorithms.tree.operations"]], "networkx.algorithms.tree.recognition": [[793, "module-networkx.algorithms.tree.recognition"]], "networkx.algorithms.triads": [[794, "module-networkx.algorithms.triads"]], "networkx.algorithms.vitality": [[795, "module-networkx.algorithms.vitality"]], "networkx.algorithms.voronoi": [[796, "module-networkx.algorithms.voronoi"]], "networkx.algorithms.wiener": [[797, "module-networkx.algorithms.wiener"]], "digraph (class in networkx)": [[798, "networkx.DiGraph"]], "copy() (adjacencyview method)": [[799, "networkx.classes.coreviews.AdjacencyView.copy"]], "get() (adjacencyview method)": [[800, "networkx.classes.coreviews.AdjacencyView.get"]], "items() (adjacencyview method)": [[801, "networkx.classes.coreviews.AdjacencyView.items"]], "keys() (adjacencyview method)": [[802, "networkx.classes.coreviews.AdjacencyView.keys"]], "values() (adjacencyview method)": [[803, "networkx.classes.coreviews.AdjacencyView.values"]], "copy() (atlasview method)": [[804, "networkx.classes.coreviews.AtlasView.copy"]], "get() (atlasview method)": [[805, "networkx.classes.coreviews.AtlasView.get"]], "items() (atlasview method)": [[806, "networkx.classes.coreviews.AtlasView.items"]], "keys() (atlasview method)": [[807, "networkx.classes.coreviews.AtlasView.keys"]], "values() (atlasview method)": [[808, "networkx.classes.coreviews.AtlasView.values"]], "get() (filteradjacency method)": [[809, "networkx.classes.coreviews.FilterAdjacency.get"]], "items() (filteradjacency method)": [[810, "networkx.classes.coreviews.FilterAdjacency.items"]], "keys() (filteradjacency method)": [[811, "networkx.classes.coreviews.FilterAdjacency.keys"]], "values() (filteradjacency method)": [[812, "networkx.classes.coreviews.FilterAdjacency.values"]], "get() (filteratlas method)": [[813, "networkx.classes.coreviews.FilterAtlas.get"]], "items() (filteratlas method)": [[814, "networkx.classes.coreviews.FilterAtlas.items"]], "keys() (filteratlas method)": [[815, "networkx.classes.coreviews.FilterAtlas.keys"]], "values() (filteratlas method)": [[816, 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"networkx.classes.function.edge_subgraph"]], "edges() (in module networkx.classes.function)": [[1065, "networkx.classes.function.edges"]], "freeze() (in module networkx.classes.function)": [[1066, "networkx.classes.function.freeze"]], "get_edge_attributes() (in module networkx.classes.function)": [[1067, "networkx.classes.function.get_edge_attributes"]], "get_node_attributes() (in module networkx.classes.function)": [[1068, "networkx.classes.function.get_node_attributes"]], "induced_subgraph() (in module networkx.classes.function)": [[1069, "networkx.classes.function.induced_subgraph"]], "is_directed() (in module networkx.classes.function)": [[1070, "networkx.classes.function.is_directed"]], "is_empty() (in module networkx.classes.function)": [[1071, "networkx.classes.function.is_empty"]], "is_frozen() (in module networkx.classes.function)": [[1072, "networkx.classes.function.is_frozen"]], "is_negatively_weighted() (in module networkx.classes.function)": [[1073, "networkx.classes.function.is_negatively_weighted"]], "is_path() (in module networkx.classes.function)": [[1074, "networkx.classes.function.is_path"]], "is_weighted() (in module networkx.classes.function)": [[1075, "networkx.classes.function.is_weighted"]], "neighbors() (in module networkx.classes.function)": [[1076, "networkx.classes.function.neighbors"]], "nodes() (in module networkx.classes.function)": [[1077, "networkx.classes.function.nodes"]], "nodes_with_selfloops() (in module networkx.classes.function)": [[1078, "networkx.classes.function.nodes_with_selfloops"]], "non_edges() (in module networkx.classes.function)": [[1079, "networkx.classes.function.non_edges"]], "non_neighbors() (in module networkx.classes.function)": [[1080, "networkx.classes.function.non_neighbors"]], "number_of_edges() (in module networkx.classes.function)": [[1081, "networkx.classes.function.number_of_edges"]], "number_of_nodes() (in module networkx.classes.function)": [[1082, "networkx.classes.function.number_of_nodes"]], "number_of_selfloops() (in module networkx.classes.function)": [[1083, "networkx.classes.function.number_of_selfloops"]], "path_weight() (in module networkx.classes.function)": [[1084, "networkx.classes.function.path_weight"]], "restricted_view() (in module networkx.classes.function)": [[1085, "networkx.classes.function.restricted_view"]], "reverse_view() (in module networkx.classes.function)": [[1086, "networkx.classes.function.reverse_view"]], "selfloop_edges() (in module networkx.classes.function)": [[1087, "networkx.classes.function.selfloop_edges"]], "set_edge_attributes() (in module networkx.classes.function)": [[1088, "networkx.classes.function.set_edge_attributes"]], "set_node_attributes() (in module networkx.classes.function)": [[1089, "networkx.classes.function.set_node_attributes"]], "subgraph() (in module networkx.classes.function)": [[1090, "networkx.classes.function.subgraph"]], "subgraph_view() (in module networkx.classes.function)": [[1091, "networkx.classes.function.subgraph_view"]], "to_directed() (in module networkx.classes.function)": [[1092, "networkx.classes.function.to_directed"]], "to_undirected() (in module networkx.classes.function)": [[1093, "networkx.classes.function.to_undirected"]], "from_dict_of_dicts() (in module networkx.convert)": [[1094, "networkx.convert.from_dict_of_dicts"]], "from_dict_of_lists() (in module networkx.convert)": [[1095, "networkx.convert.from_dict_of_lists"]], "from_edgelist() (in module networkx.convert)": [[1096, "networkx.convert.from_edgelist"]], "to_dict_of_dicts() (in module networkx.convert)": [[1097, "networkx.convert.to_dict_of_dicts"]], "to_dict_of_lists() (in module networkx.convert)": [[1098, "networkx.convert.to_dict_of_lists"]], "to_edgelist() (in module networkx.convert)": [[1099, "networkx.convert.to_edgelist"]], "to_networkx_graph() (in module networkx.convert)": [[1100, "networkx.convert.to_networkx_graph"]], "from_numpy_array() 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module networkx.drawing.nx_pylab)": [[1137, "networkx.drawing.nx_pylab.draw_circular"]], "draw_kamada_kawai() (in module networkx.drawing.nx_pylab)": [[1138, "networkx.drawing.nx_pylab.draw_kamada_kawai"]], "draw_networkx() (in module networkx.drawing.nx_pylab)": [[1139, "networkx.drawing.nx_pylab.draw_networkx"]], "draw_networkx_edge_labels() (in module networkx.drawing.nx_pylab)": [[1140, "networkx.drawing.nx_pylab.draw_networkx_edge_labels"]], "draw_networkx_edges() (in module networkx.drawing.nx_pylab)": [[1141, "networkx.drawing.nx_pylab.draw_networkx_edges"]], "draw_networkx_labels() (in module networkx.drawing.nx_pylab)": [[1142, "networkx.drawing.nx_pylab.draw_networkx_labels"]], "draw_networkx_nodes() (in module networkx.drawing.nx_pylab)": [[1143, "networkx.drawing.nx_pylab.draw_networkx_nodes"]], "draw_planar() (in module networkx.drawing.nx_pylab)": [[1144, "networkx.drawing.nx_pylab.draw_planar"]], "draw_random() (in module networkx.drawing.nx_pylab)": [[1145, 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networkx.generators.community)": [[1171, "networkx.generators.community.LFR_benchmark_graph"]], "caveman_graph() (in module networkx.generators.community)": [[1172, "networkx.generators.community.caveman_graph"]], "connected_caveman_graph() (in module networkx.generators.community)": [[1173, "networkx.generators.community.connected_caveman_graph"]], "gaussian_random_partition_graph() (in module networkx.generators.community)": [[1174, "networkx.generators.community.gaussian_random_partition_graph"]], "planted_partition_graph() (in module networkx.generators.community)": [[1175, "networkx.generators.community.planted_partition_graph"]], "random_partition_graph() (in module networkx.generators.community)": [[1176, "networkx.generators.community.random_partition_graph"]], "relaxed_caveman_graph() (in module networkx.generators.community)": [[1177, "networkx.generators.community.relaxed_caveman_graph"]], "ring_of_cliques() (in module networkx.generators.community)": [[1178, 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networkx.generators.internet_as_graphs)": [[1208, "networkx.generators.internet_as_graphs.random_internet_as_graph"]], "general_random_intersection_graph() (in module networkx.generators.intersection)": [[1209, "networkx.generators.intersection.general_random_intersection_graph"]], "k_random_intersection_graph() (in module networkx.generators.intersection)": [[1210, "networkx.generators.intersection.k_random_intersection_graph"]], "uniform_random_intersection_graph() (in module networkx.generators.intersection)": [[1211, "networkx.generators.intersection.uniform_random_intersection_graph"]], "interval_graph() (in module networkx.generators.interval_graph)": [[1212, "networkx.generators.interval_graph.interval_graph"]], "directed_joint_degree_graph() (in module networkx.generators.joint_degree_seq)": [[1213, "networkx.generators.joint_degree_seq.directed_joint_degree_graph"]], "is_valid_directed_joint_degree() (in module networkx.generators.joint_degree_seq)": [[1214, 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module networkx.generators.line)": [[1222, "networkx.generators.line.inverse_line_graph"]], "line_graph() (in module networkx.generators.line)": [[1223, "networkx.generators.line.line_graph"]], "mycielski_graph() (in module networkx.generators.mycielski)": [[1224, "networkx.generators.mycielski.mycielski_graph"]], "mycielskian() (in module networkx.generators.mycielski)": [[1225, "networkx.generators.mycielski.mycielskian"]], "nonisomorphic_trees() (in module networkx.generators.nonisomorphic_trees)": [[1226, "networkx.generators.nonisomorphic_trees.nonisomorphic_trees"]], "number_of_nonisomorphic_trees() (in module networkx.generators.nonisomorphic_trees)": [[1227, "networkx.generators.nonisomorphic_trees.number_of_nonisomorphic_trees"]], "random_clustered_graph() (in module networkx.generators.random_clustered)": [[1228, "networkx.generators.random_clustered.random_clustered_graph"]], "barabasi_albert_graph() (in module networkx.generators.random_graphs)": [[1229, "networkx.generators.random_graphs.barabasi_albert_graph"]], "binomial_graph() (in module networkx.generators.random_graphs)": [[1230, "networkx.generators.random_graphs.binomial_graph"]], "connected_watts_strogatz_graph() (in module networkx.generators.random_graphs)": [[1231, "networkx.generators.random_graphs.connected_watts_strogatz_graph"]], "dense_gnm_random_graph() (in module networkx.generators.random_graphs)": [[1232, "networkx.generators.random_graphs.dense_gnm_random_graph"]], "dual_barabasi_albert_graph() (in module networkx.generators.random_graphs)": [[1233, "networkx.generators.random_graphs.dual_barabasi_albert_graph"]], "erdos_renyi_graph() (in module networkx.generators.random_graphs)": [[1234, "networkx.generators.random_graphs.erdos_renyi_graph"]], "extended_barabasi_albert_graph() (in module networkx.generators.random_graphs)": [[1235, "networkx.generators.random_graphs.extended_barabasi_albert_graph"]], "fast_gnp_random_graph() (in module networkx.generators.random_graphs)": [[1236, "networkx.generators.random_graphs.fast_gnp_random_graph"]], "gnm_random_graph() (in module networkx.generators.random_graphs)": [[1237, "networkx.generators.random_graphs.gnm_random_graph"]], "gnp_random_graph() (in module networkx.generators.random_graphs)": [[1238, "networkx.generators.random_graphs.gnp_random_graph"]], "newman_watts_strogatz_graph() (in module networkx.generators.random_graphs)": [[1239, "networkx.generators.random_graphs.newman_watts_strogatz_graph"]], "powerlaw_cluster_graph() (in module networkx.generators.random_graphs)": [[1240, "networkx.generators.random_graphs.powerlaw_cluster_graph"]], "random_kernel_graph() (in module networkx.generators.random_graphs)": [[1241, "networkx.generators.random_graphs.random_kernel_graph"]], "random_lobster() (in module networkx.generators.random_graphs)": [[1242, "networkx.generators.random_graphs.random_lobster"]], "random_powerlaw_tree() (in module networkx.generators.random_graphs)": [[1243, "networkx.generators.random_graphs.random_powerlaw_tree"]], "random_powerlaw_tree_sequence() (in module networkx.generators.random_graphs)": [[1244, "networkx.generators.random_graphs.random_powerlaw_tree_sequence"]], "random_regular_graph() (in module networkx.generators.random_graphs)": [[1245, "networkx.generators.random_graphs.random_regular_graph"]], "random_shell_graph() (in module networkx.generators.random_graphs)": [[1246, "networkx.generators.random_graphs.random_shell_graph"]], "watts_strogatz_graph() (in module networkx.generators.random_graphs)": [[1247, "networkx.generators.random_graphs.watts_strogatz_graph"]], "lcf_graph() (in module networkx.generators.small)": [[1248, "networkx.generators.small.LCF_graph"]], "bull_graph() (in module networkx.generators.small)": [[1249, "networkx.generators.small.bull_graph"]], "chvatal_graph() (in module networkx.generators.small)": [[1250, "networkx.generators.small.chvatal_graph"]], "cubical_graph() (in module networkx.generators.small)": [[1251, "networkx.generators.small.cubical_graph"]], "desargues_graph() (in module networkx.generators.small)": [[1252, "networkx.generators.small.desargues_graph"]], "diamond_graph() (in module networkx.generators.small)": [[1253, "networkx.generators.small.diamond_graph"]], "dodecahedral_graph() (in module networkx.generators.small)": [[1254, "networkx.generators.small.dodecahedral_graph"]], "frucht_graph() (in module networkx.generators.small)": [[1255, "networkx.generators.small.frucht_graph"]], "heawood_graph() (in module networkx.generators.small)": [[1256, "networkx.generators.small.heawood_graph"]], "hoffman_singleton_graph() (in module networkx.generators.small)": [[1257, "networkx.generators.small.hoffman_singleton_graph"]], "house_graph() (in module networkx.generators.small)": [[1258, "networkx.generators.small.house_graph"]], "house_x_graph() (in module networkx.generators.small)": [[1259, "networkx.generators.small.house_x_graph"]], "icosahedral_graph() (in module networkx.generators.small)": [[1260, "networkx.generators.small.icosahedral_graph"]], "krackhardt_kite_graph() (in module networkx.generators.small)": [[1261, "networkx.generators.small.krackhardt_kite_graph"]], "moebius_kantor_graph() (in module networkx.generators.small)": [[1262, "networkx.generators.small.moebius_kantor_graph"]], "octahedral_graph() (in module networkx.generators.small)": [[1263, "networkx.generators.small.octahedral_graph"]], "pappus_graph() (in module networkx.generators.small)": [[1264, "networkx.generators.small.pappus_graph"]], "petersen_graph() (in module networkx.generators.small)": [[1265, "networkx.generators.small.petersen_graph"]], "sedgewick_maze_graph() (in module networkx.generators.small)": [[1266, "networkx.generators.small.sedgewick_maze_graph"]], "tetrahedral_graph() (in module networkx.generators.small)": [[1267, "networkx.generators.small.tetrahedral_graph"]], "truncated_cube_graph() (in module networkx.generators.small)": [[1268, "networkx.generators.small.truncated_cube_graph"]], "truncated_tetrahedron_graph() (in module networkx.generators.small)": [[1269, "networkx.generators.small.truncated_tetrahedron_graph"]], "tutte_graph() (in module networkx.generators.small)": [[1270, "networkx.generators.small.tutte_graph"]], "davis_southern_women_graph() (in module networkx.generators.social)": [[1271, "networkx.generators.social.davis_southern_women_graph"]], "florentine_families_graph() (in module networkx.generators.social)": [[1272, "networkx.generators.social.florentine_families_graph"]], "karate_club_graph() (in module networkx.generators.social)": [[1273, "networkx.generators.social.karate_club_graph"]], "les_miserables_graph() (in module networkx.generators.social)": [[1274, "networkx.generators.social.les_miserables_graph"]], "spectral_graph_forge() (in module networkx.generators.spectral_graph_forge)": [[1275, "networkx.generators.spectral_graph_forge.spectral_graph_forge"]], "stochastic_graph() (in module networkx.generators.stochastic)": [[1276, "networkx.generators.stochastic.stochastic_graph"]], "sudoku_graph() (in module networkx.generators.sudoku)": [[1277, "networkx.generators.sudoku.sudoku_graph"]], "prefix_tree() (in module networkx.generators.trees)": [[1278, "networkx.generators.trees.prefix_tree"]], "random_tree() (in module networkx.generators.trees)": [[1279, "networkx.generators.trees.random_tree"]], "triad_graph() (in module networkx.generators.triads)": [[1280, "networkx.generators.triads.triad_graph"]], "algebraic_connectivity() (in module networkx.linalg.algebraicconnectivity)": [[1281, "networkx.linalg.algebraicconnectivity.algebraic_connectivity"]], "fiedler_vector() (in module networkx.linalg.algebraicconnectivity)": [[1282, "networkx.linalg.algebraicconnectivity.fiedler_vector"]], "spectral_ordering() (in module networkx.linalg.algebraicconnectivity)": [[1283, "networkx.linalg.algebraicconnectivity.spectral_ordering"]], "attr_matrix() (in module networkx.linalg.attrmatrix)": [[1284, "networkx.linalg.attrmatrix.attr_matrix"]], "attr_sparse_matrix() (in module networkx.linalg.attrmatrix)": [[1285, "networkx.linalg.attrmatrix.attr_sparse_matrix"]], "bethe_hessian_matrix() (in module networkx.linalg.bethehessianmatrix)": [[1286, "networkx.linalg.bethehessianmatrix.bethe_hessian_matrix"]], "adjacency_matrix() (in module networkx.linalg.graphmatrix)": [[1287, "networkx.linalg.graphmatrix.adjacency_matrix"]], "incidence_matrix() (in module networkx.linalg.graphmatrix)": [[1288, "networkx.linalg.graphmatrix.incidence_matrix"]], "directed_combinatorial_laplacian_matrix() (in module networkx.linalg.laplacianmatrix)": [[1289, "networkx.linalg.laplacianmatrix.directed_combinatorial_laplacian_matrix"]], "directed_laplacian_matrix() (in module networkx.linalg.laplacianmatrix)": [[1290, "networkx.linalg.laplacianmatrix.directed_laplacian_matrix"]], "laplacian_matrix() (in module networkx.linalg.laplacianmatrix)": [[1291, "networkx.linalg.laplacianmatrix.laplacian_matrix"]], "normalized_laplacian_matrix() (in module networkx.linalg.laplacianmatrix)": [[1292, "networkx.linalg.laplacianmatrix.normalized_laplacian_matrix"]], "directed_modularity_matrix() (in module networkx.linalg.modularitymatrix)": [[1293, "networkx.linalg.modularitymatrix.directed_modularity_matrix"]], "modularity_matrix() (in module networkx.linalg.modularitymatrix)": [[1294, "networkx.linalg.modularitymatrix.modularity_matrix"]], "adjacency_spectrum() (in module networkx.linalg.spectrum)": [[1295, "networkx.linalg.spectrum.adjacency_spectrum"]], "bethe_hessian_spectrum() (in module networkx.linalg.spectrum)": [[1296, "networkx.linalg.spectrum.bethe_hessian_spectrum"]], "laplacian_spectrum() (in module networkx.linalg.spectrum)": [[1297, "networkx.linalg.spectrum.laplacian_spectrum"]], "modularity_spectrum() (in module networkx.linalg.spectrum)": [[1298, "networkx.linalg.spectrum.modularity_spectrum"]], "normalized_laplacian_spectrum() (in module networkx.linalg.spectrum)": [[1299, "networkx.linalg.spectrum.normalized_laplacian_spectrum"]], "convert_node_labels_to_integers() (in module networkx.relabel)": [[1300, "networkx.relabel.convert_node_labels_to_integers"]], "relabel_nodes() (in module networkx.relabel)": [[1301, "networkx.relabel.relabel_nodes"]], "__init__() (argmap method)": [[1302, "networkx.utils.decorators.argmap.__init__"]], "argmap (class in networkx.utils.decorators)": [[1302, "networkx.utils.decorators.argmap"]], "nodes_or_number() (in module networkx.utils.decorators)": [[1303, "networkx.utils.decorators.nodes_or_number"]], "not_implemented_for() (in module networkx.utils.decorators)": [[1304, "networkx.utils.decorators.not_implemented_for"]], "np_random_state() (in module networkx.utils.decorators)": [[1305, "networkx.utils.decorators.np_random_state"]], "open_file() (in module networkx.utils.decorators)": [[1306, "networkx.utils.decorators.open_file"]], "py_random_state() (in module networkx.utils.decorators)": [[1307, "networkx.utils.decorators.py_random_state"]], "mappedqueue (class in networkx.utils.mapped_queue)": [[1308, "networkx.utils.mapped_queue.MappedQueue"]], "__init__() (mappedqueue method)": [[1308, "networkx.utils.mapped_queue.MappedQueue.__init__"]], "arbitrary_element() (in module networkx.utils.misc)": [[1309, "networkx.utils.misc.arbitrary_element"]], "create_py_random_state() (in module networkx.utils.misc)": [[1310, "networkx.utils.misc.create_py_random_state"]], "create_random_state() (in module networkx.utils.misc)": [[1311, "networkx.utils.misc.create_random_state"]], "dict_to_numpy_array() (in module networkx.utils.misc)": [[1312, "networkx.utils.misc.dict_to_numpy_array"]], "edges_equal() (in module networkx.utils.misc)": [[1313, "networkx.utils.misc.edges_equal"]], "flatten() (in module networkx.utils.misc)": [[1314, "networkx.utils.misc.flatten"]], "graphs_equal() (in module networkx.utils.misc)": [[1315, "networkx.utils.misc.graphs_equal"]], "groups() (in module networkx.utils.misc)": [[1316, "networkx.utils.misc.groups"]], "make_list_of_ints() (in module networkx.utils.misc)": [[1317, "networkx.utils.misc.make_list_of_ints"]], "nodes_equal() (in module networkx.utils.misc)": [[1318, "networkx.utils.misc.nodes_equal"]], "pairwise() (in module networkx.utils.misc)": [[1319, "networkx.utils.misc.pairwise"]], "cumulative_distribution() (in module networkx.utils.random_sequence)": [[1320, "networkx.utils.random_sequence.cumulative_distribution"]], "discrete_sequence() (in module networkx.utils.random_sequence)": [[1321, "networkx.utils.random_sequence.discrete_sequence"]], "powerlaw_sequence() (in module networkx.utils.random_sequence)": [[1322, "networkx.utils.random_sequence.powerlaw_sequence"]], "random_weighted_sample() (in module networkx.utils.random_sequence)": [[1323, "networkx.utils.random_sequence.random_weighted_sample"]], "weighted_choice() (in module networkx.utils.random_sequence)": [[1324, "networkx.utils.random_sequence.weighted_choice"]], "zipf_rv() (in module networkx.utils.random_sequence)": [[1325, "networkx.utils.random_sequence.zipf_rv"]], "cuthill_mckee_ordering() (in module networkx.utils.rcm)": [[1326, "networkx.utils.rcm.cuthill_mckee_ordering"]], "reverse_cuthill_mckee_ordering() (in module networkx.utils.rcm)": [[1327, "networkx.utils.rcm.reverse_cuthill_mckee_ordering"]], "union() (unionfind method)": [[1328, "networkx.utils.union_find.UnionFind.union"]], "networkx.generators.atlas": [[1329, "module-networkx.generators.atlas"]], "networkx.generators.classic": [[1329, "module-networkx.generators.classic"]], "networkx.generators.cographs": [[1329, "module-networkx.generators.cographs"]], "networkx.generators.community": [[1329, "module-networkx.generators.community"]], "networkx.generators.degree_seq": [[1329, "module-networkx.generators.degree_seq"]], "networkx.generators.directed": [[1329, "module-networkx.generators.directed"]], "networkx.generators.duplication": [[1329, "module-networkx.generators.duplication"]], "networkx.generators.ego": [[1329, "module-networkx.generators.ego"]], "networkx.generators.expanders": [[1329, "module-networkx.generators.expanders"]], "networkx.generators.geometric": [[1329, "module-networkx.generators.geometric"]], "networkx.generators.harary_graph": [[1329, "module-networkx.generators.harary_graph"]], "networkx.generators.internet_as_graphs": [[1329, "module-networkx.generators.internet_as_graphs"]], "networkx.generators.intersection": [[1329, "module-networkx.generators.intersection"]], "networkx.generators.interval_graph": [[1329, "module-networkx.generators.interval_graph"]], "networkx.generators.joint_degree_seq": [[1329, "module-networkx.generators.joint_degree_seq"]], "networkx.generators.lattice": [[1329, "module-networkx.generators.lattice"]], "networkx.generators.line": [[1329, "module-networkx.generators.line"]], "networkx.generators.mycielski": [[1329, "module-networkx.generators.mycielski"]], "networkx.generators.nonisomorphic_trees": [[1329, "module-networkx.generators.nonisomorphic_trees"]], "networkx.generators.random_clustered": [[1329, "module-networkx.generators.random_clustered"]], "networkx.generators.random_graphs": [[1329, "module-networkx.generators.random_graphs"]], "networkx.generators.small": [[1329, "module-networkx.generators.small"]], "networkx.generators.social": [[1329, "module-networkx.generators.social"]], "networkx.generators.spectral_graph_forge": [[1329, "module-networkx.generators.spectral_graph_forge"]], "networkx.generators.stochastic": [[1329, "module-networkx.generators.stochastic"]], "networkx.generators.sudoku": [[1329, "module-networkx.generators.sudoku"]], "networkx.generators.trees": [[1329, "module-networkx.generators.trees"]], "networkx.generators.triads": [[1329, "module-networkx.generators.triads"]], "dictionary": [[1330, "term-dictionary"]], "ebunch": [[1330, "term-ebunch"]], "edge": [[1330, "term-edge"]], "edge attribute": [[1330, "term-edge-attribute"]], "nbunch": [[1330, "term-nbunch"]], "node": [[1330, "term-node"]], "node attribute": [[1330, "term-node-attribute"]], "networkx.linalg.algebraicconnectivity": [[1333, "module-networkx.linalg.algebraicconnectivity"]], "networkx.linalg.attrmatrix": [[1333, "module-networkx.linalg.attrmatrix"]], "networkx.linalg.bethehessianmatrix": [[1333, "module-networkx.linalg.bethehessianmatrix"]], "networkx.linalg.graphmatrix": [[1333, "module-networkx.linalg.graphmatrix"]], "networkx.linalg.laplacianmatrix": [[1333, "module-networkx.linalg.laplacianmatrix"]], "networkx.linalg.modularitymatrix": [[1333, "module-networkx.linalg.modularitymatrix"]], "networkx.linalg.spectrum": [[1333, "module-networkx.linalg.spectrum"]], "networkx.readwrite.adjlist": [[1335, "module-networkx.readwrite.adjlist"]], "networkx.readwrite.edgelist": [[1336, "module-networkx.readwrite.edgelist"]], "generate_adjlist() (in module networkx.readwrite.adjlist)": [[1337, "networkx.readwrite.adjlist.generate_adjlist"]], "parse_adjlist() (in module networkx.readwrite.adjlist)": [[1338, "networkx.readwrite.adjlist.parse_adjlist"]], "read_adjlist() (in module networkx.readwrite.adjlist)": [[1339, "networkx.readwrite.adjlist.read_adjlist"]], "write_adjlist() (in module networkx.readwrite.adjlist)": [[1340, "networkx.readwrite.adjlist.write_adjlist"]], "generate_edgelist() (in module networkx.readwrite.edgelist)": [[1341, "networkx.readwrite.edgelist.generate_edgelist"]], "parse_edgelist() (in module networkx.readwrite.edgelist)": [[1342, "networkx.readwrite.edgelist.parse_edgelist"]], "read_edgelist() (in module networkx.readwrite.edgelist)": [[1343, "networkx.readwrite.edgelist.read_edgelist"]], "read_weighted_edgelist() (in module networkx.readwrite.edgelist)": [[1344, "networkx.readwrite.edgelist.read_weighted_edgelist"]], "write_edgelist() (in module networkx.readwrite.edgelist)": [[1345, "networkx.readwrite.edgelist.write_edgelist"]], "write_weighted_edgelist() (in module networkx.readwrite.edgelist)": [[1346, "networkx.readwrite.edgelist.write_weighted_edgelist"]], "generate_gexf() (in module networkx.readwrite.gexf)": [[1347, "networkx.readwrite.gexf.generate_gexf"]], "read_gexf() (in module networkx.readwrite.gexf)": [[1348, "networkx.readwrite.gexf.read_gexf"]], "relabel_gexf_graph() (in module networkx.readwrite.gexf)": [[1349, "networkx.readwrite.gexf.relabel_gexf_graph"]], "write_gexf() (in module networkx.readwrite.gexf)": [[1350, "networkx.readwrite.gexf.write_gexf"]], "generate_gml() (in module networkx.readwrite.gml)": [[1351, "networkx.readwrite.gml.generate_gml"]], "literal_destringizer() (in module networkx.readwrite.gml)": [[1352, "networkx.readwrite.gml.literal_destringizer"]], "literal_stringizer() (in module networkx.readwrite.gml)": [[1353, "networkx.readwrite.gml.literal_stringizer"]], "parse_gml() (in module networkx.readwrite.gml)": [[1354, "networkx.readwrite.gml.parse_gml"]], "read_gml() (in module networkx.readwrite.gml)": [[1355, "networkx.readwrite.gml.read_gml"]], "write_gml() (in module networkx.readwrite.gml)": [[1356, "networkx.readwrite.gml.write_gml"]], "from_graph6_bytes() (in module networkx.readwrite.graph6)": [[1357, "networkx.readwrite.graph6.from_graph6_bytes"]], "read_graph6() (in module networkx.readwrite.graph6)": [[1358, "networkx.readwrite.graph6.read_graph6"]], "to_graph6_bytes() (in module networkx.readwrite.graph6)": [[1359, "networkx.readwrite.graph6.to_graph6_bytes"]], "write_graph6() (in module networkx.readwrite.graph6)": [[1360, "networkx.readwrite.graph6.write_graph6"]], "generate_graphml() (in module networkx.readwrite.graphml)": [[1361, "networkx.readwrite.graphml.generate_graphml"]], "parse_graphml() (in module networkx.readwrite.graphml)": [[1362, "networkx.readwrite.graphml.parse_graphml"]], "read_graphml() (in module networkx.readwrite.graphml)": [[1363, "networkx.readwrite.graphml.read_graphml"]], "write_graphml() (in module networkx.readwrite.graphml)": [[1364, "networkx.readwrite.graphml.write_graphml"]], "adjacency_data() (in module networkx.readwrite.json_graph)": [[1365, "networkx.readwrite.json_graph.adjacency_data"]], "adjacency_graph() (in module networkx.readwrite.json_graph)": [[1366, "networkx.readwrite.json_graph.adjacency_graph"]], "cytoscape_data() (in module networkx.readwrite.json_graph)": [[1367, "networkx.readwrite.json_graph.cytoscape_data"]], "cytoscape_graph() (in module networkx.readwrite.json_graph)": [[1368, "networkx.readwrite.json_graph.cytoscape_graph"]], "node_link_data() (in module networkx.readwrite.json_graph)": [[1369, "networkx.readwrite.json_graph.node_link_data"]], "node_link_graph() (in module networkx.readwrite.json_graph)": [[1370, "networkx.readwrite.json_graph.node_link_graph"]], "tree_data() (in module networkx.readwrite.json_graph)": [[1371, "networkx.readwrite.json_graph.tree_data"]], "tree_graph() (in module networkx.readwrite.json_graph)": [[1372, "networkx.readwrite.json_graph.tree_graph"]], "parse_leda() (in module networkx.readwrite.leda)": [[1373, "networkx.readwrite.leda.parse_leda"]], "read_leda() (in module networkx.readwrite.leda)": [[1374, "networkx.readwrite.leda.read_leda"]], "generate_multiline_adjlist() (in module networkx.readwrite.multiline_adjlist)": [[1375, "networkx.readwrite.multiline_adjlist.generate_multiline_adjlist"]], "parse_multiline_adjlist() (in module networkx.readwrite.multiline_adjlist)": [[1376, "networkx.readwrite.multiline_adjlist.parse_multiline_adjlist"]], "read_multiline_adjlist() (in module networkx.readwrite.multiline_adjlist)": [[1377, "networkx.readwrite.multiline_adjlist.read_multiline_adjlist"]], "write_multiline_adjlist() (in module networkx.readwrite.multiline_adjlist)": [[1378, "networkx.readwrite.multiline_adjlist.write_multiline_adjlist"]], "generate_pajek() (in module networkx.readwrite.pajek)": [[1379, "networkx.readwrite.pajek.generate_pajek"]], "parse_pajek() (in module networkx.readwrite.pajek)": [[1380, "networkx.readwrite.pajek.parse_pajek"]], "read_pajek() (in module networkx.readwrite.pajek)": [[1381, "networkx.readwrite.pajek.read_pajek"]], "write_pajek() (in module networkx.readwrite.pajek)": [[1382, "networkx.readwrite.pajek.write_pajek"]], "from_sparse6_bytes() (in module networkx.readwrite.sparse6)": [[1383, "networkx.readwrite.sparse6.from_sparse6_bytes"]], "read_sparse6() (in module networkx.readwrite.sparse6)": [[1384, "networkx.readwrite.sparse6.read_sparse6"]], "to_sparse6_bytes() (in module networkx.readwrite.sparse6)": [[1385, "networkx.readwrite.sparse6.to_sparse6_bytes"]], "write_sparse6() (in module networkx.readwrite.sparse6)": [[1386, "networkx.readwrite.sparse6.write_sparse6"]], "generate_network_text() (in module networkx.readwrite.text)": [[1387, "networkx.readwrite.text.generate_network_text"]], "write_network_text() (in module networkx.readwrite.text)": 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675, 676, 677, 678, 692, 734, 1044, 1151, 1302, 1332, 1365, 1366, 1369, 1370, 1371, 1372, 1402, 1403, 1415, 1421, 1422, 1423, 1430, 1434], "nnode": [7, 39, 187, 188, 590, 854, 877, 878, 899, 920, 921, 935, 959, 960, 981, 1003, 1004], "edge_width": [7, 1045], "mean": [7, 8, 55, 58, 96, 100, 101, 102, 103, 104, 108, 110, 133, 165, 211, 214, 292, 357, 380, 452, 453, 491, 498, 506, 507, 510, 514, 522, 523, 524, 525, 526, 563, 564, 565, 588, 621, 684, 693, 705, 706, 719, 732, 755, 764, 788, 1039, 1088, 1089, 1091, 1115, 1120, 1146, 1156, 1174, 1181, 1191, 1202, 1203, 1204, 1221, 1241, 1301, 1313, 1315, 1318, 1332, 1342, 1402, 1414, 1421, 1423, 1436], "posbm": 7, "xy": [7, 246], "212": 7, "372": [7, 18, 389, 391, 392, 396], "plot_blockmodel": [7, 18], "convert": [8, 35, 51, 53, 55, 56, 57, 58, 59, 75, 76, 100, 103, 106, 113, 170, 267, 268, 294, 377, 466, 567, 568, 617, 678, 681, 852, 897, 933, 936, 979, 982, 1041, 1088, 1100, 1101, 1102, 1172, 1173, 1279, 1287, 1302, 1303, 1305, 1307, 1312, 1316, 1331, 1338, 1339, 1342, 1343, 1344, 1348, 1351, 1352, 1353, 1354, 1355, 1356, 1359, 1362, 1363, 1367, 1368, 1369, 1370, 1376, 1377, 1382, 1385, 1412, 1413, 1415, 1418, 1420, 1421, 1422, 1425, 1430, 1436], "formula": [8, 300, 317, 323, 327, 382, 387, 620, 690, 1430], "can": [8, 16, 25, 35, 39, 41, 44, 53, 55, 56, 57, 58, 59, 68, 70, 71, 72, 76, 77, 85, 89, 92, 93, 94, 95, 96, 97, 100, 101, 102, 103, 104, 106, 108, 111, 112, 113, 116, 126, 133, 142, 143, 144, 145, 152, 153, 157, 158, 159, 166, 169, 172, 177, 181, 185, 186, 190, 191, 194, 200, 201, 208, 221, 223, 225, 228, 230, 231, 232, 239, 240, 241, 244, 252, 261, 262, 263, 265, 279, 282, 283, 298, 299, 302, 303, 306, 307, 308, 309, 310, 316, 317, 325, 326, 327, 331, 332, 334, 335, 339, 341, 342, 344, 346, 347, 348, 349, 350, 351, 355, 356, 359, 360, 363, 364, 376, 378, 382, 384, 385, 387, 389, 390, 391, 392, 396, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 424, 425, 429, 441, 442, 451, 456, 458, 460, 462, 463, 466, 467, 468, 473, 474, 475, 476, 477, 493, 494, 496, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 532, 542, 555, 577, 579, 583, 588, 590, 599, 600, 603, 604, 606, 617, 618, 619, 621, 628, 630, 631, 632, 635, 643, 645, 649, 654, 655, 656, 657, 659, 660, 662, 663, 664, 669, 670, 671, 678, 679, 680, 681, 682, 689, 690, 691, 692, 693, 722, 724, 725, 726, 727, 728, 731, 732, 733, 750, 751, 753, 764, 769, 772, 777, 788, 793, 798, 852, 855, 856, 857, 858, 859, 864, 867, 869, 872, 873, 875, 876, 880, 881, 884, 889, 890, 894, 897, 900, 901, 902, 903, 904, 909, 912, 914, 916, 918, 919, 923, 927, 930, 933, 936, 937, 938, 939, 940, 945, 948, 949, 950, 953, 954, 957, 958, 962, 966, 971, 976, 979, 982, 983, 984, 985, 986, 991, 994, 995, 996, 998, 1001, 1002, 1006, 1010, 1013, 1039, 1040, 1041, 1042, 1043, 1045, 1048, 1050, 1062, 1063, 1064, 1066, 1069, 1071, 1085, 1088, 1091, 1105, 1106, 1108, 1127, 1128, 1129, 1135, 1139, 1141, 1143, 1154, 1157, 1160, 1170, 1171, 1172, 1173, 1180, 1181, 1183, 1199, 1202, 1203, 1204, 1212, 1213, 1223, 1224, 1225, 1228, 1241, 1252, 1254, 1256, 1264, 1269, 1270, 1275, 1278, 1281, 1282, 1284, 1285, 1287, 1288, 1289, 1290, 1301, 1302, 1303, 1305, 1307, 1308, 1309, 1326, 1327, 1329, 1330, 1332, 1334, 1335, 1336, 1339, 1340, 1353, 1355, 1358, 1360, 1362, 1363, 1368, 1369, 1377, 1378, 1384, 1386, 1387, 1388, 1390, 1393, 1395, 1396, 1401, 1402, 1403, 1404, 1405, 1408, 1411, 1413, 1414, 1415, 1417, 1418, 1421, 1434, 1436], "more": [8, 44, 54, 68, 87, 93, 94, 95, 98, 100, 101, 102, 103, 104, 106, 108, 110, 111, 112, 115, 116, 122, 128, 129, 144, 166, 173, 199, 200, 203, 205, 216, 217, 219, 220, 221, 222, 231, 232, 236, 257, 268, 278, 279, 282, 290, 300, 311, 315, 325, 326, 337, 340, 363, 380, 385, 387, 389, 391, 392, 394, 401, 407, 408, 409, 424, 429, 430, 434, 435, 439, 462, 466, 482, 522, 523, 561, 562, 583, 584, 585, 592, 595, 616, 621, 628, 633, 637, 655, 658, 662, 663, 664, 678, 681, 685, 693, 700, 701, 705, 713, 719, 720, 737, 739, 750, 762, 784, 788, 798, 864, 870, 888, 889, 892, 893, 909, 915, 926, 927, 928, 929, 945, 951, 970, 971, 974, 975, 991, 997, 1009, 1010, 1011, 1012, 1040, 1042, 1043, 1045, 1046, 1074, 1097, 1103, 1119, 1122, 1123, 1126, 1136, 1137, 1138, 1139, 1141, 1143, 1144, 1145, 1146, 1147, 1148, 1149, 1191, 1198, 1199, 1212, 1220, 1223, 1224, 1225, 1278, 1293, 1294, 1301, 1302, 1303, 1329, 1332, 1334, 1343, 1351, 1354, 1355, 1356, 1387, 1398, 1403, 1404, 1406, 1407, 1408, 1410, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434, 1435, 1436], "express": [8, 93, 111, 185, 316, 331, 332, 385, 386, 620, 621, 875, 918, 957, 1001, 1205, 1293, 1332], "than": [8, 11, 35, 44, 56, 98, 100, 102, 103, 104, 116, 129, 143, 144, 145, 162, 200, 215, 216, 217, 219, 220, 222, 228, 232, 236, 242, 257, 278, 279, 282, 289, 290, 298, 299, 300, 305, 307, 308, 311, 312, 316, 317, 322, 325, 326, 328, 330, 331, 332, 343, 354, 360, 363, 376, 382, 383, 385, 386, 387, 389, 391, 392, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 427, 428, 431, 437, 466, 470, 471, 502, 529, 539, 561, 562, 583, 584, 585, 592, 627, 628, 637, 638, 654, 655, 658, 660, 661, 675, 678, 680, 681, 683, 685, 688, 692, 694, 695, 696, 700, 701, 713, 733, 737, 739, 750, 754, 763, 788, 889, 927, 949, 971, 995, 1010, 1041, 1045, 1046, 1063, 1105, 1141, 1152, 1160, 1168, 1171, 1173, 1178, 1180, 1191, 1193, 1200, 1204, 1232, 1236, 1237, 1242, 1243, 1244, 1245, 1281, 1282, 1302, 1303, 1332, 1334, 1351, 1354, 1355, 1356, 1359, 1360, 1364, 1371, 1372, 1385, 1390, 1404, 1411, 1413, 1414, 1417, 1422, 1432, 1434], "worst": [8, 211, 212, 213, 222, 229, 236, 265, 294, 295, 340, 347, 348, 349, 442, 515, 517, 518, 519, 520], "reus": [8, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 1137, 1138, 1144, 1145, 1146, 1147, 1148, 1334, 1411], "subcircuit": 8, "multipl": [8, 11, 26, 41, 46, 78, 94, 95, 100, 104, 108, 110, 144, 158, 159, 167, 176, 189, 196, 208, 288, 312, 359, 387, 388, 425, 445, 449, 460, 462, 466, 487, 488, 489, 596, 597, 599, 617, 618, 643, 645, 680, 692, 693, 699, 707, 740, 764, 788, 798, 858, 859, 865, 871, 879, 886, 894, 903, 904, 910, 925, 930, 939, 940, 946, 948, 952, 961, 962, 965, 966, 968, 976, 985, 986, 992, 994, 1005, 1006, 1008, 1013, 1040, 1042, 1043, 1048, 1049, 1105, 1106, 1108, 1127, 1129, 1133, 1141, 1143, 1222, 1223, 1225, 1291, 1297, 1302, 1304, 1332, 1358, 1384, 1402, 1414, 1415, 1421, 1422, 1426, 1434, 1436], "wherea": [8, 104, 684, 764, 788, 793, 1171, 1426], "cannot": [8, 102, 104, 128, 133, 200, 233, 301, 364, 396, 478, 583, 584, 585, 586, 634, 724, 889, 927, 936, 971, 982, 1010, 1046, 1171, 1214, 1215, 1302, 1304, 1308, 1309, 1332, 1351, 1353, 1354, 1355, 1356], "subformula": 8, "onc": [8, 39, 55, 56, 89, 94, 95, 100, 101, 113, 128, 200, 228, 231, 232, 233, 247, 248, 362, 376, 382, 390, 424, 425, 430, 490, 493, 494, 583, 584, 585, 654, 680, 681, 719, 720, 889, 927, 971, 1010, 1049, 1069, 1090, 1223, 1317, 1332, 1387, 1412, 1416], "thu": [8, 89, 102, 104, 116, 216, 217, 221, 257, 259, 333, 420, 421, 429, 430, 464, 479, 502, 514, 585, 681, 700, 701, 762, 764, 798, 1040, 1042, 1043, 1046, 1090, 1115, 1154, 1221, 1223, 1240, 1284, 1285, 1302, 1334, 1411, 1414, 1416, 1434], "wai": [8, 28, 53, 54, 56, 76, 87, 89, 94, 98, 100, 101, 102, 103, 104, 105, 106, 108, 111, 113, 116, 133, 153, 158, 159, 166, 185, 227, 282, 298, 299, 316, 332, 339, 358, 590, 600, 617, 620, 680, 693, 732, 762, 793, 798, 856, 858, 859, 864, 875, 901, 903, 904, 909, 917, 918, 937, 939, 940, 945, 957, 983, 985, 986, 991, 999, 1001, 1040, 1042, 1043, 1044, 1100, 1171, 1219, 1221, 1223, 1245, 1268, 1275, 1278, 1332, 1334, 1336, 1387, 1402, 1403, 1413, 1415, 1420, 1436], "infeas": [8, 424], "circuit_to_formula": 8, "dag_to_branch": [8, 760, 1417], "transfer": [8, 203, 205, 231, 232, 471, 892, 893, 928, 929, 974, 975, 1011, 1012, 1429], "oper": [8, 31, 53, 96, 102, 113, 116, 169, 185, 190, 228, 376, 425, 462, 548, 549, 550, 554, 555, 556, 579, 597, 600, 603, 673, 674, 675, 676, 681, 682, 760, 788, 867, 875, 880, 912, 918, 948, 957, 962, 994, 1001, 1039, 1071, 1091, 1106, 1170, 1224, 1225, 1301, 1308, 1325, 1329, 1331, 1332, 1402, 1403, 1409, 1413, 1414, 1415, 1416, 1417, 1420, 1421, 1422, 1423, 1426], "variabl": [8, 95, 133, 375, 532, 542, 620, 621, 734, 798, 1040, 1041, 1042, 1043, 1045, 1127, 1129, 1160, 1171, 1332, 1417, 1421, 1422, 1423, 1429], "formula_to_str": 8, "_to_str": 8, "root": [8, 68, 85, 294, 295, 340, 389, 391, 392, 396, 451, 462, 561, 579, 611, 673, 675, 680, 706, 730, 732, 741, 762, 793, 1122, 1123, 1131, 1132, 1151, 1153, 1241, 1277, 1278, 1329, 1371, 1372, 1402, 1415, 1416, 1417, 1421, 1422, 1432, 1434], "children": [8, 462, 579, 1151, 1161, 1278, 1371, 1372, 1387], "otherwis": [8, 93, 111, 147, 150, 172, 179, 185, 186, 199, 218, 231, 250, 251, 285, 298, 299, 304, 307, 308, 312, 316, 317, 323, 324, 325, 326, 327, 328, 331, 332, 345, 355, 360, 395, 396, 397, 398, 399, 400, 412, 413, 414, 420, 421, 424, 427, 428, 464, 465, 466, 472, 481, 490, 492, 496, 497, 498, 500, 501, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 523, 557, 564, 565, 570, 574, 576, 586, 588, 590, 599, 603, 618, 620, 621, 635, 665, 675, 689, 690, 691, 698, 700, 701, 736, 737, 738, 739, 753, 850, 869, 875, 876, 888, 895, 914, 918, 919, 926, 931, 936, 950, 957, 958, 970, 977, 982, 996, 1001, 1002, 1009, 1071, 1094, 1127, 1141, 1143, 1171, 1191, 1203, 1223, 1276, 1288, 1289, 1290, 1313, 1315, 1318, 1348, 1362, 1363, 1382, 1387, 1388, 1418, 1422, 1436], "child": [8, 1153, 1278, 1387], "must": [8, 11, 94, 95, 96, 100, 101, 104, 111, 152, 153, 159, 162, 172, 205, 207, 208, 215, 216, 217, 220, 231, 232, 233, 253, 254, 258, 259, 260, 261, 262, 263, 265, 268, 269, 270, 272, 274, 277, 282, 286, 298, 299, 307, 308, 316, 317, 318, 319, 320, 325, 326, 329, 331, 332, 344, 363, 364, 365, 380, 384, 387, 393, 412, 413, 414, 415, 427, 431, 442, 473, 474, 475, 476, 477, 547, 548, 549, 550, 551, 552, 553, 555, 557, 558, 559, 560, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 579, 580, 581, 582, 586, 587, 588, 589, 590, 591, 595, 599, 601, 603, 604, 605, 606, 617, 628, 629, 634, 635, 637, 638, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 673, 674, 675, 676, 682, 692, 694, 700, 701, 709, 723, 736, 737, 738, 739, 791, 798, 855, 856, 859, 869, 893, 894, 900, 901, 904, 914, 930, 936, 940, 975, 976, 982, 986, 1013, 1040, 1041, 1042, 1043, 1066, 1074, 1088, 1105, 1139, 1143, 1152, 1168, 1171, 1179, 1182, 1192, 1194, 1196, 1199, 1203, 1205, 1215, 1219, 1223, 1225, 1241, 1245, 1246, 1276, 1281, 1282, 1283, 1284, 1285, 1301, 1302, 1304, 1313, 1315, 1316, 1317, 1318, 1321, 1339, 1343, 1344, 1345, 1346, 1365, 1367, 1368, 1369, 1370, 1371, 1372, 1382, 1402, 1403, 1404, 1416, 1436], "NOT": [8, 111, 200, 551, 552, 553, 750, 889, 927, 971, 1010], "util": [8, 15, 37, 45, 46, 94, 98, 103, 104, 230, 231, 232, 317, 376, 425, 427, 428, 431, 462, 498, 680, 681, 760, 1047, 1127, 1248, 1305, 1307, 1309, 1316, 1325, 1326, 1327, 1331, 1411, 1415, 1416, 1420, 1422, 1425, 1428, 1434], "arbitrary_el": [8, 1401, 1422], "nb": [8, 1337, 1340], "left": [8, 72, 116, 184, 312, 313, 323, 325, 326, 387, 561, 562, 586, 618, 690, 691, 741, 1109, 1140, 1142, 1152, 1185, 1212, 1286, 1361, 1364, 1387, 1413], "right": [8, 72, 111, 112, 116, 153, 207, 323, 327, 387, 429, 430, 502, 561, 562, 586, 587, 589, 590, 617, 618, 690, 691, 741, 856, 937, 983, 1140, 1142, 1152, 1161, 1163, 1185, 1212, 1219, 1221, 1276, 1286, 1387, 1388], "littl": [8, 95, 106, 299, 308], "mislead": 8, "That": [8, 98, 106, 133, 166, 213, 222, 228, 296, 387, 438, 467, 527, 537, 557, 590, 659, 673, 674, 675, 676, 693, 706, 719, 793, 864, 909, 945, 991, 1049, 1168, 1216, 1302, 1396, 1413, 1418], "okai": 8, "becaus": [8, 11, 55, 70, 95, 100, 102, 103, 104, 113, 133, 162, 216, 217, 221, 256, 312, 380, 389, 391, 392, 396, 413, 414, 429, 496, 500, 501, 502, 512, 571, 587, 589, 617, 618, 634, 654, 936, 982, 1041, 1242, 1279, 1302, 1309, 1332, 1351, 1356, 1413, 1416, 1425, 1434], "AND": [8, 111, 600, 750, 764], "OR": [8, 111, 158, 176, 189, 858, 871, 879, 903, 939, 949, 952, 961, 985, 995], "symmetr": [8, 146, 149, 238, 547, 588, 595, 763, 1179, 1198, 1241, 1252, 1256, 1257, 1262, 1264, 1275, 1326, 1327, 1395], "It": [8, 53, 57, 59, 93, 94, 95, 98, 100, 102, 103, 105, 108, 111, 113, 116, 133, 173, 185, 208, 215, 216, 217, 230, 231, 232, 250, 261, 262, 263, 265, 279, 311, 317, 325, 326, 328, 345, 348, 349, 353, 355, 414, 416, 417, 418, 419, 420, 421, 431, 440, 442, 454, 459, 466, 482, 498, 502, 510, 532, 542, 547, 561, 562, 567, 568, 569, 584, 590, 596, 597, 600, 602, 603, 617, 621, 630, 631, 632, 654, 660, 661, 665, 673, 676, 694, 719, 720, 721, 762, 763, 764, 793, 798, 870, 875, 894, 915, 918, 930, 951, 957, 976, 997, 1001, 1013, 1015, 1016, 1021, 1040, 1041, 1042, 1043, 1057, 1120, 1127, 1129, 1176, 1180, 1206, 1207, 1212, 1213, 1216, 1223, 1229, 1233, 1240, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256, 1257, 1259, 1260, 1264, 1267, 1269, 1270, 1275, 1281, 1282, 1283, 1286, 1302, 1303, 1329, 1330, 1332, 1334, 1349, 1390, 1391, 1402, 1404, 1407, 1411, 1413, 1416, 1417, 1418, 1420, 1421, 1422, 1436], "just": [8, 100, 103, 104, 105, 106, 185, 200, 340, 376, 441, 466, 561, 562, 579, 662, 663, 664, 694, 793, 875, 889, 918, 927, 948, 957, 962, 971, 994, 1001, 1010, 1045, 1123, 1128, 1132, 1235, 1284, 1285, 1302, 1334, 1402, 1413, 1415], "operand": 8, "predict": [8, 569, 570, 571, 572, 573, 574, 575, 576, 593, 594, 760, 1331, 1411, 1415, 1421], "henc": [8, 169, 190, 523, 867, 880, 912, 948, 962, 994, 1062, 1127, 1128, 1129, 1208, 1391], "doe": [8, 78, 94, 95, 100, 102, 103, 104, 105, 115, 116, 133, 148, 154, 155, 166, 169, 190, 208, 209, 228, 229, 230, 231, 232, 233, 294, 309, 341, 342, 344, 345, 354, 359, 375, 384, 387, 412, 416, 428, 452, 471, 496, 497, 498, 499, 500, 501, 502, 504, 505, 508, 509, 511, 512, 513, 514, 536, 546, 551, 552, 553, 566, 568, 585, 586, 588, 591, 603, 614, 628, 629, 680, 693, 695, 696, 700, 701, 719, 720, 723, 724, 725, 726, 727, 728, 764, 864, 867, 880, 894, 909, 912, 930, 945, 948, 962, 976, 991, 994, 1013, 1041, 1046, 1069, 1073, 1075, 1084, 1105, 1106, 1108, 1109, 1110, 1112, 1117, 1179, 1181, 1183, 1198, 1213, 1228, 1229, 1233, 1235, 1240, 1247, 1302, 1306, 1309, 1332, 1339, 1340, 1347, 1348, 1350, 1357, 1359, 1360, 1361, 1362, 1363, 1364, 1377, 1385, 1386, 1389, 1391, 1402, 1413, 1414, 1415, 1419, 1426, 1436], "necessarili": [8, 100, 343, 453, 485, 561, 562, 643, 645, 1041, 1225], "behav": [8, 89, 104, 160, 191, 201, 221, 353, 860, 881, 890, 905, 941, 963, 972, 987, 1235, 1302, 1404, 1413], "everi": [8, 11, 58, 89, 94, 110, 113, 121, 145, 158, 162, 178, 212, 213, 221, 222, 230, 231, 232, 236, 244, 265, 288, 296, 301, 325, 326, 345, 354, 382, 399, 439, 441, 442, 452, 464, 473, 474, 475, 476, 477, 479, 485, 486, 493, 514, 518, 567, 608, 616, 617, 621, 634, 635, 637, 638, 665, 687, 689, 690, 719, 720, 793, 858, 903, 939, 985, 1055, 1056, 1057, 1073, 1074, 1075, 1088, 1089, 1105, 1106, 1108, 1109, 1110, 1111, 1112, 1113, 1114, 1117, 1118, 1119, 1120, 1154, 1168, 1201, 1222, 1223, 1263, 1270, 1284, 1285, 1302, 1416], "left_subformula": 8, "right_subformula": 8, "in_degre": [8, 167, 189, 493, 680, 865, 879, 946, 961, 1183, 1213, 1214, 1413, 1415, 1416, 1436], "ha": [8, 11, 17, 45, 68, 89, 92, 94, 95, 96, 98, 100, 101, 102, 103, 104, 106, 108, 111, 113, 117, 121, 128, 153, 162, 166, 167, 174, 175, 176, 185, 189, 199, 208, 213, 215, 216, 220, 221, 227, 228, 230, 231, 232, 233, 236, 239, 240, 241, 242, 243, 244, 245, 248, 250, 253, 270, 272, 273, 274, 275, 276, 277, 283, 290, 292, 294, 295, 296, 301, 306, 311, 325, 327, 333, 345, 354, 357, 358, 365, 366, 367, 375, 380, 382, 383, 385, 386, 387, 388, 393, 395, 396, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 426, 429, 430, 431, 441, 452, 460, 462, 468, 469, 470, 473, 474, 475, 476, 477, 478, 479, 482, 493, 494, 495, 496, 497, 498, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 524, 566, 568, 579, 580, 583, 592, 595, 607, 609, 612, 613, 624, 625, 626, 630, 631, 632, 634, 635, 636, 637, 638, 640, 648, 649, 651, 654, 659, 660, 684, 690, 692, 694, 699, 713, 719, 720, 731, 732, 733, 741, 751, 788, 793, 856, 864, 865, 871, 875, 879, 888, 894, 901, 909, 910, 918, 926, 930, 937, 945, 946, 950, 952, 957, 961, 970, 976, 983, 991, 992, 996, 1001, 1009, 1013, 1043, 1046, 1048, 1069, 1071, 1073, 1075, 1078, 1083, 1087, 1101, 1102, 1104, 1105, 1106, 1108, 1125, 1136, 1151, 1160, 1166, 1168, 1171, 1182, 1186, 1191, 1199, 1201, 1202, 1203, 1204, 1205, 1213, 1216, 1217, 1221, 1223, 1228, 1240, 1245, 1249, 1250, 1254, 1255, 1260, 1265, 1267, 1270, 1273, 1275, 1276, 1278, 1281, 1282, 1283, 1284, 1285, 1287, 1288, 1289, 1290, 1291, 1292, 1295, 1297, 1299, 1302, 1306, 1332, 1334, 1336, 1339, 1340, 1359, 1360, 1377, 1378, 1385, 1387, 1390, 1402, 1403, 1404, 1407, 1412, 1413, 1414, 1415, 1416, 1418, 1422, 1423, 1425, 1432, 1434], "output": [8, 14, 17, 90, 94, 102, 103, 104, 110, 198, 288, 289, 347, 376, 382, 496, 500, 501, 511, 512, 577, 590, 679, 680, 693, 724, 1048, 1199, 1203, 1205, 1275, 1302, 1332, 1340, 1347, 1350, 1361, 1364, 1388, 1408, 1411, 1413, 1415, 1420, 1422, 1423, 1435, 1436], "two": [8, 11, 13, 17, 28, 35, 39, 44, 55, 56, 58, 59, 66, 68, 72, 89, 94, 96, 100, 101, 103, 106, 110, 113, 115, 116, 121, 133, 152, 172, 176, 185, 186, 189, 203, 208, 212, 213, 214, 215, 216, 217, 218, 221, 222, 227, 228, 231, 232, 233, 246, 250, 252, 253, 254, 258, 259, 261, 262, 263, 266, 270, 271, 272, 273, 274, 275, 276, 277, 283, 286, 287, 288, 290, 306, 312, 316, 317, 323, 328, 331, 332, 339, 343, 345, 347, 353, 354, 360, 361, 379, 382, 383, 385, 393, 413, 414, 421, 425, 430, 431, 432, 433, 444, 445, 446, 447, 449, 454, 455, 456, 459, 464, 473, 474, 475, 476, 477, 478, 482, 493, 496, 500, 501, 502, 504, 505, 508, 510, 511, 512, 513, 523, 547, 551, 552, 553, 557, 561, 562, 563, 564, 565, 566, 567, 568, 570, 571, 574, 576, 580, 586, 587, 588, 589, 590, 595, 600, 607, 609, 610, 612, 613, 617, 621, 628, 629, 631, 634, 635, 637, 638, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 673, 674, 675, 676, 677, 678, 682, 694, 696, 733, 734, 740, 741, 762, 763, 764, 782, 788, 793, 798, 855, 869, 871, 875, 876, 879, 892, 894, 900, 914, 918, 919, 928, 930, 936, 948, 950, 952, 957, 958, 961, 962, 974, 976, 982, 994, 996, 1001, 1002, 1011, 1013, 1022, 1023, 1024, 1025, 1039, 1040, 1042, 1043, 1059, 1087, 1091, 1101, 1103, 1104, 1109, 1110, 1111, 1112, 1117, 1119, 1140, 1152, 1153, 1155, 1157, 1158, 1162, 1180, 1191, 1192, 1199, 1200, 1201, 1202, 1203, 1204, 1205, 1210, 1213, 1216, 1217, 1221, 1223, 1224, 1249, 1250, 1259, 1277, 1278, 1281, 1282, 1300, 1301, 1302, 1329, 1330, 1332, 1334, 1365, 1366, 1369, 1402, 1403, 1404, 1406, 1411, 1413, 1414, 1415, 1416, 1419, 1420, 1422, 1434], "layer": [8, 37, 56, 62, 68, 104, 440, 707, 1041, 1112, 1429], "third": [8, 103, 106, 115, 250, 424, 469, 587, 589, 736, 738, 1223, 1232, 1268, 1269, 1332, 1416], "appear": [8, 84, 94, 96, 100, 101, 103, 180, 205, 231, 232, 239, 244, 247, 248, 278, 365, 366, 367, 380, 453, 454, 455, 457, 468, 472, 586, 587, 589, 590, 677, 681, 709, 732, 736, 738, 893, 975, 1039, 1045, 1091, 1105, 1142, 1156, 1158, 1160, 1163, 1165, 1193, 1194, 1283, 1288, 1329, 1330, 1351, 1354, 1355, 1356, 1390, 1416, 1422, 1423], "both": [8, 53, 56, 93, 94, 95, 101, 102, 103, 104, 116, 162, 165, 205, 215, 216, 217, 218, 241, 258, 259, 260, 265, 283, 287, 288, 290, 339, 360, 381, 385, 417, 419, 420, 421, 425, 429, 442, 472, 504, 508, 547, 577, 583, 600, 602, 603, 604, 605, 606, 607, 608, 609, 612, 613, 617, 623, 637, 638, 655, 656, 657, 678, 713, 722, 762, 763, 764, 784, 893, 975, 1023, 1039, 1069, 1078, 1083, 1087, 1091, 1100, 1123, 1132, 1150, 1171, 1195, 1198, 1205, 1213, 1216, 1217, 1219, 1221, 1288, 1302, 1332, 1334, 1364, 1369, 1370, 1395, 1402, 1404, 1411, 1422, 1425, 1426, 1434, 1436], "negat": 8, "sole": [8, 788, 1284, 1285, 1332], "fourth": [8, 231, 232, 1332, 1413], "digraph": [8, 10, 11, 17, 22, 26, 42, 46, 57, 62, 68, 70, 71, 83, 89, 102, 103, 116, 133, 152, 153, 157, 158, 159, 161, 163, 164, 166, 167, 169, 171, 172, 173, 176, 177, 186, 187, 188, 189, 190, 193, 194, 195, 196, 197, 199, 200, 203, 205, 208, 209, 217, 228, 230, 231, 232, 241, 247, 248, 300, 309, 315, 319, 320, 322, 329, 330, 336, 337, 338, 339, 341, 342, 344, 345, 390, 393, 395, 398, 399, 400, 401, 403, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 432, 433, 439, 452, 454, 455, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 483, 484, 494, 496, 497, 498, 499, 500, 501, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 515, 516, 520, 521, 525, 557, 568, 577, 578, 579, 590, 592, 615, 617, 625, 632, 638, 645, 646, 654, 658, 659, 660, 661, 665, 680, 690, 692, 695, 698, 699, 700, 701, 702, 703, 704, 708, 709, 710, 711, 713, 718, 719, 720, 721, 723, 724, 725, 726, 727, 728, 742, 743, 746, 747, 748, 749, 750, 751, 752, 754, 762, 791, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 906, 907, 908, 909, 910, 913, 914, 915, 917, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 937, 938, 939, 940, 942, 943, 944, 945, 951, 959, 960, 966, 967, 968, 969, 970, 971, 975, 976, 977, 978, 980, 981, 983, 984, 985, 986, 988, 989, 990, 991, 992, 997, 999, 1003, 1004, 1006, 1007, 1008, 1009, 1010, 1013, 1038, 1040, 1041, 1042, 1043, 1044, 1045, 1055, 1065, 1069, 1073, 1075, 1078, 1083, 1086, 1087, 1101, 1102, 1104, 1121, 1141, 1156, 1160, 1174, 1175, 1176, 1179, 1183, 1184, 1186, 1188, 1189, 1190, 1191, 1195, 1223, 1276, 1278, 1279, 1280, 1289, 1290, 1293, 1296, 1298, 1304, 1329, 1332, 1339, 1343, 1348, 1362, 1363, 1368, 1371, 1372, 1377, 1387, 1388, 1402, 1408, 1410, 1411, 1413, 1414, 1415, 1416, 1417, 1418, 1420, 1421, 1422, 1423, 1425, 1426, 1433, 1434, 1436], "add_nod": [8, 11, 27, 35, 70, 75, 90, 103, 158, 185, 247, 341, 342, 400, 424, 493, 494, 498, 506, 507, 510, 524, 525, 607, 609, 612, 613, 693, 798, 858, 875, 903, 918, 939, 957, 985, 1001, 1040, 1042, 1043, 1089, 1281, 1332, 1351, 1416, 1417, 1426, 1436], "get_node_attribut": [8, 40, 45, 72, 1219, 1413], "600": [8, 10, 12], "font_siz": [8, 17, 22, 26, 33, 36, 39, 46, 47, 1139, 1140, 1142], "22": [8, 36, 65, 67, 327, 348, 385, 386, 1277, 1329, 1412, 1417, 1421, 1431], "multipartite_layout": [8, 37, 62, 68, 1421, 1423, 1429], "subset_kei": [8, 37, 62, 68, 1112], "equal": [8, 37, 82, 145, 215, 216, 217, 231, 232, 239, 270, 272, 274, 277, 289, 298, 299, 301, 304, 307, 308, 311, 312, 313, 316, 317, 321, 324, 325, 326, 331, 332, 333, 375, 412, 413, 414, 415, 420, 421, 430, 473, 476, 478, 493, 496, 497, 498, 500, 501, 504, 505, 506, 507, 508, 509, 510, 511, 512, 527, 537, 547, 554, 555, 556, 557, 570, 574, 607, 625, 659, 673, 674, 675, 676, 689, 690, 691, 692, 723, 724, 742, 743, 755, 763, 793, 1115, 1119, 1168, 1171, 1204, 1210, 1236, 1245, 1277, 1286, 1297, 1313, 1315, 1318, 1407, 1408], "108": [8, 11, 18, 1222], "plot_circuit": [8, 18], "southern": [9, 1271], "women": [9, 1271, 1407, 1415], "unipartit": [9, 116, 259, 260, 360], "properti": [9, 11, 19, 23, 34, 64, 87, 102, 103, 104, 113, 135, 160, 162, 167, 169, 176, 177, 180, 185, 189, 190, 191, 201, 285, 286, 287, 288, 289, 327, 365, 366, 367, 390, 478, 502, 547, 571, 621, 687, 860, 865, 867, 871, 872, 875, 879, 880, 881, 890, 905, 910, 912, 918, 941, 946, 948, 952, 953, 957, 961, 962, 963, 972, 987, 992, 994, 1001, 1088, 1089, 1125, 1140, 1142, 1199, 1208, 1223, 1225, 1275, 1289, 1290, 1332, 1334, 1391, 1407, 1414, 1415, 1416, 1417, 1422, 1426, 1436], "These": [9, 53, 59, 74, 80, 87, 94, 95, 106, 338, 387, 496, 514, 561, 673, 675, 734, 750, 781, 788, 1041, 1048, 1050, 1329, 1332, 1393, 1395, 1401, 1403, 1404, 1406, 1408, 1413, 1414, 1420, 1436], "were": [9, 66, 89, 100, 102, 105, 216, 217, 221, 290, 306, 412, 439, 462, 590, 965, 1005, 1205, 1402, 1404, 1408, 1411, 1414, 1415, 1416, 1422, 1425], "et": [9, 211, 227, 228, 316, 317, 323, 332, 336, 339, 347, 354, 360, 375, 382, 383, 425, 427, 428, 453, 571, 593, 594, 683, 684, 686, 695, 1208], "al": [9, 211, 227, 228, 316, 317, 323, 332, 336, 339, 347, 354, 360, 375, 382, 383, 425, 427, 428, 453, 571, 593, 594, 683, 684, 686, 695, 1208, 1416, 1422], "1930": [9, 1405], "thei": [9, 55, 59, 66, 72, 93, 94, 95, 98, 100, 101, 102, 103, 104, 105, 106, 108, 133, 152, 166, 208, 214, 221, 250, 286, 288, 289, 297, 298, 299, 302, 303, 307, 308, 309, 310, 353, 364, 376, 393, 398, 429, 453, 454, 455, 456, 466, 467, 473, 474, 475, 476, 477, 498, 506, 507, 510, 514, 548, 549, 550, 561, 562, 578, 585, 588, 590, 602, 606, 677, 678, 706, 719, 752, 762, 788, 855, 864, 894, 900, 909, 930, 936, 945, 965, 976, 982, 991, 1005, 1013, 1039, 1041, 1069, 1088, 1091, 1112, 1123, 1127, 1128, 1129, 1132, 1139, 1141, 1143, 1157, 1165, 1171, 1199, 1203, 1204, 1223, 1277, 1278, 1329, 1334, 1359, 1360, 1362, 1363, 1365, 1369, 1403, 1405, 1411, 1413, 1415, 1418, 1423, 1436], "repres": [9, 11, 27, 44, 53, 55, 58, 68, 93, 100, 108, 116, 231, 232, 266, 282, 284, 287, 288, 289, 292, 293, 340, 352, 363, 364, 365, 379, 380, 382, 383, 384, 387, 388, 393, 450, 454, 455, 457, 459, 462, 467, 468, 496, 497, 500, 501, 502, 504, 505, 508, 509, 511, 512, 523, 567, 579, 580, 581, 582, 588, 590, 611, 617, 620, 621, 658, 662, 666, 669, 678, 681, 693, 694, 697, 699, 700, 701, 702, 704, 730, 732, 733, 736, 738, 741, 754, 788, 793, 798, 1022, 1023, 1024, 1025, 1040, 1041, 1042, 1043, 1048, 1084, 1105, 1146, 1157, 1191, 1199, 1200, 1202, 1203, 1204, 1205, 1215, 1223, 1246, 1249, 1252, 1256, 1264, 1273, 1275, 1278, 1279, 1284, 1285, 1329, 1330, 1332, 1335, 1336, 1352, 1353, 1387, 1388, 1396, 1402, 1415], "observ": [9, 14, 133, 224, 1423, 1436], "attend": 9, "14": [9, 11, 17, 20, 26, 39, 45, 60, 65, 67, 72, 230, 231, 232, 348, 385, 386, 407, 408, 503, 621, 692, 1156, 1248, 1256, 1268, 1415, 1417, 1436], "event": [9, 26, 100, 101, 111, 1171, 1235, 1306], "18": [9, 45, 65, 67, 94, 325, 326, 347, 348, 385, 386, 620, 1175, 1255, 1261, 1264, 1266, 1269, 1275, 1402, 1415, 1425, 1426, 1430, 1436], "bipartit": [9, 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, 291, 352, 353, 360, 379, 441, 442, 445, 583, 590, 760, 1046, 1109, 1157, 1209, 1210, 1211, 1271, 1331, 1404, 1407, 1408, 1409, 1410, 1415, 1416, 1420, 1422, 1426, 1430, 1434], "biadjac": [9, 283, 284, 1409, 1415], "7": [9, 12, 13, 15, 20, 26, 36, 45, 47, 64, 65, 66, 67, 69, 90, 100, 102, 103, 116, 126, 152, 159, 171, 172, 193, 208, 233, 269, 298, 300, 315, 323, 329, 334, 335, 341, 342, 344, 348, 364, 376, 382, 393, 405, 412, 415, 416, 417, 425, 426, 427, 428, 443, 447, 448, 485, 498, 503, 510, 513, 514, 557, 583, 588, 620, 621, 632, 654, 660, 665, 673, 676, 682, 697, 705, 708, 709, 710, 732, 749, 752, 763, 798, 855, 859, 868, 869, 883, 894, 900, 904, 913, 914, 917, 922, 930, 936, 940, 949, 976, 982, 986, 995, 999, 1013, 1040, 1042, 1043, 1045, 1055, 1056, 1088, 1103, 1107, 1154, 1218, 1248, 1254, 1256, 1257, 1261, 1264, 1266, 1279, 1329, 1332, 1336, 1345, 1346, 1351, 1354, 1355, 1356, 1388, 1390, 1401, 1403, 1411, 1412, 1414, 1417, 1418, 1419, 1420, 1421, 1422, 1434, 1436], "12": [9, 11, 20, 26, 45, 51, 56, 59, 65, 66, 67, 90, 92, 94, 230, 231, 232, 266, 347, 348, 382, 383, 394, 401, 407, 408, 409, 451, 488, 503, 518, 570, 574, 576, 608, 618, 1055, 1056, 1057, 1139, 1142, 1156, 1250, 1251, 1255, 1260, 1263, 1269, 1341, 1415, 1417, 1421, 1436], "9": [9, 11, 12, 13, 20, 26, 36, 45, 47, 64, 65, 66, 67, 69, 83, 90, 102, 103, 112, 116, 126, 233, 294, 296, 341, 342, 344, 348, 349, 358, 376, 382, 407, 408, 426, 440, 451, 496, 498, 503, 506, 507, 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"friend": [9, 547, 1416, 1421], "member": [9, 93, 94, 95, 101, 113, 316, 318, 319, 320, 332, 393, 485, 486, 588, 693, 1228, 1273, 1412], "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, 133], "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, 95, 1171, 1202, 1203, 1204], "50": [9, 26, 31, 35, 41, 51, 55, 56, 57, 58, 65, 66, 273, 313, 1120, 1199, 1203, 1204, 1257, 1303, 1308], "45": [9, 59, 65, 111, 227, 301, 411, 1181], "57": [9, 65], "46": [9, 65, 236, 566, 621, 1270], "24": [9, 20, 38, 65, 67, 69, 104, 348, 385, 386, 498, 507, 510, 705, 1218, 1235, 1250, 1268, 1277, 1412], "32": [9, 65, 67, 69, 210, 212, 213, 348, 385, 386, 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"summar": [10, 16, 101, 102, 692, 693, 760, 793, 1331, 1334, 1387, 1422], "dedensifi": [10, 760], "threshold": [10, 58, 84, 113, 221, 230, 232, 382, 383, 692, 694, 697, 698, 760, 788, 1120, 1199, 1200, 1202, 1203, 1204, 1331, 1407, 1415, 1416, 1417, 1421, 1423], "copi": [10, 17, 39, 45, 94, 96, 107, 168, 197, 200, 203, 204, 205, 206, 285, 286, 287, 288, 289, 343, 390, 392, 394, 408, 435, 436, 437, 438, 439, 455, 462, 471, 523, 586, 587, 589, 598, 601, 604, 605, 607, 608, 609, 612, 613, 615, 616, 635, 638, 692, 866, 887, 889, 892, 893, 911, 927, 928, 929, 947, 966, 969, 971, 974, 975, 993, 1006, 1010, 1011, 1012, 1038, 1041, 1060, 1064, 1066, 1069, 1085, 1086, 1125, 1189, 1195, 1223, 1229, 1233, 1257, 1276, 1300, 1301, 1302, 1412, 1413, 1415, 1416, 1417, 1418, 1421, 1422, 1431, 1434], "nonexp_node_color": 10, "nonexp_node_s": 10, "yellow": [10, 16, 600, 762, 1436], "nonexp_po": 10, "75": [10, 35, 240, 261, 300, 315, 357, 358, 388, 684, 1175, 1176, 1177, 1179, 1413, 1417, 1436], 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501, 504, 505, 508, 509, 511, 512, 516, 520, 1183, 1185, 1198, 1228], "end": [11, 26, 37, 53, 96, 102, 107, 154, 155, 207, 216, 228, 268, 269, 301, 334, 335, 344, 373, 374, 429, 616, 620, 621, 628, 629, 633, 634, 636, 637, 638, 641, 642, 652, 653, 654, 655, 656, 657, 662, 666, 669, 679, 680, 682, 736, 738, 1041, 1045, 1064, 1069, 1078, 1083, 1085, 1087, 1120, 1127, 1139, 1141, 1158, 1171, 1212, 1235, 1332, 1339, 1340, 1343, 1344, 1345, 1346, 1348, 1350, 1356, 1359, 1363, 1364, 1374, 1377, 1378, 1381, 1382, 1385, 1388, 1413, 1422], "In": [11, 17, 28, 44, 55, 58, 59, 89, 93, 94, 95, 96, 98, 100, 101, 102, 104, 111, 116, 128, 133, 134, 176, 185, 200, 218, 230, 231, 232, 236, 241, 258, 259, 260, 279, 284, 287, 289, 290, 300, 312, 313, 325, 326, 331, 352, 359, 380, 381, 382, 412, 415, 416, 417, 424, 431, 445, 449, 452, 460, 462, 496, 500, 501, 503, 512, 567, 570, 574, 576, 592, 593, 617, 621, 623, 654, 655, 656, 659, 660, 665, 672, 677, 678, 692, 693, 703, 705, 719, 720, 721, 732, 734, 742, 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1434, 1435, 1436], "513": [11, 1407, 1415], "reach": [11, 100, 101, 315, 325, 329, 378, 385, 389, 391, 392, 396, 412, 413, 414, 420, 421, 496, 500, 501, 512, 566, 568, 628, 629, 634, 642, 645, 654, 695, 713, 760, 1194, 1213, 1216, 1387, 1388, 1416], "orbit": 11, "up": [11, 71, 81, 94, 95, 98, 100, 101, 102, 105, 106, 108, 133, 134, 348, 349, 379, 425, 429, 511, 532, 542, 579, 621, 654, 655, 659, 750, 1039, 1041, 1064, 1069, 1085, 1091, 1105, 1127, 1129, 1150, 1154, 1179, 1219, 1221, 1278, 1332, 1334, 1361, 1364, 1404, 1405, 1411, 1413, 1415, 1419, 1420, 1422, 1423, 1425, 1426, 1429, 1434, 1436], "reveal": [11, 713, 788], "cycl": [11, 39, 45, 96, 121, 215, 228, 229, 230, 231, 232, 233, 264, 294, 295, 296, 340, 343, 345, 360, 451, 452, 453, 454, 455, 459, 464, 465, 466, 468, 469, 470, 482, 498, 503, 506, 507, 510, 521, 586, 587, 589, 610, 630, 631, 632, 634, 654, 659, 660, 665, 699, 729, 744, 745, 760, 793, 1046, 1055, 1141, 1143, 1154, 1155, 1158, 1169, 1192, 1196, 1248, 1250, 1266, 1270, 1331, 1404, 1406, 1407, 1410, 1412, 1413, 1415, 1416, 1417, 1420, 1421, 1423, 1433], "requir": [11, 39, 66, 94, 95, 96, 100, 101, 102, 103, 105, 107, 108, 110, 112, 116, 166, 208, 292, 293, 294, 297, 302, 303, 309, 310, 317, 439, 478, 502, 522, 523, 617, 682, 700, 701, 702, 722, 731, 733, 788, 793, 798, 864, 894, 909, 930, 945, 976, 991, 1013, 1040, 1042, 1043, 1049, 1114, 1149, 1198, 1199, 1205, 1221, 1223, 1241, 1302, 1332, 1351, 1354, 1355, 1356, 1390, 1402, 1403, 1405, 1411, 1414, 1415, 1416, 1417, 1420, 1421, 1422, 1423, 1428, 1429, 1434, 1436], "less": [11, 35, 44, 100, 102, 129, 143, 145, 228, 290, 325, 326, 382, 383, 385, 386, 387, 424, 427, 428, 431, 466, 522, 523, 638, 675, 688, 733, 788, 1141, 1168, 1180, 1191, 1193, 1200, 1281, 1282, 1359, 1360, 1385, 1413, 1414, 1417, 1420, 1422, 1423], "smallest": [11, 32, 212, 222, 265, 364, 372, 378, 383, 442, 485, 492, 681, 731, 733, 1051, 1206, 1255, 1265, 1281, 1282, 1308, 1326, 1327, 1416], "177": [11, 298, 299, 307, 308, 331], "e": [11, 16, 17, 32, 35, 39, 47, 53, 62, 66, 68, 70, 72, 77, 83, 90, 92, 93, 94, 95, 96, 98, 100, 102, 103, 104, 105, 108, 111, 112, 113, 116, 128, 142, 145, 152, 153, 158, 159, 169, 171, 172, 178, 190, 193, 196, 208, 212, 218, 219, 222, 227, 234, 237, 242, 245, 249, 250, 268, 276, 279, 281, 283, 285, 289, 290, 291, 294, 296, 301, 302, 303, 306, 307, 308, 309, 310, 312, 313, 314, 323, 325, 326, 327, 328, 333, 334, 335, 341, 342, 343, 345, 347, 357, 358, 360, 363, 373, 374, 376, 380, 385, 387, 400, 407, 408, 431, 436, 451, 454, 455, 457, 469, 470, 471, 473, 474, 476, 477, 478, 481, 490, 492, 493, 494, 496, 498, 500, 501, 504, 505, 506, 507, 508, 509, 510, 511, 512, 519, 520, 567, 568, 577, 579, 584, 588, 590, 592, 595, 600, 604, 617, 618, 620, 621, 627, 628, 677, 679, 680, 688, 690, 693, 694, 695, 734, 736, 738, 764, 798, 852, 855, 856, 858, 859, 867, 868, 869, 880, 883, 886, 894, 897, 900, 901, 903, 904, 912, 913, 914, 922, 925, 930, 933, 936, 937, 939, 940, 948, 949, 950, 962, 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1043, 1170, 1199, 1205, 1217, 1329, 1416, 1417, 1423, 1436], "green": [17, 33, 39, 71, 94, 116, 466, 600, 762, 1045, 1308, 1336, 1403, 1421, 1436], "goal": [17, 89, 93, 100, 106, 108, 128, 385, 628, 629, 719, 720, 1045], "g_ex": 17, "m": [17, 26, 29, 31, 32, 64, 66, 68, 92, 94, 97, 103, 107, 111, 113, 129, 182, 192, 202, 210, 212, 213, 220, 228, 232, 236, 237, 239, 240, 241, 242, 244, 245, 249, 258, 259, 260, 264, 273, 275, 276, 279, 281, 283, 285, 294, 295, 297, 301, 302, 303, 309, 310, 316, 317, 318, 332, 340, 343, 345, 347, 354, 357, 358, 363, 364, 372, 382, 385, 387, 414, 431, 433, 434, 435, 453, 464, 481, 496, 500, 501, 511, 512, 513, 514, 521, 547, 557, 571, 584, 586, 587, 589, 590, 608, 616, 621, 627, 654, 660, 661, 686, 688, 693, 694, 708, 750, 751, 763, 764, 777, 874, 882, 891, 955, 964, 973, 1063, 1157, 1161, 1163, 1175, 1181, 1183, 1185, 1187, 1205, 1207, 1208, 1209, 1210, 1211, 1213, 1214, 1215, 1216, 1217, 1219, 1221, 1222, 1224, 1225, 1226, 1228, 1229, 1232, 1235, 1236, 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1356, 1360, 1361, 1364, 1369, 1374, 1377, 1378, 1381, 1382, 1390, 1415, 1416, 1421], "angular": [53, 56], "inform": [53, 67, 93, 94, 100, 101, 102, 103, 104, 108, 112, 113, 122, 133, 160, 166, 201, 203, 205, 221, 227, 231, 232, 250, 302, 303, 304, 309, 310, 315, 324, 325, 326, 327, 340, 407, 408, 440, 455, 457, 482, 490, 502, 514, 566, 568, 570, 574, 575, 576, 585, 594, 616, 621, 626, 693, 777, 784, 788, 798, 860, 864, 890, 892, 893, 905, 909, 928, 929, 941, 945, 972, 974, 975, 987, 991, 1011, 1012, 1040, 1042, 1043, 1045, 1115, 1147, 1149, 1191, 1212, 1220, 1222, 1223, 1224, 1225, 1273, 1286, 1296, 1302, 1362, 1379, 1381, 1382, 1389, 1391, 1397, 1398, 1402, 1403, 1413, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434, 1435, 1436], "angl": [53, 56, 1117, 1119, 1127, 1128, 1129], "instead": [53, 94, 95, 102, 103, 104, 107, 142, 166, 171, 283, 321, 340, 368, 372, 392, 394, 401, 407, 408, 409, 413, 414, 418, 419, 420, 421, 426, 427, 429, 502, 563, 564, 565, 587, 589, 634, 729, 731, 733, 735, 736, 737, 738, 739, 798, 864, 868, 909, 913, 945, 949, 991, 995, 1040, 1041, 1042, 1043, 1045, 1100, 1105, 1106, 1130, 1133, 1141, 1178, 1185, 1190, 1192, 1198, 1199, 1205, 1213, 1223, 1306, 1348, 1381, 1387, 1388, 1391, 1402, 1403, 1404, 1406, 1408, 1410, 1411, 1413, 1414, 1415, 1416, 1417, 1419, 1420, 1421, 1422, 1423, 1425, 1426, 1428, 1429, 1430, 1432, 1433, 1434, 1436], "nonplanar": [53, 1256], "form": [53, 56, 106, 111, 152, 171, 221, 239, 379, 383, 393, 424, 429, 442, 451, 452, 453, 490, 502, 519, 523, 569, 570, 571, 572, 573, 574, 575, 576, 580, 581, 582, 590, 591, 679, 681, 699, 713, 719, 720, 721, 731, 732, 733, 750, 754, 769, 788, 793, 855, 868, 900, 913, 936, 949, 982, 995, 1041, 1067, 1088, 1152, 1173, 1205, 1212, 1221, 1223, 1228, 1246, 1249, 1251, 1254, 1258, 1408, 1415, 1416, 1436], "flow": [53, 67, 106, 279, 297, 302, 303, 304, 309, 310, 324, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 425, 429, 430, 432, 433, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 521, 561, 758, 760, 1273, 1331, 1404, 1408, 1409, 1412, 1415, 1416, 1417, 1420, 1423, 1434], "dead": 53, "detail": [53, 54, 87, 93, 94, 98, 100, 101, 129, 253, 254, 257, 258, 259, 260, 261, 278, 279, 282, 283, 285, 286, 287, 288, 289, 298, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 424, 429, 478, 496, 500, 501, 502, 511, 512, 513, 514, 576, 693, 713, 722, 737, 739, 793, 798, 1040, 1042, 1043, 1045, 1105, 1108, 1139, 1143, 1146, 1213, 1302, 1325, 1351, 1354, 1355, 1356, 1387, 1402, 1408, 1409, 1410, 1411, 1415, 1422, 1423, 1436], "methodologi": 53, "avail": [53, 94, 100, 101, 102, 104, 142, 185, 227, 233, 281, 424, 427, 428, 587, 589, 782, 875, 918, 957, 1001, 1042, 1045, 1200, 1202, 1203, 1204, 1334, 1337, 1340, 1402, 1403, 1405, 1411, 1414, 1415, 1418, 1421, 1422, 1436], "1016": [53, 113, 227, 232, 275, 298, 299, 300, 304, 307, 308, 314, 323, 324, 340, 348, 349, 457, 762, 1239], "compenvurbsi": 53, "2017": [53, 228, 514, 1213, 1214, 1415, 1416], "004": [53, 343], "scienc": [53, 92, 102, 106, 108, 110, 111, 113, 220, 229, 250, 297, 302, 303, 304, 309, 310, 324, 327, 348, 349, 411, 414, 433, 443, 447, 448, 455, 478, 500, 620, 621, 682, 683, 685, 1209, 1229, 1261], "pydata": [53, 1422, 1432, 1433, 1434], "stack": [53, 112, 348, 695, 1048, 1049], "showcas": [54, 87, 94, 110], "analys": [54, 71, 87, 311], "ecosystem": [54, 87, 100, 101, 105, 108, 111, 1434], "descript": [54, 87, 94, 98, 466, 468, 706, 719, 788, 1127, 1128, 1129, 1136, 1137, 1138, 1139, 1144, 1145, 1146, 1147, 1148, 1213, 1228, 1248, 1416, 1420, 1422, 1430, 1431], "plu": [55, 388, 585, 1039, 1091, 1154, 1259], "voronoi": [55, 754, 760, 1331, 1416], "cholera": [55, 58], "broad": [55, 58, 106, 1302], "pump": [55, 58], "record": [55, 58, 95, 100, 693, 1436], "john": [55, 58, 92, 279, 570, 574, 687, 1211, 1256, 1417, 1422], "snow": [55, 58], "1853": [55, 58], "method": [55, 58, 59, 76, 89, 93, 94, 96, 102, 103, 104, 108, 113, 144, 162, 165, 166, 186, 187, 188, 191, 201, 203, 205, 207, 208, 227, 232, 233, 251, 261, 262, 263, 300, 302, 303, 304, 309, 310, 312, 313, 324, 325, 338, 376, 378, 381, 382, 383, 387, 425, 442, 453, 464, 478, 502, 516, 529, 539, 547, 566, 568, 570, 574, 583, 585, 602, 606, 617, 634, 635, 637, 638, 656, 657, 658, 673, 674, 675, 676, 686, 694, 721, 722, 735, 740, 754, 777, 788, 854, 864, 876, 877, 878, 881, 890, 892, 893, 894, 899, 909, 919, 920, 921, 928, 929, 930, 935, 936, 937, 945, 958, 959, 960, 974, 975, 976, 981, 982, 983, 991, 1002, 1003, 1004, 1011, 1012, 1013, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1036, 1041, 1046, 1047, 1048, 1049, 1069, 1180, 1188, 1190, 1199, 1203, 1281, 1282, 1283, 1286, 1302, 1307, 1308, 1329, 1332, 1369, 1404, 1408, 1412, 1413, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1425, 1426, 1431, 1434, 1436], "shown": [55, 58, 101, 103, 519, 520, 949, 995, 1045, 1281, 1282, 1283, 1306, 1355, 1387, 1388, 1413], "centroid": [55, 58, 59], "libpys": [55, 56, 58, 59], "cg": [55, 103, 297, 302, 303, 304, 309, 310, 324, 590], "voronoi_fram": 55, "contextili": [55, 56, 58], "add_basemap": [55, 56, 58], "geopackag": [55, 56, 57, 58], "sqlite": [55, 58], "reli": [55, 58, 100, 104, 364, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 504, 505, 508, 509, 1402, 1416, 1420, 1434], "fiona": [55, 58], "level": [55, 58, 102, 104, 105, 107, 112, 113, 116, 126, 166, 221, 323, 336, 338, 376, 382, 383, 389, 391, 392, 396, 425, 429, 642, 693, 772, 788, 864, 909, 945, 991, 1015, 1016, 1021, 1022, 1023, 1024, 1025, 1097, 1111, 1161, 1208, 1213, 1214, 1242, 1302, 1329, 1334, 1405, 1408, 1416, 1421, 1422, 1423], "interfac": [55, 58, 59, 76, 77, 97, 99, 100, 102, 103, 108, 110, 111, 185, 431, 498, 675, 760, 763, 764, 782, 875, 918, 957, 1001, 1045, 1047, 1332, 1334, 1402, 1405, 1407, 1411, 1413, 1414, 1415, 1418, 1422, 1423, 1434, 1436], "kind": [55, 58, 59, 93, 94, 95, 100, 209, 468, 724, 1208, 1332, 1391], "read_fil": [55, 56, 58, 59], "cholera_cas": [55, 58], "gpkg": [55, 57, 58], "correctli": [55, 165, 325, 326, 1402, 1413, 1415, 1420, 1421, 1428, 1434], "construct": [55, 56, 57, 58, 59, 68, 95, 103, 228, 230, 231, 232, 233, 270, 274, 277, 354, 425, 452, 462, 515, 547, 548, 549, 550, 554, 555, 556, 558, 559, 560, 611, 687, 697, 710, 718, 734, 1045, 1049, 1050, 1055, 1056, 1104, 1105, 1106, 1107, 1108, 1159, 1160, 1181, 1183, 1184, 1186, 1192, 1196, 1197, 1198, 1201, 1209, 1213, 1214, 1215, 1216, 1223, 1225, 1228, 1235, 1242, 1257, 1265, 1269, 1275, 1278, 1284, 1285, 1302, 1329, 1333, 1387, 1388, 1404, 1408, 1415, 1418, 1424, 1434], "column_stack": [55, 58, 59], "could": [55, 94, 102, 103, 104, 106, 166, 216, 217, 225, 583, 681, 864, 909, 945, 991, 1069, 1097, 1105, 1106, 1123, 1132, 1180, 1302, 1306, 1332, 1402, 1413, 1423, 1436], "present": [55, 59, 94, 108, 111, 133, 185, 221, 227, 316, 317, 332, 359, 361, 431, 496, 498, 499, 500, 501, 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108, 1140, 1142, 1277, 1329], "control": [55, 169, 180, 190, 205, 231, 232, 325, 326, 452, 469, 867, 880, 893, 912, 948, 962, 994, 1334, 1411, 1417, 1418, 1422, 1434], "cell": [55, 59, 754, 760, 1277, 1329, 1331, 1416], "convex": 55, "hull": 55, "contigu": [55, 59, 440, 1105, 1283, 1284], "being": [55, 93, 95, 96, 100, 102, 103, 110, 218, 228, 466, 467, 468, 561, 562, 713, 1041, 1048, 1150, 1181, 1242, 1302, 1402, 1403, 1416, 1421, 1422, 1425, 1434], "face": [55, 102, 103, 116, 184, 207, 617, 1046, 1268, 1269], "analogu": [55, 59, 231], "von": 55, "neuman": 55, "neighborhood": [55, 59, 115, 214, 241, 250, 286, 287, 325, 326, 514, 692, 788, 1195], "cardin": [55, 116, 219, 222, 265, 278, 279, 280, 281, 341, 343, 345, 347, 416, 417, 418, 419, 430, 442, 443, 446, 448, 583, 585, 613, 693, 1404], "regular": [55, 59, 66, 89, 100, 479, 480, 481, 482, 624, 625, 626, 760, 1041, 1191, 1196, 1197, 1198, 1245, 1251, 1256, 1257, 1260, 1264, 1267, 1268, 1269, 1270, 1286, 1296, 1329, 1331, 1403, 1404, 1407, 1415, 1421, 1422, 1434], "come": [55, 94, 101, 102, 103, 106, 519, 579, 590, 600, 610, 679, 700, 701, 1049, 1249, 1332, 1411, 1422], "piec": [55, 376], "move": [55, 95, 96, 101, 102, 106, 231, 232, 379, 382, 1120, 1213, 1216, 1402, 1404, 1413, 1414, 1415, 1416, 1420, 1422, 1425, 1428, 1430, 1434], "chessboard": 55, "from_datafram": [55, 56, 58, 59], "built": [55, 70, 94, 103, 104, 107, 231, 232, 364, 466, 1105, 1106, 1108, 1188, 1189, 1190, 1302, 1334, 1405, 1436], "relev": [55, 94, 100, 102, 104, 105, 107, 133, 169, 177, 185, 190, 499, 503, 506, 507, 510, 659, 867, 872, 875, 880, 912, 918, 948, 953, 957, 962, 994, 1001, 1087, 1313, 1318, 1329, 1420, 1426], "delaunay_graph": 55, "merg": [55, 58, 59, 94, 100, 101, 107, 385, 586, 587, 589, 1328, 1412], "nice": [55, 58, 59, 102, 106, 215, 348, 496, 1064, 1334, 1388, 1419], "basemap": [55, 58, 59], "lightblu": [55, 59], "cornsilk": 55, "174": [55, 60, 388, 1170, 1175, 1176, 1177, 1329], "plot_delaunai": [55, 60], "sometim": [56, 64, 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383, 385, 387, 388, 440, 521, 595, 654, 660, 665, 760, 788, 1171, 1175, 1176, 1177, 1332, 1416, 1417, 1418, 1421, 1423], "empathet": 93, "welcom": [93, 95, 110], "patient": 93, "resolv": [93, 94, 95, 98, 100, 101, 102, 466, 1420, 1421, 1434], "assum": [93, 94, 95, 98, 102, 107, 112, 133, 185, 220, 236, 266, 292, 293, 315, 317, 329, 380, 431, 473, 474, 475, 476, 477, 579, 583, 590, 602, 628, 629, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 690, 691, 693, 755, 763, 875, 918, 933, 957, 979, 1001, 1042, 1043, 1089, 1094, 1100, 1149, 1215, 1276, 1293, 1294, 1302, 1308, 1332, 1402, 1403, 1413, 1416, 1434], "intent": [93, 1332], "experi": [93, 95, 101, 106, 214, 348, 349, 483, 484, 1174, 1334], "frustrat": 93, "attack": 93, "peopl": [93, 100, 166, 468, 782, 864, 909, 945, 991, 1045, 1332, 1334, 1413, 1414, 1416, 1422, 1425, 1434], "uncomfort": 93, "threaten": 93, "benefit": [93, 94, 104, 105, 692], "willing": 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94, 100, 103, 741, 1415, 1436], "encourag": [93, 95, 100, 106, 231, 782, 1408], "although": [93, 700, 701, 764, 1150, 1387, 1411], "appropri": [93, 100, 101, 103, 112, 627, 630, 631, 632, 697, 731, 733, 1045, 1101, 1102, 1121, 1302, 1416], "forum": [93, 100], "hard": [93, 102, 107, 113, 213, 424, 782, 1045, 1120, 1224, 1240, 1413, 1421], "respons": [93, 94, 95, 100, 104, 764, 791], "own": [93, 94, 95, 98, 104, 168, 200, 231, 232, 233, 259, 364, 375, 382, 385, 386, 590, 866, 889, 911, 927, 947, 971, 993, 1010, 1064, 1069, 1085, 1171, 1181, 1334, 1387, 1418], "speech": 93, "insult": 93, "harass": 93, "exclusionari": 93, "behaviour": [93, 1422, 1426, 1434], "violent": 93, "threat": 93, "against": [93, 94, 101, 784, 1041, 1265, 1430], "sexist": 93, "racist": 93, "discriminatori": 93, "joke": 93, "post": [93, 94, 95, 100, 105, 107, 233, 714, 1048, 1171, 1302], "sexual": 93, "explicit": [93, 94, 98, 102, 152, 620, 855, 900, 936, 982, 1041, 1196, 1329, 1332, 1404, 1414, 1421, 1422, 1430], 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412, 416, 875, 918, 957, 1001, 1434], "advanc": [94, 104, 576, 594, 620, 675, 760, 798, 1040, 1042, 1043, 1198, 1286, 1296, 1422, 1423], "rebas": [94, 95], "squash": [94, 95], "often": [94, 95, 100, 102, 103, 106, 380, 385, 386, 390, 466, 734, 782, 788, 798, 1040, 1041, 1042, 1043, 1127, 1128, 1129, 1240, 1302, 1332, 1334, 1414, 1434], "typic": [94, 98, 104, 128, 306, 798, 1040, 1042, 1043, 1105, 1106, 1181, 1329, 1422], "propos": [94, 98, 99, 100, 102, 103, 104, 105, 106, 108, 216, 231, 300, 580, 690, 1390, 1421, 1422, 1423, 1431], "easi": [94, 98, 103, 108, 110, 298, 299, 386, 762, 1127, 1129, 1332, 1334, 1391, 1421], "demonstr": [94, 101, 311, 1413, 1415], "spread": [94, 302, 303, 309, 310, 331], "sp": [94, 472, 475, 1104, 1395, 1436], "pd": [94, 1102, 1103, 1106, 1421], "stat": [94, 245, 382, 383, 750, 752, 1199, 1203, 1230, 1234, 1238], "optim": [94, 108, 113, 126, 209, 213, 227, 231, 232, 332, 355, 364, 382, 383, 384, 387, 424, 431, 498, 510, 674, 694, 722, 724, 725, 726, 727, 728, 731, 733, 734, 762, 782, 1111, 1120, 1241, 1326, 1327, 1411, 1420, 1421, 1425], "subpackag": [94, 106, 769, 1332, 1422, 1434], "particular": [94, 98, 111, 116, 359, 376, 519, 620, 752, 1181, 1284, 1285, 1334, 1356, 1418], "decor": [94, 103, 104, 1048, 1049, 1050, 1303, 1304, 1305, 1306, 1307, 1331, 1414, 1416, 1420, 1422, 1423, 1426, 1434], "not_implemented_for": [94, 1302, 1416, 1426], "doesn": [94, 95, 98, 102, 103, 157, 171, 563, 564, 565, 763, 798, 857, 868, 902, 913, 938, 949, 984, 995, 1040, 1042, 1043, 1120, 1181, 1183, 1185, 1222, 1228, 1302, 1332, 1413, 1415, 1416, 1421, 1423, 1434], "function_not_for_multidigraph": 94, "function_only_for_graph": 94, "framework": [94, 103, 1364], "submodul": [94, 1422], "specif": [94, 97, 100, 102, 108, 111, 112, 113, 158, 185, 233, 348, 349, 372, 460, 504, 505, 508, 509, 519, 567, 683, 685, 705, 858, 875, 903, 918, 939, 949, 957, 985, 995, 1001, 1126, 1139, 1141, 1143, 1171, 1199, 1205, 1293, 1294, 1302, 1332, 1349, 1351, 1354, 1355, 1356, 1357, 1358, 1359, 1360, 1383, 1384, 1385, 1386, 1389, 1390, 1391, 1407, 1414, 1418, 1421, 1423, 1433, 1436], "readwrit": [94, 96, 1351, 1353, 1354, 1355, 1356, 1365, 1366, 1371, 1372, 1411, 1415, 1416, 1422], "test_edgelist": 94, "test_parse_edgelist_with_data_list": 94, "doctest": [94, 107, 1416, 1417, 1420, 1421, 1422, 1434], "ideal": [94, 1391], "coverag": [94, 98, 110, 388, 1416, 1420, 1421, 1422, 1429, 1433, 1434], "cov": 94, "stmt": 94, "miss": [94, 106, 472, 571, 575, 607, 609, 612, 613, 1161, 1349, 1410, 1415, 1416, 1420, 1421, 1422, 1423, 1425, 1433, 1434], "brpart": 94, "91": [94, 627, 1422], "114": [94, 490, 492, 496, 1415], "cliqu": [94, 210, 211, 212, 225, 235, 341, 342, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 378, 425, 439, 445, 551, 734, 760, 1172, 1173, 1177, 1178, 1180, 1194, 1223, 1282, 1331, 1404, 1408, 1409, 1415, 1417, 1420, 1421, 1422, 1423], "97": [94, 111, 359], "troubl": [94, 225, 1418, 1422], "anywai": [94, 102, 1418], "tell": [94, 100, 103, 762, 1281, 1284, 1285, 1302, 1334, 1421], "compar": [94, 466, 547, 548, 549, 550, 554, 555, 556, 558, 559, 560, 561, 562, 563, 564, 565, 617, 762, 784, 1171, 1308, 1423], "baselin": [94, 1140, 1142], "ones": [94, 100, 108, 110, 283, 682, 1041, 1404, 1411, 1413], "savefig": [94, 1436], "mpl_image_compar": 94, "test_barbel": 94, "barbel": [94, 294, 295, 393, 426, 1152, 1163, 1282, 1436], "addit": [94, 98, 100, 101, 104, 108, 112, 116, 185, 352, 425, 478, 536, 546, 547, 736, 738, 763, 793, 798, 875, 918, 949, 957, 982, 995, 1001, 1039, 1040, 1042, 1043, 1091, 1120, 1201, 1278, 1302, 1308, 1332, 1351, 1354, 1355, 1356, 1389, 1390, 1391, 1404, 1412, 1413, 1414, 1415, 1416, 1422, 1423, 1434, 1436], "noplot": 94, "browser": 94, "gather": [94, 100], "assembl": [94, 1049, 1050, 1302], "idea": [94, 95, 98, 100, 103, 106, 133, 218, 375, 425, 430, 689, 691, 1332, 1390, 1413, 1416], "plot_": 94, "plot_new_exampl": 94, "highlight": [94, 107, 1412], "resourc": [94, 97, 478, 479, 480, 574, 575, 620, 1171, 1206], "docstr": [94, 95, 96, 98, 110, 349, 1351, 1354, 1355, 1356, 1408, 1415, 1416, 1417, 1420, 1421, 1422, 1423, 1425, 1426, 1429, 1430, 1431, 1432, 1434], "chicago": [94, 1271], "citat": [94, 98, 348, 349, 568, 1245, 1421], "quickest": 94, "scholar": 94, "paywal": 94, "arxiv": [94, 111, 129, 218, 221, 301, 306, 334, 335, 357, 360, 373, 374, 375, 387, 388, 429, 434, 435, 439, 514, 575, 621, 627, 687, 695, 1159, 1175, 1176, 1177, 1191, 1233, 1275, 1286], "access": [94, 102, 126, 152, 169, 190, 431, 473, 474, 475, 476, 477, 498, 608, 628, 629, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 695, 723, 762, 772, 791, 798, 855, 867, 880, 900, 912, 917, 936, 948, 962, 982, 994, 999, 1040, 1041, 1042, 1043, 1141, 1332, 1401, 1402, 1403, 1405, 1407, 1408, 1411, 1415, 1416, 1417, 1419], "cheong": 94, "se": 94, "hang": 94, "yain": 94, "whar": 94, "schemat": 94, "placement": [94, 616], "survei": [94, 111, 566, 568, 583, 788, 1207], "2020": [94, 100, 101, 102, 103, 571, 1415, 1421], "1177": 94, "2f1473871618821740": 94, "upload": [94, 107, 218], "pdf": [94, 111, 113, 129, 215, 216, 217, 218, 221, 236, 306, 312, 313, 316, 323, 325, 326, 327, 332, 344, 357, 358, 375, 412, 413, 414, 415, 416, 417, 419, 428, 429, 432, 444, 449, 450, 478, 485, 492, 496, 513, 514, 521, 566, 568, 569, 572, 573, 575, 620, 621, 692, 695, 750, 751, 752, 762, 764, 1045, 1199, 1203, 1204, 1332, 1416, 1421, 1436], "docx": 94, "ppt": 94, "lectur": [94, 111, 414, 433, 500, 618, 1209], "wayback": [94, 1422], "machin": [94, 313, 333, 496, 513, 514, 764, 1405, 1415, 1422], "snapshot": 94, "unreach": 94, "conduct": [94, 97, 101, 110, 449, 450, 760], "contributor": [95, 97, 100, 106, 107, 111, 1277, 1329, 1412], "shepherd": [95, 100], "mission": [95, 97, 98, 101, 108], "approv": [95, 101], "nuclear": 95, "launch": 95, "carefulli": 95, "clean": [95, 107, 532, 542, 1306, 1415, 1416, 1420, 1422, 1429, 1434], "nearli": 95, "volunt": [95, 108, 1422], "tremend": 95, "felt": 95, "evalu": [95, 131, 153, 158, 159, 196, 332, 620, 621, 628, 629, 856, 858, 859, 886, 901, 903, 904, 925, 937, 939, 940, 968, 983, 985, 986, 1008, 1127, 1129, 1302, 1426], "novic": 95, "strongli": [95, 218, 233, 390, 393, 399, 400, 401, 405, 407, 408, 425, 482, 493, 494, 521, 590, 635, 699, 701, 753, 755, 1191, 1387, 1411, 1415, 1420, 1423, 1426, 1434], "mentorship": [95, 1422], "handhold": 95, "liber": 95, "workflow": [95, 97, 98, 101, 107, 1422, 1429], "realiz": [95, 515, 516, 517, 518, 519, 520, 695, 1181, 1183, 1186, 1213, 1214, 1215, 1216, 1228, 1270], "gentl": 95, "abandon": 95, "difficult": [95, 1414], "carri": [95, 101, 510], "polici": [95, 97, 100, 1421, 1423], "effici": [95, 103, 113, 213, 276, 291, 379, 389, 391, 392, 394, 396, 401, 407, 408, 409, 424, 427, 428, 488, 489, 510, 514, 583, 616, 682, 690, 693, 700, 701, 760, 1137, 1138, 1144, 1145, 1146, 1147, 1148, 1185, 1209, 1236, 1331, 1394, 1398, 1407, 1408, 1415, 1416, 1417, 1420, 1422], "explor": [95, 106, 108, 111, 706, 713, 719], "corner": [95, 1416, 1423], "tempt": 95, "nitpicki": 95, "spell": [95, 1415, 1421, 1422], "suggest": [95, 103, 106, 634, 637, 638, 1171, 1332, 1411, 1415, 1421, 1423, 1434], "latter": [95, 101, 103, 442, 731, 733, 793, 1305], "choic": [95, 103, 205, 387, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 481, 504, 505, 508, 509, 736, 737, 738, 739, 782, 893, 975, 1041, 1045, 1231, 1247, 1286, 1332, 1436], "wish": [95, 621, 1069, 1402], "bring": [95, 102, 568], "advis": [95, 111, 1423], "aris": [95, 111, 239, 244, 1223, 1251], "experienc": 95, "credit": [95, 106], "send": [95, 100, 498, 499, 503, 506, 507, 510, 1402, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434, 1435], "notif": 95, "maintain": [95, 96, 100, 101, 104, 106, 108, 110, 231, 232, 616, 798, 1040, 1042, 1043, 1415, 1434], "concern": [95, 102, 104, 133, 791, 793, 1390], "mere": [95, 1152, 1163], "understood": 95, "made": [95, 100, 101, 103, 223, 283, 285, 286, 287, 288, 289, 325, 326, 333, 695, 696, 1125, 1216, 1332, 1402, 1412, 1413, 1416, 1421], "freeli": 95, "consult": [95, 112], "extern": [95, 108, 621, 1332, 1391, 1416], "insight": 95, "opportun": [95, 100], "patch": [95, 100, 103, 1045, 1139, 1141, 1421, 1422], "vouch": 95, "fulli": [95, 763, 1045, 1194], "behind": [95, 106], "clarif": [95, 300, 323], "deem": 95, "nich": 95, "devot": 95, "sustain": [95, 97], "effort": [95, 108, 1332], "priorit": 95, "similarli": [95, 104, 116, 208, 348, 358, 600, 623, 798, 894, 930, 976, 1013, 1040, 1042, 1043, 1045, 1154, 1181, 1183, 1199, 1204, 1213, 1302, 1403, 1413, 1436], "worth": [95, 763, 1436], "mainten": 95, "burden": 95, "necessari": [95, 96, 101, 105, 529, 539, 956, 1000, 1141, 1143, 1302, 1415, 1421], "valid": [95, 102, 162, 178, 257, 278, 279, 282, 283, 379, 388, 441, 460, 466, 468, 499, 515, 516, 517, 518, 519, 520, 561, 562, 580, 581, 582, 590, 616, 617, 736, 737, 738, 739, 748, 760, 1041, 1046, 1074, 1090, 1103, 1107, 1108, 1171, 1193, 1199, 1243, 1244, 1280, 1284, 1285, 1302, 1337, 1340, 1416, 1421, 1422, 1423, 1426, 1428, 1431], "wari": 95, "alien": 95, "visibl": [95, 98], "thread": [95, 98, 100, 104, 105, 1422], "appeal": [95, 101], "empow": 95, "regardless": [95, 100, 1141, 1197, 1413], "outcom": [95, 106, 1039, 1091, 1390, 1426], "past": [95, 107, 1387, 1414], "pep8": [95, 1416, 1421, 1425], "pep257": 95, "superset": [95, 584], "stackoverflow": 95, "monitor": [95, 102], "signatur": [96, 98, 104, 110, 547, 1048, 1302, 1408, 1413, 1416, 1422, 1428, 1431, 1434], "buggi": 96, "usual": [96, 102, 169, 177, 190, 292, 293, 331, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 440, 442, 469, 617, 755, 764, 798, 867, 872, 880, 912, 948, 953, 962, 994, 1042, 1043, 1045, 1048, 1097, 1180, 1205, 1223, 1278, 1302, 1332, 1412], "minor": [96, 101, 107, 586, 760, 1331, 1403, 1404, 1412, 1415, 1416, 1417, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434], "strict": [96, 111, 215, 216, 217, 621, 1417, 1422], "rule": [96, 101, 200, 510, 762, 889, 927, 971, 1010, 1064, 1085, 1150, 1304], "procedur": [96, 98, 100, 218, 221, 282, 306, 379, 510, 682, 1194, 1387, 1426], "upon": [96, 103, 582, 1302, 1422, 1425], "justif": [96, 105], "literal_string": [96, 1351, 1356, 1392, 1421], "literal_destring": [96, 1353, 1355, 1392, 1421], "coreview": [96, 1422, 1434], "filter": [96, 323, 455, 1039, 1064, 1085, 1091, 1275, 1330, 1331, 1422, 1434], "link_analysi": [96, 1414], "pagerank_alg": [96, 1414], "replac": [96, 100, 103, 104, 203, 233, 271, 387, 413, 414, 432, 433, 514, 585, 798, 892, 928, 936, 974, 982, 1011, 1040, 1042, 1043, 1054, 1097, 1231, 1247, 1301, 1302, 1303, 1317, 1323, 1332, 1353, 1369, 1370, 1387, 1402, 1403, 1405, 1408, 1413, 1415, 1416, 1417, 1418, 1420, 1421, 1422, 1423, 1426, 1431, 1433, 1434], "pagerank": [96, 312, 313, 325, 326, 327, 567, 760, 1289, 1290, 1403, 1407, 1414, 1415, 1416, 1422, 1434], "pagerank_scipi": [96, 1414, 1420, 1422], "renam": [96, 103, 107, 599, 603, 606, 611, 1301, 1354, 1355, 1363, 1403, 1416, 1421, 1430, 1433], "pagerank_numpi": [96, 1414, 1416, 1422], "_pagerank_numpi": 96, "convert_matrix": [96, 1395, 1416, 1420, 1422], "to_pandas_edgelist": [96, 1103, 1416, 1417, 1422, 1434], "binari": [96, 111, 431, 478, 588, 595, 732, 741, 1423], "asmatrix": 96, "wrapper": [96, 1122, 1131, 1302, 1414, 1422], "google_matrix": [96, 568, 1423, 1434], "futurewarn": [96, 1422, 1423], "attrmatrix": [96, 1434], "reflect": [96, 100, 104, 200, 297, 302, 303, 304, 309, 310, 324, 468, 889, 927, 971, 1010, 1064, 1069, 1085, 1088, 1089, 1332, 1415, 1416, 1429], "ndarrai": [96, 108, 567, 631, 1101, 1105, 1284, 1395, 1414, 1423, 1434], "distance_measur": [96, 218, 1420], "extrema_bound": [96, 1425, 1434], "maxcardin": [96, 583, 585, 1425, 1434], "min_weight_match": [96, 760, 1425, 1434], "scale_free_graph": [96, 1422, 1429], "nx_pydot": [96, 1044, 1045, 1130, 1131, 1132, 1133, 1134, 1405, 1417, 1434, 1436], "5723": [96, 1434], "node_link": [96, 1416, 1431, 1434], "node_link_graph": [96, 1369, 1392], "forest_str": [96, 1422], "write_network_text": [96, 1279, 1392], "1rc1": [97, 111, 1331, 1426], "dev0": [97, 111, 1331], "feb": [97, 111, 1331], "2023": [97, 111, 1331, 1434], "about": [97, 100, 101, 102, 104, 106, 112, 116, 231, 232, 250, 415, 425, 490, 496, 500, 501, 511, 512, 621, 763, 764, 1041, 1064, 1069, 1147, 1223, 1302, 1329, 1332, 1415, 1416, 1420, 1421, 1422, 1423, 1425, 1431, 1434, 1436], "emeritu": 97, "introduct": [97, 111, 312, 313, 325, 326, 385, 387, 466, 468, 620, 621, 1161, 1275, 1308, 1331, 1420], "guidelin": [97, 100, 1425, 1428], "divers": [97, 108], "enforc": [97, 116, 695, 696, 1428, 1434], "endnot": 97, "diverg": [97, 1193, 1331, 1404], "upstream": [97, 466, 1428], "mentor": [97, 110, 1422, 1423, 1434], "pedagog": [97, 110, 349, 454, 724, 1414, 1423], "incorpor": [97, 100, 1408, 1436], "ismag": [97, 762, 1420, 1429], "me": 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655, 1041, 1196, 1331, 1404, 1415, 1416, 1417, 1422, 1433, 1434], "prototyp": 100, "sound": 100, "principl": [100, 101, 104, 133], "impract": 100, "wip": [100, 1416, 1417, 1421], "stabil": [100, 336, 337, 683, 685], "provision": 100, "short": [100, 105, 162, 228, 1041, 1069, 1201, 1415], "unlik": [100, 101, 213, 368, 427, 428, 1391], "reject": [100, 101, 105, 1325], "withdrawn": [100, 105], "wherev": [100, 1288], "defer": [100, 102, 105, 281], "challeng": 100, "wider": 100, "done": [100, 102, 103, 231, 232, 250, 375, 442, 468, 519, 566, 568, 616, 692, 764, 1049, 1225, 1302, 1332, 1413], "fact": [100, 354, 462, 621, 1213, 1216, 1413], "actual": [100, 116, 133, 166, 211, 214, 215, 216, 217, 221, 289, 387, 452, 579, 627, 694, 719, 720, 864, 909, 945, 991, 1105, 1106, 1205, 1302, 1330, 1332, 1411, 1425], "compet": [100, 585], "accordingli": [100, 456, 1113, 1416, 1434], "supersed": [100, 105], "render": [100, 106, 217, 412, 415, 1415], "obsolet": [100, 268, 1343, 1415, 1416], "never": [100, 185, 390, 610, 875, 918, 957, 1001, 1242], "meant": [100, 292, 293, 633, 1223, 1332, 1422, 1426], "concret": [100, 101], "think": [100, 103, 231, 232, 300, 763, 1436], "bodi": [100, 1249], "briefli": 100, "sentenc": [100, 101], "substant": 100, "pipermail": 100, "2018": [100, 316, 332, 439, 762, 1415, 1417, 1418], "june": [100, 693, 1261, 1407, 1411, 1415, 1428, 1429], "078345": 100, "verg": 100, "chanc": [100, 231, 1240, 1302], "period": [100, 1217, 1218, 1219, 1221, 1303, 1412, 1415, 1421], "beyond": [100, 108, 385, 1216, 1242], "fine": 100, "shouldn": [100, 103], "rigid": 100, "compromis": 100, "followup": [100, 1422], "notifi": [100, 1423], "celebratori": 100, "emoji": 100, "again": [100, 430, 763, 1223, 1412, 1416, 1420, 1425], "unusu": [100, 1402], "disagr": [100, 101], "escal": [100, 101], "controversi": [100, 108], "ultim": 100, "practic": [100, 211, 221, 483, 484, 496, 621, 655, 1334, 1414], "precis": [100, 313, 570, 574, 583, 1275, 1404, 1418], "natur": [100, 103, 110, 378, 445, 468, 587, 589, 620, 755, 1160, 1223, 1231, 1247, 1302, 1332, 1402, 1419], "utf": [100, 268, 269, 1339, 1340, 1343, 1344, 1345, 1346, 1347, 1350, 1361, 1364, 1374, 1377, 1378, 1381, 1382, 1395, 1415], "restructuredtext": 100, "restructuredtextprim": 100, "dd": [100, 105, 1097], "mmm": 100, "yyyi": [100, 105], "dom": 100, "ain": 100, "separ": [100, 103, 106, 107, 153, 158, 159, 196, 215, 216, 259, 266, 267, 268, 269, 300, 323, 345, 429, 430, 456, 466, 760, 856, 858, 859, 886, 901, 903, 904, 925, 937, 939, 940, 968, 983, 985, 986, 1008, 1048, 1115, 1119, 1199, 1201, 1222, 1331, 1337, 1338, 1339, 1340, 1341, 1342, 1343, 1344, 1345, 1346, 1375, 1376, 1377, 1378, 1404, 1415, 1416, 1421, 1422, 1434, 1436], "older": 100, "brows": 100, "colgat": [101, 111], "deadlock": 101, "websit": [101, 107, 1171, 1390, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434, 1435], "ongo": [101, 1414], "trust": [101, 1389, 1391], "cast": [101, 102, 1421, 1431], "vote": [101, 339, 1421], "therebi": 101, "adher": 101, "nomin": 101, "lazi": [101, 327, 1289, 1290], "unanim": 101, "agreement": [101, 1208], "initi": [101, 103, 142, 231, 232, 283, 316, 325, 326, 340, 375, 379, 380, 468, 497, 513, 514, 527, 537, 617, 694, 721, 735, 798, 852, 897, 933, 979, 1040, 1042, 1043, 1105, 1108, 1111, 1120, 1191, 1192, 1193, 1194, 1229, 1233, 1240, 1284, 1285, 1302, 1308, 1329, 1403, 1404, 1415, 1420, 1421, 1422, 1423], "voic": 101, "smooth": 101, "strateg": 101, "plan": [101, 106, 1403, 1414, 1416, 1422], "fund": [101, 1423, 1434], "theirs": 101, "pursu": 101, "pictur": [101, 1127, 1128, 1129], "perspect": [101, 105, 1201, 1332], "timefram": 101, "entiti": [101, 1351, 1354, 1355, 1356, 1390, 1436], "occasion": [101, 231], "seek": [101, 764, 1358, 1360, 1384, 1386, 1395], "tri": [101, 113, 345, 382, 933, 979, 1042, 1043, 1181, 1187, 1231, 1243, 1244, 1413], "distinguish": [101, 936, 965, 982, 1005, 1043], "fundament": [101, 108, 111, 340, 451, 620, 621, 1223, 1422], "flaw": 101, "forward": [101, 106, 218, 452, 713, 719, 720], "typo": [101, 1405, 1415, 1416, 1417, 1420, 1421, 1422, 1423, 1425, 1426, 1428, 1430, 1434], "land": 101, "outlin": [101, 250, 338, 464, 1416], "templat": [101, 1422], "taken": [101, 102, 146, 149, 208, 445, 452, 719, 720, 751, 763, 894, 930, 976, 1013, 1120, 1418], "suffici": [101, 102, 1332], "scikit": [101, 104, 110], "expos": [102, 376, 1414], "nodeview": [102, 185, 393, 600, 601, 603, 604, 605, 606, 697, 875, 918, 957, 1001, 1039, 1091, 1355, 1368, 1413, 1416], "nodedataview": [102, 185, 393, 593, 594, 602, 875, 918, 957, 1001, 1223, 1436], "edgeview": [102, 592, 593, 594, 600, 601, 602, 603, 604, 605, 606, 614, 626, 772, 912, 1039, 1091, 1101, 1413, 1422], "edgedataview": [102, 169, 190, 867, 880, 912, 948, 962, 994, 1101, 1223, 1368, 1421, 1436], "semant": [102, 533, 543, 764, 1412, 1414], "inher": [102, 221, 429], "impli": [102, 111, 133, 221, 313, 315, 329, 457, 468, 513, 514, 547, 1302], "element": [102, 103, 231, 232, 271, 292, 293, 312, 352, 373, 393, 459, 466, 520, 561, 562, 580, 581, 582, 588, 642, 658, 673, 675, 677, 679, 730, 732, 741, 751, 754, 1039, 1041, 1051, 1052, 1053, 1054, 1090, 1091, 1141, 1143, 1179, 1212, 1217, 1218, 1223, 1243, 1244, 1246, 1255, 1278, 1283, 1284, 1285, 1288, 1293, 1294, 1302, 1308, 1309, 1317, 1324, 1329, 1361, 1364, 1367, 1368, 1414], "intend": [102, 105, 108, 112, 329, 569, 1041, 1045, 1275, 1302, 1402], "impos": [102, 104, 547, 793], "due": [102, 103, 110, 232, 265, 442, 583, 585, 628, 629, 1223, 1414, 1421, 1423, 1432, 1434], "bit": [102, 210, 212, 213, 455, 513, 514, 788, 1351, 1354, 1355, 1356, 1390, 1420, 1434], "lot": [102, 106, 454, 1332, 1414], "screen": 102, "instinct": 102, "error": [102, 103, 153, 158, 159, 196, 281, 289, 297, 312, 325, 416, 424, 473, 474, 475, 476, 477, 491, 499, 503, 506, 507, 510, 558, 559, 560, 566, 568, 583, 586, 655, 662, 669, 677, 678, 798, 856, 858, 859, 886, 901, 903, 904, 925, 937, 939, 940, 968, 983, 985, 986, 1008, 1040, 1046, 1120, 1150, 1405, 1410, 1413, 1415, 1416, 1420, 1421, 1422, 1423, 1426, 1428, 1434], "definit": [102, 133, 236, 239, 244, 290, 292, 293, 304, 324, 344, 358, 400, 437, 439, 466, 469, 551, 552, 553, 610, 620, 621, 622, 627, 678, 687, 689, 702, 737, 739, 793, 1198, 1199, 1203, 1223, 1241, 1293, 1332, 1415, 1422, 1436], "coupl": [102, 103, 133, 1263, 1411, 1413], "realis": 102, "But": [102, 103, 108, 144, 171, 239, 244, 257, 278, 279, 282, 298, 299, 585, 798, 868, 913, 1015, 1016, 1021, 1022, 1023, 1024, 1025, 1040, 1042, 1043, 1097, 1334, 1402, 1434], "seem": [102, 103, 299, 308, 793, 1240], "eas": [102, 108, 1418], "idiom": [102, 160, 191, 201, 860, 881, 890, 905, 941, 963, 972, 987, 1302, 1403, 1413, 1420], "subscript": [102, 152, 160, 201, 798, 855, 860, 890, 900, 905, 936, 941, 972, 982, 987, 1040, 1042, 1043, 1403, 1436], "repr": [102, 1353, 1422], "4950": [102, 1423], "traceback": [102, 452, 466, 586, 654, 660, 1308, 1309], "recent": [102, 439, 452, 466, 586, 654, 660, 966, 1006, 1308, 1309, 1420], "typeerror": [102, 384, 466, 1212, 1308, 1413], "opaqu": 102, "ambigu": [102, 104, 116, 253, 254, 466, 764, 1046, 1415], "ambigi": 102, "counter": [102, 154, 359], "nativ": [102, 110], "caveat": 102, "nodes_it": [102, 1413, 1416], "toward": [102, 687, 1416, 1422, 1434], "inner": [102, 231, 232, 382, 798, 1015, 1016, 1021, 1022, 1023, 1024, 1025, 1040, 1042, 1043, 1089], "synonym": 102, "primarili": [102, 1436], "becam": [102, 1420], "concept": [102, 133, 221, 311, 429, 690, 1046], "intuit": [102, 110], "On": [102, 106, 157, 218, 295, 298, 299, 307, 308, 316, 382, 407, 408, 516, 517, 520, 595, 857, 902, 938, 984, 1186, 1208, 1230, 1234, 1238], "front": [102, 621, 1039, 1091], "constuct": 102, "indx": 102, "desir": [102, 103, 143, 144, 205, 348, 349, 424, 427, 428, 600, 631, 649, 893, 975, 1088, 1097, 1105, 1106, 1108, 1127, 1128, 1156, 1158, 1163, 1165, 1166, 1169, 1171, 1193, 1224, 1226, 1227, 1240, 1287, 1362, 1363, 1423, 1436], 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1048, 1369, 1370], "un": [102, 466, 734, 1416, 1422], "sliceabl": 102, "notabl": [102, 1045], "dict_kei": [102, 1309, 1423], "dict_valu": [102, 381, 1413, 1422], "cpython": [102, 108, 431, 498, 1041, 1411, 1422], "consider": [102, 104, 325, 326, 348, 349, 355, 527, 537, 557, 673, 674, 675, 676, 734, 762, 1174, 1422], "cours": [102, 106, 218, 620, 1332, 1436], "action": [102, 107, 1045, 1422, 1426, 1434], "allevi": 102, "dig": 102, "enough": [102, 470, 511, 1171, 1387], "satisfactorili": 102, "reconsid": [102, 1421], "went": [102, 504], "ahead": 102, "4300": [102, 1422], "4304": [102, 1422], "path_edg": 103, "former": [103, 104, 793], "stylist": 103, "creation": [103, 108, 111, 250, 276, 790, 1160, 1176, 1230, 1234, 1236, 1238, 1331, 1408, 1413, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434, 1435], "cleaner": [103, 1410, 1415], "creativ": [103, 466, 468], "demand": [103, 498, 499, 503, 506, 507, 510], "had": [103, 654, 1223, 1302, 1418, 1425], "node_iter": 103, "isn": [103, 348, 349, 722, 1337, 1340, 1415, 1423, 1434], "leav": [103, 232, 390, 502, 510, 586, 587, 588, 589, 680, 1151, 1161, 1302, 1413, 1418, 1436], "dg": [103, 208, 323, 457, 458, 459, 460, 461, 463, 464, 466, 467, 468, 469, 470, 471, 894, 930, 976, 1013, 1044, 1413, 1436], "mdg": [103, 208, 894, 930, 976, 1013, 1429], "customgraph": 103, "elist": [103, 1332], "isol": [103, 357, 382, 437, 493, 494, 524, 526, 623, 737, 739, 760, 1224, 1331, 1336, 1407, 1410, 1415, 1416, 1426], "ekei": [103, 208, 894, 930, 936, 976, 982, 1013, 1087, 1107], "protocol": [103, 1413], "hashabl": [103, 145, 152, 157, 172, 181, 268, 547, 548, 549, 550, 763, 798, 855, 857, 869, 873, 900, 902, 914, 916, 936, 938, 949, 950, 954, 965, 982, 984, 995, 996, 998, 1005, 1040, 1041, 1042, 1043, 1090, 1213, 1284, 1285, 1301, 1316, 1330, 1332, 1339, 1343, 1344, 1436], "logic": [103, 104, 221, 762, 764, 1304, 1415, 1416, 1428, 1434], "denot": [103, 115, 213, 220, 300, 301, 323, 569, 570, 571, 572, 573, 574, 575, 610, 621, 689, 690, 691, 692, 693, 1127, 1128, 1129, 1180], "multiedg": [103, 555, 936, 982, 1042, 1043, 1088, 1332, 1362, 1363, 1402, 1415, 1421, 1423], "attrdict": [103, 158, 858, 903, 939, 985, 1415], "edge_kei": [103, 491, 1042, 1043, 1103, 1107, 1422], "networkxinvalidedgelist": 103, "flexibl": [103, 111, 469, 1332, 1390, 1391, 1404, 1410, 1415, 1416, 1420, 1436], "wheel": [103, 107, 1169, 1267, 1420, 1430, 1434], "spoke": 103, "wheel_graph": [103, 343, 673, 674, 676], "star": [103, 261, 301, 617, 628, 629, 781, 1057, 1157, 1166, 1229, 1233, 1403, 1413, 1415, 1416, 1420], "mycustomgraph": 103, "configuration_model_graph": 103, "deg_sequ": [103, 517, 519, 520, 1181, 1182, 1183, 1184, 1186, 1228], "graph_build": 103, "py_random_st": [103, 104, 1302, 1305, 1414, 1434], "extended_barabasi_albert_graph": 103, "node_and_edge_build": 103, "ladder_graph": 103, "incompat": [103, 1205, 1411, 1412, 1415], "thrust": 103, "incept": 103, "attach": [103, 215, 275, 359, 571, 573, 623, 1039, 1091, 1125, 1188, 1191, 1229, 1233, 1235, 1332, 1436], "presum": [103, 1303], "rewritten": [103, 1404, 1411, 1415], "gradual": 103, "accomplish": [103, 110, 1171], "wrap": [103, 1048, 1050, 1127, 1129, 1302, 1307, 1310], "custom_graph": 103, "ichain": 103, "tripl": [103, 115, 250, 251, 713, 1420], "overli": 103, "empty_graph": [103, 755, 1060, 1164, 1303, 1329, 1415, 1418, 1419], "3036": 103, "1393": 103, "canon": [103, 686, 732, 1421], "huge": 103, "path_edgelist": 103, "disallow": [103, 798, 1040, 1042, 1043, 1193, 1426], "2022": [104, 106, 695, 1423, 1424, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433], "pseudo": [104, 105, 678, 1326, 1327, 1414, 1416], "nep19": 104, "legaci": [104, 1404, 1411, 1417], "randomst": [104, 1103, 1114, 1120, 1305, 1307, 1310, 1311, 1334, 1414, 1418], "statist": [104, 111, 129, 275, 360, 385, 387, 440, 1228, 1334, 1414], "strategi": [104, 124, 223, 364, 368, 372, 455], "engin": [104, 108, 731, 733, 1421], "modern": 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1282, 1283], "graphic": [133, 456, 519, 520, 695, 760, 1181, 1183, 1186, 1187, 1228, 1331, 1391, 1407, 1410, 1415], "overview": [133, 478, 1041, 1302], "collid": [133, 456], "triplet": [133, 747], "successor": [133, 160, 175, 182, 192, 201, 241, 283, 389, 391, 392, 396, 503, 689, 709, 717, 860, 874, 882, 890, 905, 941, 955, 964, 972, 987, 1058, 1189, 1190, 1195, 1332, 1413, 1416, 1425, 1436], "descend": [133, 456, 458, 467, 711, 760, 1278, 1410, 1413, 1415, 1422, 1423, 1434], "unblock": 133, "commonli": [133, 281, 456, 686, 784], "probabilist": [133, 380], "causal": 133, "markov": [133, 464, 567, 694, 1194], "hmm": 133, "s1": [133, 1248, 1319, 1369], "s2": [133, 1248, 1319], "s3": [133, 1319], "s4": 133, "s5": 133, "o1": 133, "o2": 133, "o3": 133, "o4": 133, "o5": 133, "ob": 133, "d_separ": [133, 760, 1421], "darwich": 133, "shachter": 133, "1998": [133, 1149, 1150, 1231, 1247, 1416], "bay": 133, "ball": 133, "ration": 133, "pastim": 133, "irrelev": [133, 1416], "requisit": 133, "influenc": [133, 325, 326, 514, 788], "fourteenth": [133, 1192], "uncertainti": [133, 592, 734], "artifici": [133, 576, 592, 734], "480": [133, 428, 516, 520, 1407, 1415], "487": 133, "francisco": [133, 734], "morgan": [133, 734], "kaufmann": [133, 734], "koller": 133, "friedman": 133, "mit": [133, 344, 521, 620], "causal_markov_condit": 133, "ness": [134, 686, 784], "classmethod": [142, 1050], "auxiliari": [142, 143, 144, 221, 413, 414, 415, 417, 418, 419, 420, 421, 425, 432, 433, 1411], "sink": [142, 303, 310, 418, 420, 496, 497, 500, 501, 503, 504, 505, 508, 509, 511, 512, 567], "pick": [142, 218, 333, 659, 1194, 1213, 1216, 1416], "st": [142, 417, 419], "cut": [142, 223, 224, 294, 379, 384, 389, 391, 392, 396, 413, 414, 416, 417, 418, 419, 421, 429, 430, 431, 444, 445, 446, 447, 449, 496, 497, 500, 501, 502, 504, 505, 508, 509, 511, 512, 621, 760, 762, 1041, 1069, 1118, 1268, 1331, 1404, 1411, 1415, 1422], "auxgraph": [144, 425], "node_partit": 145, "permut": [145, 370, 454, 455, 457, 468, 750, 1291, 1326, 1327], "frozenset": [145, 268, 341, 385, 588, 590, 754, 1171, 1339, 1343, 1344, 1421], "abc": [145, 547, 1160, 1212, 1309, 1421, 1422], "interchang": [145, 364], "bool": [146, 147, 149, 150, 166, 169, 172, 177, 185, 190, 197, 205, 209, 233, 238, 239, 243, 244, 246, 250, 251, 259, 266, 267, 268, 269, 273, 276, 287, 288, 289, 292, 295, 296, 297, 298, 299, 300, 302, 303, 306, 307, 308, 309, 310, 311, 315, 316, 323, 325, 326, 327, 328, 329, 332, 345, 352, 357, 364, 395, 396, 397, 398, 399, 400, 441, 456, 464, 465, 469, 481, 482, 490, 491, 493, 496, 500, 501, 511, 512, 515, 516, 517, 518, 519, 520, 522, 523, 524, 547, 564, 566, 580, 581, 582, 583, 590, 615, 616, 618, 619, 624, 625, 627, 642, 654, 665, 675, 681, 687, 692, 698, 700, 701, 702, 706, 710, 721, 725, 726, 727, 728, 730, 732, 735, 736, 737, 738, 739, 740, 742, 743, 744, 745, 864, 867, 869, 872, 875, 880, 887, 893, 909, 912, 914, 918, 929, 933, 945, 948, 950, 953, 957, 962, 969, 975, 979, 991, 994, 996, 1001, 1042, 1043, 1048, 1060, 1071, 1073, 1074, 1075, 1087, 1094, 1100, 1119, 1127, 1129, 1139, 1140, 1141, 1142, 1175, 1185, 1191, 1195, 1215, 1217, 1218, 1219, 1221, 1230, 1234, 1236, 1237, 1238, 1281, 1282, 1283, 1284, 1285, 1288, 1301, 1302, 1313, 1315, 1318, 1341, 1342, 1343, 1345, 1347, 1348, 1350, 1359, 1360, 1361, 1362, 1363, 1364, 1366, 1370, 1385, 1386, 1387, 1388], "account": [146, 149, 400, 450, 751, 763, 1276, 1402, 1422], "graph_nod": [146, 149], "subgraph_nod": [146, 149], "find_isomorph": [148, 151], "induc": [149, 168, 200, 212, 227, 344, 390, 394, 408, 429, 438, 439, 472, 489, 496, 497, 500, 501, 504, 505, 508, 509, 511, 512, 514, 588, 591, 754, 763, 764, 866, 889, 911, 927, 947, 971, 993, 1010, 1041, 1064, 1069, 1090, 1105, 1106, 1108, 1195, 1289, 1290, 1402], "u_of_edg": [152, 855, 900], "v_of_edg": [152, 855, 900], "capac": [152, 266, 297, 302, 303, 304, 309, 310, 324, 413, 414, 417, 418, 419, 420, 421, 432, 433, 496, 497, 498, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 760, 855, 900, 936, 982, 1341, 1411], "342": [152, 855, 900, 936, 982, 1261], "ebunch_to_add": [153, 159, 856, 859, 901, 904, 937, 940, 983, 986], "add_weighted_edges_from": [153, 230, 231, 232, 327, 510, 583, 632, 659, 661, 723, 856, 901, 937, 983, 1073, 1332, 1413, 1416, 1436], "runtimeerror": [153, 158, 159, 196, 466, 467, 468, 856, 858, 859, 886, 901, 903, 904, 925, 937, 939, 940, 968, 983, 985, 986, 1008], "happen": [153, 158, 159, 196, 382, 586, 856, 858, 859, 886, 901, 903, 904, 925, 937, 939, 940, 968, 983, 985, 986, 1008, 1412, 1413, 1434], "iterator_of_edg": [153, 159, 856, 859, 901, 904, 937, 940, 983, 986], "wn2898": [153, 856, 901, 937, 983], "wrong": [153, 158, 159, 724, 856, 858, 859, 901, 903, 904, 937, 939, 940, 983, 985, 986, 1415, 1420, 1425, 1434], "start_nod": [154, 155, 156], "end_nod": [154, 155, 156], "reference_neighbor": [154, 155], "half": [154, 155, 156, 165, 178, 184, 207, 298, 299, 617, 655], "clockwis": [154, 155, 170, 183, 198, 617], "networkxexcept": [154, 155, 162, 333, 590, 595, 726, 728, 1046, 1113, 1144, 1186, 1331], "add_half_edge_cw": [154, 156, 165, 617], "connect_compon": [154, 155, 156, 617], "add_half_edge_first": [154, 155, 165, 617], "add_half_edge_ccw": [155, 156, 165, 617], "node_for_ad": [157, 857, 902, 938, 984], "mutabl": [157, 857, 902, 938, 984, 1064, 1069, 1085, 1088, 1089], "hash": [157, 513, 514, 760, 857, 902, 938, 984, 1330, 1331, 1423, 1436], "hello": [157, 158, 857, 858, 902, 903, 938, 939, 984, 985, 1309], "k3": [157, 158, 857, 858, 902, 903, 938, 939, 984, 985, 1223], "utm": [157, 857, 902, 938, 984], "382871": [157, 857, 902, 938, 984], "3972649": [157, 857, 902, 938, 984], "nodes_for_ad": [158, 858, 903, 939, 985], "iterator_of_nod": [158, 196, 858, 886, 903, 925, 939, 968, 985, 1008], "datadict": [160, 191, 201, 208, 736, 738, 860, 881, 890, 894, 905, 930, 941, 963, 972, 976, 1013, 1087, 1318, 1332], "foovalu": [160, 191, 201, 860, 881, 890, 905, 941, 972], "nbrdict": [161, 861, 906, 942, 988, 1022, 1097], "fulfil": [162, 617], "cw": [162, 617], "ccw": [162, 617], "planar": [162, 616, 618, 619, 760, 1113, 1144, 1249, 1252, 1253, 1255, 1331, 1418, 1419], "first_nbr": [162, 617], "invalid": [162, 617, 1422], "alter": [164, 863, 908, 944, 990], "afterward": 165, "as_view": [166, 203, 205, 864, 892, 893, 909, 928, 929, 945, 974, 975, 991, 1011, 1012, 1092, 1093], "shallow": [166, 203, 205, 285, 286, 287, 288, 289, 864, 892, 893, 909, 928, 929, 945, 974, 975, 991, 1011, 1012, 1403], "deepcopi": [166, 203, 205, 864, 892, 893, 909, 928, 929, 945, 974, 975, 991, 1011, 1012, 1418], "__class__": [166, 200, 864, 889, 909, 927, 945, 971, 991, 1010, 1413, 1416, 1418, 1419, 1420], "fresh": [166, 864, 909, 945, 991, 1413], "inspir": [166, 231, 232, 344, 683, 864, 909, 945, 991, 1232, 1329, 1413], "deep": [166, 203, 205, 864, 892, 893, 909, 928, 929, 945, 974, 975, 991, 1011, 1012, 1271, 1403], "degreeview": [167, 865, 910, 946, 952, 992, 1413, 1436], "didegreeview": [167, 865], "outedgeview": [169, 190, 469, 470, 615, 749, 752, 867, 880, 1038, 1086, 1413, 1427], "ddict": [169, 177, 185, 190, 867, 872, 875, 880, 912, 918, 948, 953, 957, 962, 994, 1001], "in_edg": [169, 190, 867, 880, 948, 962, 1413, 1415, 1416], "out_edg": [169, 867, 948, 1065, 1413, 1415, 1416, 1436], "quietli": [169, 190, 867, 880, 912, 948, 962, 994, 1090, 1436], "outedgedataview": [169, 190, 867, 880, 1413, 1420], "set_data": 170, "edge_dict": [171, 868, 913, 949, 995], "safe": [171, 868, 913, 1413, 1421], "edge_ind": [172, 869, 914, 950, 996], "data_dictionari": [172, 869, 914], "simpler": [173, 185, 870, 875, 915, 918, 951, 957, 997, 1001, 1415, 1416, 1426], "indegreeview": [176, 871, 1413], "deg": [176, 189, 244, 260, 358, 363, 687, 871, 879, 952, 961, 1171, 1185, 1228, 1413], "inedgeview": [177, 872, 1413], "inedgedataview": [177, 872], "silent": [181, 194, 196, 321, 873, 884, 886, 916, 923, 925, 954, 966, 968, 998, 1006, 1008, 1088, 1089, 1133, 1359, 1360, 1365, 1369, 1415, 1422], "niter": [181, 683, 684, 685, 686, 853, 873, 898, 916, 934, 954, 980, 998, 1423], "__iter__": [181, 873, 916, 954, 998, 1309], "nodedata": [185, 875, 918, 957, 1001], "5pm": [185, 798, 875, 918, 957, 1001, 1040, 1042, 1043, 1403, 1436], "Not": [185, 381, 434, 435, 436, 437, 438, 439, 440, 478, 875, 918, 957, 1001, 1120, 1222], "nedg": [186, 590, 876, 919, 958, 1002], "__len__": [187, 188, 877, 878, 920, 921, 959, 960, 1003, 1004], "outdegreeview": [189, 879], "Will": [194, 364, 607, 609, 612, 884, 923, 966, 1006, 1413, 1423], "get_data": [198, 618], "inplac": [200, 692, 889, 927, 971, 1010, 1069, 1402], "reduct": [200, 471, 620, 788, 889, 927, 971, 1010, 1069, 1326, 1327, 1422, 1423], "sg": [200, 889, 927, 971, 1010], "largest_wcc": [200, 889, 927, 971, 1010], "is_multigraph": [200, 760, 889, 927, 971, 1010, 1160, 1421], "keydict": [200, 208, 889, 894, 927, 930, 971, 976, 1010, 1013, 1042, 1043], "contrast": [203, 205, 302, 303, 309, 310, 892, 893, 928, 929, 974, 975, 1011, 1012, 1069, 1239, 1247, 1436], "reciproc": [205, 300, 321, 323, 358, 413, 432, 449, 478, 622, 760, 893, 975, 1331, 1425, 1434], "mark_half_edg": 207, "li": [207, 621, 672, 677, 687, 777, 1213, 1216, 1434], "straightforward": [208, 894, 930, 976, 1013], "slightli": [208, 328, 439, 522, 523, 583, 894, 930, 976, 1013, 1171, 1332, 1413, 1416, 1421, 1423, 1434], "singleton": [208, 358, 590, 894, 930, 976, 1013, 1224, 1257, 1416], "preserve_attr": [209, 725, 726, 727, 728], "optimum": [209, 232, 585, 722, 724, 793, 1404, 1415], "arboresc": [209, 462, 721, 722, 724, 726, 728, 742, 745, 760, 1278, 1404, 1415], "span": [209, 227, 228, 229, 296, 510, 620, 621, 626, 721, 722, 724, 726, 728, 734, 735, 736, 737, 738, 739, 740, 760, 1403, 1406, 1415, 1416, 1429], "max_ind_cliqu": 210, "networkxnotimpl": [210, 211, 212, 213, 221, 225, 228, 294, 295, 296, 319, 320, 322, 330, 345, 381, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 405, 406, 407, 408, 409, 424, 426, 427, 428, 429, 431, 457, 459, 460, 461, 462, 470, 483, 484, 502, 591, 592, 610, 682, 734, 1046, 1222, 1281, 1282, 1304, 1331, 1359, 1360, 1385, 1416, 1417], "boppana": [210, 212, 213], "halld\u00f3rsson": [210, 212, 213], "1992": [210, 212, 213, 519, 520, 1416], "exclud": [210, 212, 213, 216, 217, 262, 263, 455, 690, 721, 725, 726, 727, 728, 735, 753, 1039, 1041, 1091, 1223, 1421], "180": [210, 212, 213, 239, 1434], "196": [210, 212, 213], "heurist": [211, 221, 229, 234, 235, 379, 382, 383, 429, 496, 511, 628, 629, 654, 665, 705, 760, 1179, 1326, 1327, 1331, 1404, 1417, 1421, 1422], "max_cliqu": 211, "rigor": 211, "pattabiraman": 211, "bharath": 211, "massiv": [211, 218], "421": 211, "448": 211, "1080": [211, 298, 299, 307, 308, 331], "15427951": 211, "986778": 211, "apx": [212, 213], "subseteq": [212, 281, 290, 620, 677], "omega": [212, 760, 784, 1423], "maximum_cliqu": 212, "1007": [212, 227, 297, 302, 303, 304, 309, 310, 324, 325, 326, 343, 433, 453, 500, 576, 1150, 1187], "bf01994876": 212, "iset": 213, "trial": [214, 231, 232, 1201, 1243, 1244], "estim": [214, 225, 298, 307, 314, 566, 627, 628, 629, 784, 1286, 1416], "coeffici": [214, 249, 261, 262, 263, 264, 290, 357, 358, 360, 572, 620, 621, 627, 684, 686, 780, 784, 1406, 1407, 1408, 1415, 1422], "fraction": [214, 258, 260, 287, 290, 298, 300, 305, 307, 316, 318, 319, 320, 322, 323, 328, 330, 332, 358, 360, 361, 521, 1127, 1129, 1171, 1240], "schank": 214, "thoma": [214, 753, 1416, 1418, 1422], "dorothea": [214, 1174], "wagner": [214, 431, 760, 1174, 1411, 1415], "universit\u00e4t": 214, "karlsruh": 214, "fakult\u00e4t": 214, "f\u00fcr": 214, "informatik": [214, 414], "5445": 214, "ir": [214, 608], "1000001239": 214, "erdos_renyi_graph": [214, 1230, 1238, 1332, 1415, 1436], "cutoff": [215, 216, 311, 328, 385, 412, 413, 414, 420, 421, 496, 497, 500, 501, 512, 639, 640, 642, 643, 644, 645, 646, 649, 650, 651, 658, 662, 663, 664, 669, 670, 671, 679, 680, 1240, 1407, 1411, 1415, 1422, 1425, 1433, 1434], "distinct": [215, 216, 256, 282, 289, 354, 393, 454, 455, 462, 580, 597, 610, 620, 702, 703, 736, 737, 738, 739, 791, 1156, 1250, 1277, 1329, 1332, 1334, 1404, 1426], "nonadjac": [215, 216, 482, 586, 587, 589], "cutset": [215, 216, 416, 417, 418, 419, 429, 430, 502, 508, 760], "menger": [215, 216, 217], "theorem": [215, 216, 217, 221, 236, 282, 312, 313, 323, 413, 508, 509, 516, 519, 520, 620, 1196, 1211], "local_node_connect": [215, 217, 410, 411, 412, 413, 415], "node_connect": [215, 216, 411, 412, 413, 414, 416, 417, 418, 419, 421, 429, 430, 1411], "dougla": [215, 216, 217, 221, 1422, 1434], "035": [215, 216, 217, 221], "eclect": [215, 216, 217], "ss": [215, 216, 217], "uci": [215, 216, 217, 469, 706, 708, 709, 710, 712, 736, 738], "drwhite": [215, 216, 217], "pprint": [215, 348, 579, 713], "all_pairs_node_connect": [216, 217, 1411, 1433], "bf": [216, 217, 218, 365, 590, 706, 708, 709, 710, 719, 1406, 1410, 1415, 1418, 1421, 1422, 1434], "lose": [216, 798, 1040, 1042, 1043], 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"christofides"]], "greedy_tsp": [[230, "greedy-tsp"]], "simulated_annealing_tsp": [[231, "simulated-annealing-tsp"]], "threshold_accepting_tsp": [[232, "threshold-accepting-tsp"]], "traveling_salesman_problem": [[233, "traveling-salesman-problem"]], "treewidth_min_degree": [[234, "treewidth-min-degree"]], "treewidth_min_fill_in": [[235, "treewidth-min-fill-in"]], "min_weighted_vertex_cover": [[236, "min-weighted-vertex-cover"]], "attribute_assortativity_coefficient": [[237, "attribute-assortativity-coefficient"]], "attribute_mixing_dict": [[238, "attribute-mixing-dict"]], "attribute_mixing_matrix": [[239, "attribute-mixing-matrix"]], "average_degree_connectivity": [[240, "average-degree-connectivity"]], "average_neighbor_degree": [[241, "average-neighbor-degree"]], "degree_assortativity_coefficient": [[242, "degree-assortativity-coefficient"]], "degree_mixing_dict": [[243, "degree-mixing-dict"]], "degree_mixing_matrix": [[244, "degree-mixing-matrix"]], 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"eppstein_matching": [[278, "eppstein-matching"]], "hopcroft_karp_matching": [[279, "hopcroft-karp-matching"]], "maximum_matching": [[280, "maximum-matching"]], "minimum_weight_full_matching": [[281, "minimum-weight-full-matching"]], "to_vertex_cover": [[282, "to-vertex-cover"]], "biadjacency_matrix": [[283, "biadjacency-matrix"]], "from_biadjacency_matrix": [[284, "from-biadjacency-matrix"]], "collaboration_weighted_projected_graph": [[285, "collaboration-weighted-projected-graph"]], "generic_weighted_projected_graph": [[286, "generic-weighted-projected-graph"]], "overlap_weighted_projected_graph": [[287, "overlap-weighted-projected-graph"]], "projected_graph": [[288, "projected-graph"]], "weighted_projected_graph": [[289, "weighted-projected-graph"]], "node_redundancy": [[290, "node-redundancy"]], "spectral_bipartivity": [[291, "spectral-bipartivity"]], "edge_boundary": [[292, "edge-boundary"]], "node_boundary": [[293, "node-boundary"]], "bridges": [[294, "bridges"]], "has_bridges": 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"complete_to_chordal_graph": [[343, "complete-to-chordal-graph"]], "find_induced_nodes": [[344, "find-induced-nodes"]], "is_chordal": [[345, "is-chordal"]], "cliques_containing_node": [[346, "cliques-containing-node"]], "enumerate_all_cliques": [[347, "enumerate-all-cliques"]], "find_cliques": [[348, "find-cliques"]], "find_cliques_recursive": [[349, "find-cliques-recursive"]], "graph_clique_number": [[350, "graph-clique-number"]], "graph_number_of_cliques": [[351, "graph-number-of-cliques"]], "make_clique_bipartite": [[352, "make-clique-bipartite"]], "make_max_clique_graph": [[353, "make-max-clique-graph"]], "max_weight_clique": [[354, "max-weight-clique"]], "node_clique_number": [[355, "node-clique-number"]], "number_of_cliques": [[356, "number-of-cliques"]], "generalized_degree": [[359, "generalized-degree"]], "square_clustering": [[360, "square-clustering"]], "transitivity": [[361, "transitivity"]], "triangles": [[362, "triangles"]], "equitable_color": [[363, "equitable-color"]], 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"GraphMatcher.subgraph_isomorphisms_iter": [[545, "graphmatcher-subgraph-isomorphisms-iter"]], "GraphMatcher.syntactic_feasibility": [[546, "graphmatcher-syntactic-feasibility"]], "networkx.algorithms.isomorphism.ISMAGS": [[547, "networkx-algorithms-isomorphism-ismags"]], "categorical_edge_match": [[548, "categorical-edge-match"]], "categorical_multiedge_match": [[549, "categorical-multiedge-match"]], "categorical_node_match": [[550, "categorical-node-match"]], "could_be_isomorphic": [[551, "could-be-isomorphic"]], "fast_could_be_isomorphic": [[552, "fast-could-be-isomorphic"]], "faster_could_be_isomorphic": [[553, "faster-could-be-isomorphic"]], "generic_edge_match": [[554, "generic-edge-match"]], "generic_multiedge_match": [[555, "generic-multiedge-match"]], "generic_node_match": [[556, "generic-node-match"]], "is_isomorphic": [[557, "is-isomorphic"]], "numerical_edge_match": [[558, "numerical-edge-match"]], "numerical_multiedge_match": [[559, "numerical-multiedge-match"]], "numerical_node_match": [[560, "numerical-node-match"]], "rooted_tree_isomorphism": [[561, "rooted-tree-isomorphism"]], "tree_isomorphism": [[562, "tree-isomorphism"]], "vf2pp_all_isomorphisms": [[563, "vf2pp-all-isomorphisms"]], "vf2pp_is_isomorphic": [[564, "vf2pp-is-isomorphic"]], "vf2pp_isomorphism": [[565, "vf2pp-isomorphism"]], "hits": [[566, "hits"]], "google_matrix": [[567, "google-matrix"]], "pagerank": [[568, "pagerank"]], "adamic_adar_index": [[569, "adamic-adar-index"]], "cn_soundarajan_hopcroft": [[570, "cn-soundarajan-hopcroft"]], "common_neighbor_centrality": [[571, "common-neighbor-centrality"]], "jaccard_coefficient": [[572, "jaccard-coefficient"]], "preferential_attachment": [[573, "preferential-attachment"]], "ra_index_soundarajan_hopcroft": [[574, "ra-index-soundarajan-hopcroft"]], "resource_allocation_index": [[575, "resource-allocation-index"]], "within_inter_cluster": [[576, "within-inter-cluster"]], "all_pairs_lowest_common_ancestor": [[577, "all-pairs-lowest-common-ancestor"]], "lowest_common_ancestor": [[578, "lowest-common-ancestor"]], "tree_all_pairs_lowest_common_ancestor": [[579, "tree-all-pairs-lowest-common-ancestor"]], "is_matching": [[580, "is-matching"]], "is_maximal_matching": [[581, "is-maximal-matching"]], "is_perfect_matching": [[582, "is-perfect-matching"]], "max_weight_matching": [[583, "max-weight-matching"]], "maximal_matching": [[584, "maximal-matching"]], "min_weight_matching": [[585, "min-weight-matching"]], "contracted_edge": [[586, "contracted-edge"]], "contracted_nodes": [[587, "contracted-nodes"]], "equivalence_classes": [[588, "equivalence-classes"]], "identified_nodes": [[589, "identified-nodes"]], "quotient_graph": [[590, "quotient-graph"]], "maximal_independent_set": [[591, "maximal-independent-set"]], "moral_graph": [[592, "moral-graph"]], "harmonic_function": [[593, "harmonic-function"]], "local_and_global_consistency": [[594, "local-and-global-consistency"]], "non_randomness": [[595, "non-randomness"]], "compose_all": [[596, "compose-all"]], "disjoint_union_all": [[597, "disjoint-union-all"]], "intersection_all": [[598, "intersection-all"]], "union_all": [[599, "union-all"]], "compose": [[600, "compose"]], "difference": [[601, "difference"]], "disjoint_union": [[602, "disjoint-union"]], "full_join": [[603, "full-join"]], "intersection": [[604, "intersection"]], "symmetric_difference": [[605, "symmetric-difference"]], "union": [[606, "union"]], "cartesian_product": [[607, "cartesian-product"]], "corona_product": [[608, "corona-product"]], "lexicographic_product": [[609, "lexicographic-product"]], "power": [[610, "power"]], "rooted_product": [[611, "rooted-product"]], "strong_product": [[612, "strong-product"]], "tensor_product": [[613, "tensor-product"]], "complement": [[614, "complement"]], "reverse": [[615, "reverse"]], "combinatorial_embedding_to_pos": [[616, "combinatorial-embedding-to-pos"]], "networkx.algorithms.planarity.PlanarEmbedding": [[617, "networkx-algorithms-planarity-planarembedding"]], "check_planarity": [[618, "check-planarity"]], "is_planar": [[619, "is-planar"]], "chromatic_polynomial": [[620, "chromatic-polynomial"]], "tutte_polynomial": [[621, "tutte-polynomial"]], "overall_reciprocity": [[622, "overall-reciprocity"]], "reciprocity": [[623, "reciprocity"]], "is_k_regular": [[624, "is-k-regular"]], "is_regular": [[625, "is-regular"]], "k_factor": [[626, "k-factor"]], "rich_club_coefficient": [[627, "rich-club-coefficient"]], "astar_path": [[628, "astar-path"]], "astar_path_length": [[629, "astar-path-length"]], "floyd_warshall": [[630, "floyd-warshall"]], "floyd_warshall_numpy": [[631, "floyd-warshall-numpy"]], "floyd_warshall_predecessor_and_distance": [[632, "floyd-warshall-predecessor-and-distance"]], "reconstruct_path": [[633, "reconstruct-path"]], "all_shortest_paths": [[634, "all-shortest-paths"]], "average_shortest_path_length": [[635, "average-shortest-path-length"]], "has_path": [[636, "has-path"]], "shortest_path": [[637, "shortest-path"]], "shortest_path_length": [[638, "shortest-path-length"]], "all_pairs_shortest_path": [[639, "all-pairs-shortest-path"]], "all_pairs_shortest_path_length": [[640, "all-pairs-shortest-path-length"]], "bidirectional_shortest_path": [[641, "bidirectional-shortest-path"]], "predecessor": [[642, "predecessor"]], "single_source_shortest_path": [[643, "single-source-shortest-path"]], "single_source_shortest_path_length": [[644, "single-source-shortest-path-length"]], "single_target_shortest_path": [[645, "single-target-shortest-path"]], "single_target_shortest_path_length": [[646, "single-target-shortest-path-length"]], "all_pairs_bellman_ford_path": [[647, "all-pairs-bellman-ford-path"]], "all_pairs_bellman_ford_path_length": [[648, "all-pairs-bellman-ford-path-length"]], "all_pairs_dijkstra": [[649, "all-pairs-dijkstra"]], "all_pairs_dijkstra_path": [[650, "all-pairs-dijkstra-path"]], "all_pairs_dijkstra_path_length": [[651, "all-pairs-dijkstra-path-length"]], "bellman_ford_path": [[652, "bellman-ford-path"]], "bellman_ford_path_length": [[653, "bellman-ford-path-length"]], "bellman_ford_predecessor_and_distance": [[654, "bellman-ford-predecessor-and-distance"]], "bidirectional_dijkstra": [[655, "bidirectional-dijkstra"]], "dijkstra_path": [[656, "dijkstra-path"]], "dijkstra_path_length": [[657, "dijkstra-path-length"]], "dijkstra_predecessor_and_distance": [[658, "dijkstra-predecessor-and-distance"]], "find_negative_cycle": [[659, "find-negative-cycle"]], "goldberg_radzik": [[660, "goldberg-radzik"]], "johnson": [[661, "johnson"]], "multi_source_dijkstra": [[662, "multi-source-dijkstra"]], "multi_source_dijkstra_path": [[663, "multi-source-dijkstra-path"]], "multi_source_dijkstra_path_length": [[664, "multi-source-dijkstra-path-length"]], "negative_edge_cycle": [[665, "negative-edge-cycle"]], "single_source_bellman_ford": [[666, "single-source-bellman-ford"]], "single_source_bellman_ford_path": [[667, "single-source-bellman-ford-path"]], "single_source_bellman_ford_path_length": [[668, "single-source-bellman-ford-path-length"]], "single_source_dijkstra": [[669, "single-source-dijkstra"]], "single_source_dijkstra_path": [[670, "single-source-dijkstra-path"]], "single_source_dijkstra_path_length": [[671, "single-source-dijkstra-path-length"]], "generate_random_paths": [[672, "generate-random-paths"]], "graph_edit_distance": [[673, "graph-edit-distance"]], "optimal_edit_paths": [[674, "optimal-edit-paths"]], "optimize_edit_paths": [[675, "optimize-edit-paths"]], "optimize_graph_edit_distance": [[676, "optimize-graph-edit-distance"]], "panther_similarity": [[677, "panther-similarity"]], "simrank_similarity": [[678, "simrank-similarity"]], "all_simple_edge_paths": [[679, "all-simple-edge-paths"]], "all_simple_paths": [[680, "all-simple-paths"]], "is_simple_path": [[681, "is-simple-path"]], "shortest_simple_paths": [[682, "shortest-simple-paths"]], "lattice_reference": [[683, "lattice-reference"]], "omega": [[684, "omega"]], "random_reference": [[685, "random-reference"]], "sigma": [[686, "sigma"]], "s_metric": [[687, "s-metric"]], "spanner": [[688, "spanner"]], "constraint": [[689, "constraint"]], "effective_size": [[690, "effective-size"]], "local_constraint": [[691, "local-constraint"]], "dedensify": [[692, "dedensify"]], "snap_aggregation": [[693, "snap-aggregation"]], "connected_double_edge_swap": [[694, "connected-double-edge-swap"]], "directed_edge_swap": [[695, "directed-edge-swap"]], "double_edge_swap": [[696, "double-edge-swap"]], "find_threshold_graph": [[697, "find-threshold-graph"]], "is_threshold_graph": [[698, "is-threshold-graph"]], "hamiltonian_path": [[699, "hamiltonian-path"]], "is_reachable": [[700, "is-reachable"]], "is_tournament": [[702, "is-tournament"]], "random_tournament": [[703, "random-tournament"]], "score_sequence": [[704, "score-sequence"]], "bfs_beam_edges": [[705, "bfs-beam-edges"]], "bfs_edges": [[706, "bfs-edges"]], "bfs_layers": [[707, "bfs-layers"]], "bfs_predecessors": [[708, "bfs-predecessors"]], "bfs_successors": [[709, "bfs-successors"]], "bfs_tree": [[710, "bfs-tree"]], "descendants_at_distance": [[711, "descendants-at-distance"]], "dfs_edges": [[712, "dfs-edges"]], "dfs_labeled_edges": [[713, "dfs-labeled-edges"]], "dfs_postorder_nodes": [[714, "dfs-postorder-nodes"]], "dfs_predecessors": [[715, "dfs-predecessors"]], "dfs_preorder_nodes": [[716, "dfs-preorder-nodes"]], "dfs_successors": [[717, "dfs-successors"]], "dfs_tree": [[718, "dfs-tree"]], "edge_bfs": [[719, "edge-bfs"]], "edge_dfs": [[720, "edge-dfs"]], "networkx.algorithms.tree.branchings.ArborescenceIterator": [[721, "networkx-algorithms-tree-branchings-arborescenceiterator"]], "networkx.algorithms.tree.branchings.Edmonds": [[722, "networkx-algorithms-tree-branchings-edmonds"]], "branching_weight": [[723, "branching-weight"]], "greedy_branching": [[724, "greedy-branching"]], "maximum_branching": [[725, "maximum-branching"]], "maximum_spanning_arborescence": [[726, "maximum-spanning-arborescence"]], "minimum_branching": [[727, "minimum-branching"]], "minimum_spanning_arborescence": [[728, "minimum-spanning-arborescence"]], "NotATree": [[729, "notatree"]], "from_nested_tuple": [[730, "from-nested-tuple"]], "from_prufer_sequence": [[731, "from-prufer-sequence"]], "to_nested_tuple": [[732, "to-nested-tuple"]], "to_prufer_sequence": [[733, "to-prufer-sequence"]], "junction_tree": [[734, "junction-tree"]], "networkx.algorithms.tree.mst.SpanningTreeIterator": [[735, "networkx-algorithms-tree-mst-spanningtreeiterator"]], "maximum_spanning_edges": [[736, "maximum-spanning-edges"]], "maximum_spanning_tree": [[737, "maximum-spanning-tree"]], "minimum_spanning_edges": [[738, "minimum-spanning-edges"]], "minimum_spanning_tree": [[739, "minimum-spanning-tree"]], "random_spanning_tree": [[740, "random-spanning-tree"]], "join": [[741, "join"]], "is_arborescence": [[742, "is-arborescence"]], "is_branching": [[743, "is-branching"]], "is_forest": [[744, "is-forest"]], "is_tree": [[745, "is-tree"]], "all_triads": [[746, "all-triads"]], "all_triplets": [[747, "all-triplets"]], "is_triad": [[748, "is-triad"]], "random_triad": [[749, "random-triad"]], "triad_type": [[750, "triad-type"]], "triadic_census": [[751, "triadic-census"]], "triads_by_type": [[752, "triads-by-type"]], "closeness_vitality": [[753, "closeness-vitality"]], "voronoi_cells": [[754, "voronoi-cells"]], "wiener_index": [[755, "wiener-index"]], "Graph Hashing": [[756, "module-networkx.algorithms.graph_hashing"]], "Graphical degree sequence": [[757, "module-networkx.algorithms.graphical"]], "Hierarchy": [[758, "module-networkx.algorithms.hierarchy"]], "Hybrid": [[759, "module-networkx.algorithms.hybrid"]], "Isolates": [[761, "module-networkx.algorithms.isolate"]], "Isomorphism": [[762, "isomorphism"]], "VF2++": [[762, "module-networkx.algorithms.isomorphism.vf2pp"]], "VF2++ Algorithm": [[762, "vf2-algorithm"]], "Tree Isomorphism": [[762, "module-networkx.algorithms.isomorphism.tree_isomorphism"]], "Advanced Interfaces": [[762, "advanced-interfaces"]], "ISMAGS Algorithm": [[763, "module-networkx.algorithms.isomorphism.ismags"]], "Notes": [[763, "notes"], [764, "notes"], [1045, "notes"]], "ISMAGS object": [[763, "ismags-object"]], "VF2 Algorithm": [[764, "module-networkx.algorithms.isomorphism.isomorphvf2"]], "Subgraph Isomorphism": [[764, "subgraph-isomorphism"]], "Graph Matcher": [[764, "graph-matcher"]], "DiGraph Matcher": [[764, "digraph-matcher"]], "Match helpers": [[764, "match-helpers"]], "Link Analysis": [[765, "link-analysis"]], "PageRank": [[765, "module-networkx.algorithms.link_analysis.pagerank_alg"]], "Hits": [[765, "module-networkx.algorithms.link_analysis.hits_alg"]], "Link Prediction": [[766, "module-networkx.algorithms.link_prediction"]], "Lowest Common Ancestor": [[767, "module-networkx.algorithms.lowest_common_ancestors"]], "Minors": [[769, "module-networkx.algorithms.minors"]], "Maximal independent set": [[770, "module-networkx.algorithms.mis"]], "Moral": [[771, "module-networkx.algorithms.moral"]], "Node Classification": [[772, "module-networkx.algorithms.node_classification"]], "non-randomness": [[773, "module-networkx.algorithms.non_randomness"]], "Operators": [[774, "operators"]], "Planar Drawing": [[775, "module-networkx.algorithms.planar_drawing"]], "Planarity": [[776, "module-networkx.algorithms.planarity"]], "Graph Polynomials": [[777, "module-networkx.algorithms.polynomials"]], "Reciprocity": [[778, "module-networkx.algorithms.reciprocity"]], "Regular": [[779, "module-networkx.algorithms.regular"]], "Rich Club": [[780, "module-networkx.algorithms.richclub"]], "Shortest Paths": [[781, "module-networkx.algorithms.shortest_paths.generic"]], "Advanced Interface": [[781, "module-networkx.algorithms.shortest_paths.unweighted"]], "Dense Graphs": [[781, "module-networkx.algorithms.shortest_paths.dense"]], "A* Algorithm": [[781, "module-networkx.algorithms.shortest_paths.astar"]], "Similarity Measures": [[782, "module-networkx.algorithms.similarity"]], "Simple Paths": [[783, "module-networkx.algorithms.simple_paths"]], "Small-world": [[784, "module-networkx.algorithms.smallworld"]], "s metric": [[785, "module-networkx.algorithms.smetric"]], "Sparsifiers": [[786, "module-networkx.algorithms.sparsifiers"]], "Structural holes": [[787, "module-networkx.algorithms.structuralholes"]], "Summarization": [[788, "module-networkx.algorithms.summarization"]], "Swap": [[789, "module-networkx.algorithms.swap"]], "Threshold Graphs": [[790, "module-networkx.algorithms.threshold"]], "Tournament": [[791, "module-networkx.algorithms.tournament"]], "Traversal": [[792, "traversal"]], "Depth First Search": [[792, "module-networkx.algorithms.traversal.depth_first_search"]], "Breadth First Search": [[792, "module-networkx.algorithms.traversal.breadth_first_search"]], "Beam search": [[792, "module-networkx.algorithms.traversal.beamsearch"]], "Depth First Search on Edges": [[792, "module-networkx.algorithms.traversal.edgedfs"]], "Breadth First Search on Edges": [[792, "module-networkx.algorithms.traversal.edgebfs"]], "Tree": [[793, "tree"]], "Recognition": [[793, "module-networkx.algorithms.tree.recognition"]], "Recognition Tests": [[793, "recognition-tests"]], "Branchings and Spanning Arborescences": [[793, "module-networkx.algorithms.tree.branchings"]], "Encoding and decoding": [[793, "module-networkx.algorithms.tree.coding"]], "Operations": [[793, "module-networkx.algorithms.tree.operations"]], "Spanning Trees": [[793, "module-networkx.algorithms.tree.mst"]], "Exceptions": [[793, "exceptions"], [1046, "module-networkx.exception"]], "Vitality": [[795, "module-networkx.algorithms.vitality"]], "Voronoi cells": [[796, "module-networkx.algorithms.voronoi"]], "Wiener index": [[797, "module-networkx.algorithms.wiener"]], "DiGraph\u2014Directed graphs with self loops": [[798, "digraph-directed-graphs-with-self-loops"]], "Overview": [[798, "overview"], [1040, "overview"], [1042, "overview"], [1043, "overview"]], "Methods": [[798, "methods"], [1040, "methods"], [1042, "methods"], [1043, "methods"]], "Adding and removing nodes and edges": [[798, "adding-and-removing-nodes-and-edges"], [1040, "adding-and-removing-nodes-and-edges"], [1043, "adding-and-removing-nodes-and-edges"]], "Reporting nodes edges and neighbors": [[798, "reporting-nodes-edges-and-neighbors"], [1040, "reporting-nodes-edges-and-neighbors"], [1042, "reporting-nodes-edges-and-neighbors"], [1043, "reporting-nodes-edges-and-neighbors"]], "Counting nodes edges and neighbors": [[798, "counting-nodes-edges-and-neighbors"], [1040, "counting-nodes-edges-and-neighbors"], [1042, "counting-nodes-edges-and-neighbors"], [1043, "counting-nodes-edges-and-neighbors"]], "Making copies and subgraphs": [[798, "making-copies-and-subgraphs"], [1040, "making-copies-and-subgraphs"], [1042, "making-copies-and-subgraphs"], [1043, "making-copies-and-subgraphs"]], "AdjacencyView.copy": [[799, "adjacencyview-copy"]], "AdjacencyView.get": [[800, "adjacencyview-get"]], "AdjacencyView.items": [[801, "adjacencyview-items"]], "AdjacencyView.keys": [[802, "adjacencyview-keys"]], "AdjacencyView.values": [[803, "adjacencyview-values"]], "AtlasView.copy": [[804, "atlasview-copy"]], "AtlasView.get": [[805, "atlasview-get"]], "AtlasView.items": [[806, "atlasview-items"]], "AtlasView.keys": [[807, "atlasview-keys"]], "AtlasView.values": [[808, "atlasview-values"]], "FilterAdjacency.get": [[809, "filteradjacency-get"]], "FilterAdjacency.items": [[810, "filteradjacency-items"]], "FilterAdjacency.keys": [[811, "filteradjacency-keys"]], "FilterAdjacency.values": [[812, "filteradjacency-values"]], "FilterAtlas.get": [[813, "filteratlas-get"]], "FilterAtlas.items": [[814, "filteratlas-items"]], "FilterAtlas.keys": [[815, "filteratlas-keys"]], "FilterAtlas.values": [[816, "filteratlas-values"]], "FilterMultiAdjacency.get": [[817, "filtermultiadjacency-get"]], "FilterMultiAdjacency.items": [[818, "filtermultiadjacency-items"]], "FilterMultiAdjacency.keys": [[819, "filtermultiadjacency-keys"]], "FilterMultiAdjacency.values": [[820, "filtermultiadjacency-values"]], "FilterMultiInner.get": [[821, "filtermultiinner-get"]], "FilterMultiInner.items": [[822, "filtermultiinner-items"]], "FilterMultiInner.keys": [[823, "filtermultiinner-keys"]], "FilterMultiInner.values": [[824, "filtermultiinner-values"]], "MultiAdjacencyView.copy": [[825, "multiadjacencyview-copy"]], "MultiAdjacencyView.get": [[826, "multiadjacencyview-get"]], "MultiAdjacencyView.items": [[827, "multiadjacencyview-items"]], "MultiAdjacencyView.keys": [[828, "multiadjacencyview-keys"]], "MultiAdjacencyView.values": [[829, "multiadjacencyview-values"]], "UnionAdjacency.copy": [[830, "unionadjacency-copy"]], "UnionAdjacency.get": [[831, "unionadjacency-get"]], "UnionAdjacency.items": [[832, "unionadjacency-items"]], "UnionAdjacency.keys": [[833, "unionadjacency-keys"]], "UnionAdjacency.values": [[834, "unionadjacency-values"]], "UnionAtlas.copy": [[835, "unionatlas-copy"]], "UnionAtlas.get": [[836, "unionatlas-get"]], "UnionAtlas.items": [[837, "unionatlas-items"]], "UnionAtlas.keys": [[838, "unionatlas-keys"]], "UnionAtlas.values": [[839, "unionatlas-values"]], "UnionMultiAdjacency.copy": [[840, "unionmultiadjacency-copy"]], "UnionMultiAdjacency.get": [[841, "unionmultiadjacency-get"]], "UnionMultiAdjacency.items": [[842, "unionmultiadjacency-items"]], "UnionMultiAdjacency.keys": [[843, "unionmultiadjacency-keys"]], "UnionMultiAdjacency.values": [[844, "unionmultiadjacency-values"]], "UnionMultiInner.copy": [[845, "unionmultiinner-copy"]], "UnionMultiInner.get": [[846, "unionmultiinner-get"]], "UnionMultiInner.items": [[847, "unionmultiinner-items"]], "UnionMultiInner.keys": [[848, "unionmultiinner-keys"]], "UnionMultiInner.values": [[849, "unionmultiinner-values"]], "DiGraph.__contains__": [[850, "digraph-contains"]], "DiGraph.__getitem__": [[851, "digraph-getitem"]], "DiGraph.__init__": [[852, "digraph-init"]], "DiGraph.__iter__": [[853, "digraph-iter"]], "DiGraph.__len__": [[854, "digraph-len"]], "DiGraph.add_edge": [[855, "digraph-add-edge"]], "DiGraph.add_edges_from": [[856, "digraph-add-edges-from"]], "DiGraph.add_node": [[857, "digraph-add-node"]], "DiGraph.add_nodes_from": [[858, "digraph-add-nodes-from"]], "DiGraph.add_weighted_edges_from": [[859, "digraph-add-weighted-edges-from"]], "DiGraph.adj": [[860, "digraph-adj"]], "DiGraph.adjacency": [[861, "digraph-adjacency"]], "DiGraph.clear": [[862, "digraph-clear"]], "DiGraph.clear_edges": [[863, "digraph-clear-edges"]], "DiGraph.copy": [[864, "digraph-copy"]], "DiGraph.degree": [[865, "digraph-degree"]], "DiGraph.edge_subgraph": [[866, "digraph-edge-subgraph"]], "DiGraph.edges": [[867, "digraph-edges"]], "DiGraph.get_edge_data": [[868, "digraph-get-edge-data"]], "DiGraph.has_edge": [[869, 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Applying classic graph operations, such as:": [[1436, "applying-classic-graph-operations-such-as"]], "2. Using a call to one of the classic small graphs, e.g.,": [[1436, "using-a-call-to-one-of-the-classic-small-graphs-e-g"]], "3. Using a (constructive) generator for a classic graph, e.g.,": [[1436, "using-a-constructive-generator-for-a-classic-graph-e-g"]], "4. Using a stochastic graph generator, e.g,": [[1436, "using-a-stochastic-graph-generator-e-g"]], "5. Reading a graph stored in a file using common graph formats": [[1436, "reading-a-graph-stored-in-a-file-using-common-graph-formats"]], "Analyzing graphs": [[1436, "analyzing-graphs"]], "Drawing graphs": [[1436, "drawing-graphs"]], "NX-Guides": [[1436, "nx-guides"]]}, "indexentries": {"module": [[113, "module-networkx.algorithms.approximation"], [113, "module-networkx.algorithms.approximation.clique"], [113, "module-networkx.algorithms.approximation.clustering_coefficient"], [113, "module-networkx.algorithms.approximation.connectivity"], [113, "module-networkx.algorithms.approximation.distance_measures"], [113, "module-networkx.algorithms.approximation.dominating_set"], [113, "module-networkx.algorithms.approximation.kcomponents"], [113, "module-networkx.algorithms.approximation.matching"], [113, "module-networkx.algorithms.approximation.maxcut"], [113, "module-networkx.algorithms.approximation.ramsey"], [113, "module-networkx.algorithms.approximation.steinertree"], [113, 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"networkx.algorithms.cuts": [[131, "module-networkx.algorithms.cuts"]], "networkx.algorithms.cycles": [[132, "module-networkx.algorithms.cycles"]], "networkx.algorithms.d_separation": [[133, "module-networkx.algorithms.d_separation"]], "networkx.algorithms.dag": [[134, "module-networkx.algorithms.dag"]], "networkx.algorithms.distance_measures": [[135, "module-networkx.algorithms.distance_measures"]], "networkx.algorithms.distance_regular": [[136, "module-networkx.algorithms.distance_regular"]], "networkx.algorithms.dominance": [[137, "module-networkx.algorithms.dominance"]], "networkx.algorithms.dominating": [[138, "module-networkx.algorithms.dominating"]], "networkx.algorithms.efficiency_measures": [[139, "module-networkx.algorithms.efficiency_measures"]], "networkx.algorithms.euler": [[140, "module-networkx.algorithms.euler"]], "networkx.algorithms.flow": [[141, "module-networkx.algorithms.flow"]], "construct() (edgecomponentauxgraph class method)": [[142, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.construct"]], "k_edge_components() (edgecomponentauxgraph method)": [[143, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.k_edge_components"]], "k_edge_subgraphs() (edgecomponentauxgraph method)": [[144, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.k_edge_subgraphs"]], "analyze_symmetry() (ismags method)": [[145, "networkx.algorithms.isomorphism.ISMAGS.analyze_symmetry"]], "find_isomorphisms() (ismags method)": [[146, "networkx.algorithms.isomorphism.ISMAGS.find_isomorphisms"]], "is_isomorphic() (ismags method)": [[147, "networkx.algorithms.isomorphism.ISMAGS.is_isomorphic"]], "isomorphisms_iter() (ismags method)": [[148, "networkx.algorithms.isomorphism.ISMAGS.isomorphisms_iter"]], "largest_common_subgraph() (ismags method)": [[149, "networkx.algorithms.isomorphism.ISMAGS.largest_common_subgraph"]], "subgraph_is_isomorphic() (ismags method)": [[150, "networkx.algorithms.isomorphism.ISMAGS.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (ismags method)": [[151, "networkx.algorithms.isomorphism.ISMAGS.subgraph_isomorphisms_iter"]], "add_edge() (planarembedding method)": [[152, "networkx.algorithms.planarity.PlanarEmbedding.add_edge"]], "add_edges_from() (planarembedding method)": [[153, "networkx.algorithms.planarity.PlanarEmbedding.add_edges_from"]], "add_half_edge_ccw() (planarembedding method)": [[154, "networkx.algorithms.planarity.PlanarEmbedding.add_half_edge_ccw"]], "add_half_edge_cw() (planarembedding method)": [[155, "networkx.algorithms.planarity.PlanarEmbedding.add_half_edge_cw"]], "add_half_edge_first() (planarembedding method)": [[156, "networkx.algorithms.planarity.PlanarEmbedding.add_half_edge_first"]], "add_node() (planarembedding method)": [[157, "networkx.algorithms.planarity.PlanarEmbedding.add_node"]], "add_nodes_from() (planarembedding method)": [[158, "networkx.algorithms.planarity.PlanarEmbedding.add_nodes_from"]], "add_weighted_edges_from() (planarembedding method)": [[159, "networkx.algorithms.planarity.PlanarEmbedding.add_weighted_edges_from"]], "adj (planarembedding property)": [[160, "networkx.algorithms.planarity.PlanarEmbedding.adj"]], "adjacency() (planarembedding method)": [[161, "networkx.algorithms.planarity.PlanarEmbedding.adjacency"]], "check_structure() (planarembedding method)": [[162, "networkx.algorithms.planarity.PlanarEmbedding.check_structure"]], "clear() (planarembedding method)": [[163, "networkx.algorithms.planarity.PlanarEmbedding.clear"]], "clear_edges() (planarembedding method)": [[164, "networkx.algorithms.planarity.PlanarEmbedding.clear_edges"]], "connect_components() (planarembedding method)": [[165, "networkx.algorithms.planarity.PlanarEmbedding.connect_components"]], "copy() (planarembedding method)": [[166, "networkx.algorithms.planarity.PlanarEmbedding.copy"]], "degree (planarembedding property)": [[167, "networkx.algorithms.planarity.PlanarEmbedding.degree"]], "edge_subgraph() (planarembedding method)": [[168, "networkx.algorithms.planarity.PlanarEmbedding.edge_subgraph"]], "edges (planarembedding property)": [[169, "networkx.algorithms.planarity.PlanarEmbedding.edges"]], "get_data() (planarembedding method)": [[170, "networkx.algorithms.planarity.PlanarEmbedding.get_data"]], "get_edge_data() (planarembedding method)": [[171, "networkx.algorithms.planarity.PlanarEmbedding.get_edge_data"]], "has_edge() (planarembedding method)": [[172, "networkx.algorithms.planarity.PlanarEmbedding.has_edge"]], "has_node() (planarembedding method)": [[173, "networkx.algorithms.planarity.PlanarEmbedding.has_node"]], "has_predecessor() (planarembedding method)": [[174, "networkx.algorithms.planarity.PlanarEmbedding.has_predecessor"]], "has_successor() (planarembedding method)": [[175, "networkx.algorithms.planarity.PlanarEmbedding.has_successor"]], "in_degree (planarembedding property)": [[176, "networkx.algorithms.planarity.PlanarEmbedding.in_degree"]], "in_edges (planarembedding property)": [[177, "networkx.algorithms.planarity.PlanarEmbedding.in_edges"]], "is_directed() (planarembedding method)": [[178, "networkx.algorithms.planarity.PlanarEmbedding.is_directed"]], "is_multigraph() (planarembedding method)": [[179, "networkx.algorithms.planarity.PlanarEmbedding.is_multigraph"]], "name (planarembedding property)": [[180, "networkx.algorithms.planarity.PlanarEmbedding.name"]], "nbunch_iter() (planarembedding method)": [[181, "networkx.algorithms.planarity.PlanarEmbedding.nbunch_iter"]], "neighbors() (planarembedding method)": [[182, "networkx.algorithms.planarity.PlanarEmbedding.neighbors"]], "neighbors_cw_order() (planarembedding method)": [[183, "networkx.algorithms.planarity.PlanarEmbedding.neighbors_cw_order"]], "next_face_half_edge() (planarembedding method)": [[184, "networkx.algorithms.planarity.PlanarEmbedding.next_face_half_edge"]], "nodes (planarembedding property)": [[185, "networkx.algorithms.planarity.PlanarEmbedding.nodes"]], "number_of_edges() (planarembedding method)": [[186, "networkx.algorithms.planarity.PlanarEmbedding.number_of_edges"]], "number_of_nodes() (planarembedding method)": [[187, "networkx.algorithms.planarity.PlanarEmbedding.number_of_nodes"]], "order() (planarembedding method)": [[188, "networkx.algorithms.planarity.PlanarEmbedding.order"]], "out_degree (planarembedding property)": [[189, "networkx.algorithms.planarity.PlanarEmbedding.out_degree"]], "out_edges (planarembedding property)": [[190, "networkx.algorithms.planarity.PlanarEmbedding.out_edges"]], "pred (planarembedding property)": [[191, "networkx.algorithms.planarity.PlanarEmbedding.pred"]], "predecessors() (planarembedding method)": [[192, "networkx.algorithms.planarity.PlanarEmbedding.predecessors"]], "remove_edge() (planarembedding method)": [[193, "networkx.algorithms.planarity.PlanarEmbedding.remove_edge"]], "remove_edges_from() (planarembedding method)": [[194, "networkx.algorithms.planarity.PlanarEmbedding.remove_edges_from"]], "remove_node() (planarembedding method)": [[195, "networkx.algorithms.planarity.PlanarEmbedding.remove_node"]], "remove_nodes_from() (planarembedding method)": [[196, "networkx.algorithms.planarity.PlanarEmbedding.remove_nodes_from"]], "reverse() (planarembedding method)": [[197, "networkx.algorithms.planarity.PlanarEmbedding.reverse"]], "set_data() (planarembedding method)": [[198, "networkx.algorithms.planarity.PlanarEmbedding.set_data"]], "size() (planarembedding method)": [[199, "networkx.algorithms.planarity.PlanarEmbedding.size"]], "subgraph() (planarembedding method)": [[200, "networkx.algorithms.planarity.PlanarEmbedding.subgraph"]], "succ (planarembedding property)": [[201, "networkx.algorithms.planarity.PlanarEmbedding.succ"]], "successors() (planarembedding method)": [[202, "networkx.algorithms.planarity.PlanarEmbedding.successors"]], "to_directed() (planarembedding method)": [[203, "networkx.algorithms.planarity.PlanarEmbedding.to_directed"]], "to_directed_class() (planarembedding method)": [[204, "networkx.algorithms.planarity.PlanarEmbedding.to_directed_class"]], "to_undirected() (planarembedding method)": [[205, "networkx.algorithms.planarity.PlanarEmbedding.to_undirected"]], "to_undirected_class() (planarembedding method)": [[206, "networkx.algorithms.planarity.PlanarEmbedding.to_undirected_class"]], "traverse_face() (planarembedding method)": [[207, "networkx.algorithms.planarity.PlanarEmbedding.traverse_face"]], "update() (planarembedding method)": [[208, "networkx.algorithms.planarity.PlanarEmbedding.update"]], "find_optimum() (edmonds method)": [[209, "networkx.algorithms.tree.branchings.Edmonds.find_optimum"]], "clique_removal() (in module networkx.algorithms.approximation.clique)": [[210, "networkx.algorithms.approximation.clique.clique_removal"]], "large_clique_size() (in module networkx.algorithms.approximation.clique)": [[211, "networkx.algorithms.approximation.clique.large_clique_size"]], "max_clique() (in module networkx.algorithms.approximation.clique)": [[212, "networkx.algorithms.approximation.clique.max_clique"]], "maximum_independent_set() (in module networkx.algorithms.approximation.clique)": [[213, "networkx.algorithms.approximation.clique.maximum_independent_set"]], "average_clustering() (in module networkx.algorithms.approximation.clustering_coefficient)": [[214, "networkx.algorithms.approximation.clustering_coefficient.average_clustering"]], "all_pairs_node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[215, "networkx.algorithms.approximation.connectivity.all_pairs_node_connectivity"]], "local_node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[216, "networkx.algorithms.approximation.connectivity.local_node_connectivity"]], "node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[217, "networkx.algorithms.approximation.connectivity.node_connectivity"]], "diameter() (in module networkx.algorithms.approximation.distance_measures)": [[218, "networkx.algorithms.approximation.distance_measures.diameter"]], "min_edge_dominating_set() (in module networkx.algorithms.approximation.dominating_set)": [[219, "networkx.algorithms.approximation.dominating_set.min_edge_dominating_set"]], "min_weighted_dominating_set() (in module networkx.algorithms.approximation.dominating_set)": [[220, "networkx.algorithms.approximation.dominating_set.min_weighted_dominating_set"]], "k_components() (in module networkx.algorithms.approximation.kcomponents)": [[221, "networkx.algorithms.approximation.kcomponents.k_components"]], "min_maximal_matching() (in module networkx.algorithms.approximation.matching)": [[222, "networkx.algorithms.approximation.matching.min_maximal_matching"]], "one_exchange() (in module networkx.algorithms.approximation.maxcut)": [[223, "networkx.algorithms.approximation.maxcut.one_exchange"]], "randomized_partitioning() (in module networkx.algorithms.approximation.maxcut)": [[224, "networkx.algorithms.approximation.maxcut.randomized_partitioning"]], "ramsey_r2() (in module networkx.algorithms.approximation.ramsey)": [[225, "networkx.algorithms.approximation.ramsey.ramsey_R2"]], "metric_closure() (in module networkx.algorithms.approximation.steinertree)": [[226, "networkx.algorithms.approximation.steinertree.metric_closure"]], "steiner_tree() (in module networkx.algorithms.approximation.steinertree)": [[227, "networkx.algorithms.approximation.steinertree.steiner_tree"]], "asadpour_atsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[228, "networkx.algorithms.approximation.traveling_salesman.asadpour_atsp"]], "christofides() (in module networkx.algorithms.approximation.traveling_salesman)": [[229, "networkx.algorithms.approximation.traveling_salesman.christofides"]], "greedy_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[230, "networkx.algorithms.approximation.traveling_salesman.greedy_tsp"]], "simulated_annealing_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[231, "networkx.algorithms.approximation.traveling_salesman.simulated_annealing_tsp"]], "threshold_accepting_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[232, "networkx.algorithms.approximation.traveling_salesman.threshold_accepting_tsp"]], "traveling_salesman_problem() (in module networkx.algorithms.approximation.traveling_salesman)": [[233, "networkx.algorithms.approximation.traveling_salesman.traveling_salesman_problem"]], "treewidth_min_degree() (in module networkx.algorithms.approximation.treewidth)": [[234, "networkx.algorithms.approximation.treewidth.treewidth_min_degree"]], "treewidth_min_fill_in() (in module networkx.algorithms.approximation.treewidth)": [[235, "networkx.algorithms.approximation.treewidth.treewidth_min_fill_in"]], "min_weighted_vertex_cover() (in module networkx.algorithms.approximation.vertex_cover)": [[236, "networkx.algorithms.approximation.vertex_cover.min_weighted_vertex_cover"]], "attribute_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[237, "networkx.algorithms.assortativity.attribute_assortativity_coefficient"]], "attribute_mixing_dict() (in module networkx.algorithms.assortativity)": [[238, "networkx.algorithms.assortativity.attribute_mixing_dict"]], "attribute_mixing_matrix() (in module networkx.algorithms.assortativity)": [[239, "networkx.algorithms.assortativity.attribute_mixing_matrix"]], "average_degree_connectivity() (in module networkx.algorithms.assortativity)": [[240, "networkx.algorithms.assortativity.average_degree_connectivity"]], "average_neighbor_degree() (in module networkx.algorithms.assortativity)": [[241, "networkx.algorithms.assortativity.average_neighbor_degree"]], "degree_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[242, "networkx.algorithms.assortativity.degree_assortativity_coefficient"]], "degree_mixing_dict() (in module networkx.algorithms.assortativity)": [[243, "networkx.algorithms.assortativity.degree_mixing_dict"]], "degree_mixing_matrix() (in module networkx.algorithms.assortativity)": [[244, "networkx.algorithms.assortativity.degree_mixing_matrix"]], "degree_pearson_correlation_coefficient() (in module networkx.algorithms.assortativity)": [[245, "networkx.algorithms.assortativity.degree_pearson_correlation_coefficient"]], "mixing_dict() (in module networkx.algorithms.assortativity)": [[246, "networkx.algorithms.assortativity.mixing_dict"]], "node_attribute_xy() (in module networkx.algorithms.assortativity)": [[247, "networkx.algorithms.assortativity.node_attribute_xy"]], "node_degree_xy() (in module networkx.algorithms.assortativity)": [[248, "networkx.algorithms.assortativity.node_degree_xy"]], "numeric_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[249, "networkx.algorithms.assortativity.numeric_assortativity_coefficient"]], "find_asteroidal_triple() (in module networkx.algorithms.asteroidal)": [[250, "networkx.algorithms.asteroidal.find_asteroidal_triple"]], "is_at_free() (in module networkx.algorithms.asteroidal)": [[251, "networkx.algorithms.asteroidal.is_at_free"]], "color() (in module networkx.algorithms.bipartite.basic)": [[252, "networkx.algorithms.bipartite.basic.color"]], "degrees() (in module networkx.algorithms.bipartite.basic)": [[253, "networkx.algorithms.bipartite.basic.degrees"]], "density() (in module networkx.algorithms.bipartite.basic)": [[254, "networkx.algorithms.bipartite.basic.density"]], "is_bipartite() (in module networkx.algorithms.bipartite.basic)": [[255, "networkx.algorithms.bipartite.basic.is_bipartite"]], "is_bipartite_node_set() (in module networkx.algorithms.bipartite.basic)": [[256, "networkx.algorithms.bipartite.basic.is_bipartite_node_set"]], "sets() (in module networkx.algorithms.bipartite.basic)": [[257, "networkx.algorithms.bipartite.basic.sets"]], "betweenness_centrality() (in module networkx.algorithms.bipartite.centrality)": [[258, "networkx.algorithms.bipartite.centrality.betweenness_centrality"]], "closeness_centrality() (in module networkx.algorithms.bipartite.centrality)": [[259, "networkx.algorithms.bipartite.centrality.closeness_centrality"]], "degree_centrality() (in module networkx.algorithms.bipartite.centrality)": [[260, "networkx.algorithms.bipartite.centrality.degree_centrality"]], "average_clustering() (in module networkx.algorithms.bipartite.cluster)": [[261, "networkx.algorithms.bipartite.cluster.average_clustering"]], "clustering() (in module networkx.algorithms.bipartite.cluster)": [[262, "networkx.algorithms.bipartite.cluster.clustering"]], "latapy_clustering() (in module networkx.algorithms.bipartite.cluster)": [[263, "networkx.algorithms.bipartite.cluster.latapy_clustering"]], "robins_alexander_clustering() (in module networkx.algorithms.bipartite.cluster)": [[264, "networkx.algorithms.bipartite.cluster.robins_alexander_clustering"]], "min_edge_cover() (in module networkx.algorithms.bipartite.covering)": [[265, "networkx.algorithms.bipartite.covering.min_edge_cover"]], "generate_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[266, "networkx.algorithms.bipartite.edgelist.generate_edgelist"]], "parse_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[267, "networkx.algorithms.bipartite.edgelist.parse_edgelist"]], "read_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[268, "networkx.algorithms.bipartite.edgelist.read_edgelist"]], "write_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[269, "networkx.algorithms.bipartite.edgelist.write_edgelist"]], "alternating_havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[270, "networkx.algorithms.bipartite.generators.alternating_havel_hakimi_graph"]], "complete_bipartite_graph() (in module networkx.algorithms.bipartite.generators)": [[271, "networkx.algorithms.bipartite.generators.complete_bipartite_graph"]], "configuration_model() (in module networkx.algorithms.bipartite.generators)": [[272, "networkx.algorithms.bipartite.generators.configuration_model"]], "gnmk_random_graph() (in module networkx.algorithms.bipartite.generators)": [[273, "networkx.algorithms.bipartite.generators.gnmk_random_graph"]], "havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[274, "networkx.algorithms.bipartite.generators.havel_hakimi_graph"]], "preferential_attachment_graph() (in module networkx.algorithms.bipartite.generators)": [[275, "networkx.algorithms.bipartite.generators.preferential_attachment_graph"]], "random_graph() (in module networkx.algorithms.bipartite.generators)": [[276, "networkx.algorithms.bipartite.generators.random_graph"]], "reverse_havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[277, "networkx.algorithms.bipartite.generators.reverse_havel_hakimi_graph"]], "eppstein_matching() (in module networkx.algorithms.bipartite.matching)": [[278, "networkx.algorithms.bipartite.matching.eppstein_matching"]], "hopcroft_karp_matching() (in module networkx.algorithms.bipartite.matching)": [[279, "networkx.algorithms.bipartite.matching.hopcroft_karp_matching"]], "maximum_matching() (in module networkx.algorithms.bipartite.matching)": [[280, "networkx.algorithms.bipartite.matching.maximum_matching"]], "minimum_weight_full_matching() (in module networkx.algorithms.bipartite.matching)": [[281, "networkx.algorithms.bipartite.matching.minimum_weight_full_matching"]], "to_vertex_cover() (in module networkx.algorithms.bipartite.matching)": [[282, "networkx.algorithms.bipartite.matching.to_vertex_cover"]], "biadjacency_matrix() (in module networkx.algorithms.bipartite.matrix)": [[283, "networkx.algorithms.bipartite.matrix.biadjacency_matrix"]], "from_biadjacency_matrix() (in module networkx.algorithms.bipartite.matrix)": [[284, "networkx.algorithms.bipartite.matrix.from_biadjacency_matrix"]], "collaboration_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[285, "networkx.algorithms.bipartite.projection.collaboration_weighted_projected_graph"]], "generic_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[286, "networkx.algorithms.bipartite.projection.generic_weighted_projected_graph"]], "overlap_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[287, "networkx.algorithms.bipartite.projection.overlap_weighted_projected_graph"]], "projected_graph() (in module networkx.algorithms.bipartite.projection)": [[288, "networkx.algorithms.bipartite.projection.projected_graph"]], "weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[289, "networkx.algorithms.bipartite.projection.weighted_projected_graph"]], "node_redundancy() (in module networkx.algorithms.bipartite.redundancy)": [[290, "networkx.algorithms.bipartite.redundancy.node_redundancy"]], "spectral_bipartivity() (in module networkx.algorithms.bipartite.spectral)": [[291, "networkx.algorithms.bipartite.spectral.spectral_bipartivity"]], "edge_boundary() (in module networkx.algorithms.boundary)": [[292, "networkx.algorithms.boundary.edge_boundary"]], "node_boundary() (in module networkx.algorithms.boundary)": [[293, "networkx.algorithms.boundary.node_boundary"]], "bridges() (in module networkx.algorithms.bridges)": [[294, "networkx.algorithms.bridges.bridges"]], "has_bridges() (in module networkx.algorithms.bridges)": [[295, "networkx.algorithms.bridges.has_bridges"]], "local_bridges() (in module networkx.algorithms.bridges)": [[296, "networkx.algorithms.bridges.local_bridges"]], "approximate_current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[297, "networkx.algorithms.centrality.approximate_current_flow_betweenness_centrality"]], "betweenness_centrality() (in module networkx.algorithms.centrality)": [[298, "networkx.algorithms.centrality.betweenness_centrality"]], "betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[299, "networkx.algorithms.centrality.betweenness_centrality_subset"]], "closeness_centrality() (in module networkx.algorithms.centrality)": [[300, "networkx.algorithms.centrality.closeness_centrality"]], "communicability_betweenness_centrality() (in module networkx.algorithms.centrality)": [[301, "networkx.algorithms.centrality.communicability_betweenness_centrality"]], "current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[302, "networkx.algorithms.centrality.current_flow_betweenness_centrality"]], "current_flow_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[303, "networkx.algorithms.centrality.current_flow_betweenness_centrality_subset"]], "current_flow_closeness_centrality() (in module networkx.algorithms.centrality)": [[304, "networkx.algorithms.centrality.current_flow_closeness_centrality"]], "degree_centrality() (in module networkx.algorithms.centrality)": [[305, "networkx.algorithms.centrality.degree_centrality"]], "dispersion() (in module networkx.algorithms.centrality)": [[306, "networkx.algorithms.centrality.dispersion"]], "edge_betweenness_centrality() (in module networkx.algorithms.centrality)": [[307, "networkx.algorithms.centrality.edge_betweenness_centrality"]], "edge_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[308, "networkx.algorithms.centrality.edge_betweenness_centrality_subset"]], "edge_current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[309, "networkx.algorithms.centrality.edge_current_flow_betweenness_centrality"]], "edge_current_flow_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[310, "networkx.algorithms.centrality.edge_current_flow_betweenness_centrality_subset"]], "edge_load_centrality() (in module networkx.algorithms.centrality)": [[311, "networkx.algorithms.centrality.edge_load_centrality"]], "eigenvector_centrality() (in module networkx.algorithms.centrality)": [[312, "networkx.algorithms.centrality.eigenvector_centrality"]], "eigenvector_centrality_numpy() (in module networkx.algorithms.centrality)": [[313, "networkx.algorithms.centrality.eigenvector_centrality_numpy"]], "estrada_index() (in module networkx.algorithms.centrality)": [[314, "networkx.algorithms.centrality.estrada_index"]], "global_reaching_centrality() (in module networkx.algorithms.centrality)": [[315, "networkx.algorithms.centrality.global_reaching_centrality"]], "group_betweenness_centrality() (in module networkx.algorithms.centrality)": [[316, "networkx.algorithms.centrality.group_betweenness_centrality"]], "group_closeness_centrality() (in module networkx.algorithms.centrality)": [[317, "networkx.algorithms.centrality.group_closeness_centrality"]], "group_degree_centrality() (in module networkx.algorithms.centrality)": [[318, "networkx.algorithms.centrality.group_degree_centrality"]], "group_in_degree_centrality() (in module networkx.algorithms.centrality)": [[319, "networkx.algorithms.centrality.group_in_degree_centrality"]], "group_out_degree_centrality() (in module networkx.algorithms.centrality)": [[320, "networkx.algorithms.centrality.group_out_degree_centrality"]], "harmonic_centrality() (in module networkx.algorithms.centrality)": [[321, "networkx.algorithms.centrality.harmonic_centrality"]], "in_degree_centrality() (in module networkx.algorithms.centrality)": [[322, "networkx.algorithms.centrality.in_degree_centrality"]], "incremental_closeness_centrality() (in module networkx.algorithms.centrality)": [[323, "networkx.algorithms.centrality.incremental_closeness_centrality"]], "information_centrality() (in module networkx.algorithms.centrality)": [[324, "networkx.algorithms.centrality.information_centrality"]], "katz_centrality() (in module networkx.algorithms.centrality)": [[325, "networkx.algorithms.centrality.katz_centrality"]], "katz_centrality_numpy() (in module networkx.algorithms.centrality)": [[326, "networkx.algorithms.centrality.katz_centrality_numpy"]], "laplacian_centrality() (in module networkx.algorithms.centrality)": [[327, "networkx.algorithms.centrality.laplacian_centrality"]], "load_centrality() (in module networkx.algorithms.centrality)": [[328, "networkx.algorithms.centrality.load_centrality"]], "local_reaching_centrality() (in module networkx.algorithms.centrality)": [[329, "networkx.algorithms.centrality.local_reaching_centrality"]], "out_degree_centrality() (in module networkx.algorithms.centrality)": [[330, "networkx.algorithms.centrality.out_degree_centrality"]], "percolation_centrality() (in module networkx.algorithms.centrality)": [[331, "networkx.algorithms.centrality.percolation_centrality"]], "prominent_group() (in module networkx.algorithms.centrality)": [[332, "networkx.algorithms.centrality.prominent_group"]], "second_order_centrality() (in module networkx.algorithms.centrality)": [[333, "networkx.algorithms.centrality.second_order_centrality"]], "subgraph_centrality() (in module networkx.algorithms.centrality)": [[334, "networkx.algorithms.centrality.subgraph_centrality"]], "subgraph_centrality_exp() (in module networkx.algorithms.centrality)": [[335, "networkx.algorithms.centrality.subgraph_centrality_exp"]], "trophic_differences() (in module networkx.algorithms.centrality)": [[336, "networkx.algorithms.centrality.trophic_differences"]], "trophic_incoherence_parameter() (in module networkx.algorithms.centrality)": [[337, "networkx.algorithms.centrality.trophic_incoherence_parameter"]], "trophic_levels() (in module networkx.algorithms.centrality)": [[338, "networkx.algorithms.centrality.trophic_levels"]], "voterank() (in module networkx.algorithms.centrality)": [[339, "networkx.algorithms.centrality.voterank"]], "chain_decomposition() (in module networkx.algorithms.chains)": [[340, "networkx.algorithms.chains.chain_decomposition"]], "chordal_graph_cliques() (in module networkx.algorithms.chordal)": [[341, "networkx.algorithms.chordal.chordal_graph_cliques"]], "chordal_graph_treewidth() (in module networkx.algorithms.chordal)": [[342, "networkx.algorithms.chordal.chordal_graph_treewidth"]], "complete_to_chordal_graph() (in module networkx.algorithms.chordal)": [[343, "networkx.algorithms.chordal.complete_to_chordal_graph"]], "find_induced_nodes() (in module networkx.algorithms.chordal)": [[344, "networkx.algorithms.chordal.find_induced_nodes"]], "is_chordal() (in module networkx.algorithms.chordal)": [[345, "networkx.algorithms.chordal.is_chordal"]], "cliques_containing_node() (in module networkx.algorithms.clique)": [[346, "networkx.algorithms.clique.cliques_containing_node"]], "enumerate_all_cliques() (in module networkx.algorithms.clique)": [[347, "networkx.algorithms.clique.enumerate_all_cliques"]], "find_cliques() (in module networkx.algorithms.clique)": [[348, "networkx.algorithms.clique.find_cliques"]], "find_cliques_recursive() (in module networkx.algorithms.clique)": [[349, "networkx.algorithms.clique.find_cliques_recursive"]], "graph_clique_number() (in module networkx.algorithms.clique)": [[350, "networkx.algorithms.clique.graph_clique_number"]], "graph_number_of_cliques() (in module networkx.algorithms.clique)": [[351, "networkx.algorithms.clique.graph_number_of_cliques"]], "make_clique_bipartite() (in module networkx.algorithms.clique)": [[352, "networkx.algorithms.clique.make_clique_bipartite"]], "make_max_clique_graph() (in module networkx.algorithms.clique)": [[353, "networkx.algorithms.clique.make_max_clique_graph"]], "max_weight_clique() (in module networkx.algorithms.clique)": [[354, "networkx.algorithms.clique.max_weight_clique"]], "node_clique_number() (in module networkx.algorithms.clique)": [[355, "networkx.algorithms.clique.node_clique_number"]], "number_of_cliques() (in module networkx.algorithms.clique)": [[356, "networkx.algorithms.clique.number_of_cliques"]], "average_clustering() (in module networkx.algorithms.cluster)": [[357, "networkx.algorithms.cluster.average_clustering"]], "clustering() (in module networkx.algorithms.cluster)": [[358, "networkx.algorithms.cluster.clustering"]], "generalized_degree() (in module networkx.algorithms.cluster)": [[359, "networkx.algorithms.cluster.generalized_degree"]], "square_clustering() (in module networkx.algorithms.cluster)": [[360, "networkx.algorithms.cluster.square_clustering"]], "transitivity() (in module networkx.algorithms.cluster)": [[361, "networkx.algorithms.cluster.transitivity"]], "triangles() (in module networkx.algorithms.cluster)": [[362, "networkx.algorithms.cluster.triangles"]], "equitable_color() (in module networkx.algorithms.coloring)": [[363, "networkx.algorithms.coloring.equitable_color"]], "greedy_color() (in module networkx.algorithms.coloring)": [[364, "networkx.algorithms.coloring.greedy_color"]], "strategy_connected_sequential() (in module networkx.algorithms.coloring)": [[365, "networkx.algorithms.coloring.strategy_connected_sequential"]], "strategy_connected_sequential_bfs() (in module networkx.algorithms.coloring)": [[366, "networkx.algorithms.coloring.strategy_connected_sequential_bfs"]], "strategy_connected_sequential_dfs() (in module networkx.algorithms.coloring)": [[367, "networkx.algorithms.coloring.strategy_connected_sequential_dfs"]], "strategy_independent_set() (in module networkx.algorithms.coloring)": [[368, "networkx.algorithms.coloring.strategy_independent_set"]], "strategy_largest_first() (in module networkx.algorithms.coloring)": [[369, "networkx.algorithms.coloring.strategy_largest_first"]], "strategy_random_sequential() (in module networkx.algorithms.coloring)": [[370, "networkx.algorithms.coloring.strategy_random_sequential"]], "strategy_saturation_largest_first() (in module networkx.algorithms.coloring)": [[371, "networkx.algorithms.coloring.strategy_saturation_largest_first"]], "strategy_smallest_last() (in module networkx.algorithms.coloring)": [[372, "networkx.algorithms.coloring.strategy_smallest_last"]], "communicability() (in module networkx.algorithms.communicability_alg)": [[373, "networkx.algorithms.communicability_alg.communicability"]], "communicability_exp() (in module networkx.algorithms.communicability_alg)": [[374, "networkx.algorithms.communicability_alg.communicability_exp"]], "asyn_fluidc() (in module networkx.algorithms.community.asyn_fluid)": [[375, "networkx.algorithms.community.asyn_fluid.asyn_fluidc"]], "girvan_newman() (in module networkx.algorithms.community.centrality)": [[376, "networkx.algorithms.community.centrality.girvan_newman"]], "is_partition() (in module networkx.algorithms.community.community_utils)": [[377, "networkx.algorithms.community.community_utils.is_partition"]], "k_clique_communities() (in module networkx.algorithms.community.kclique)": [[378, "networkx.algorithms.community.kclique.k_clique_communities"]], "kernighan_lin_bisection() (in module networkx.algorithms.community.kernighan_lin)": [[379, "networkx.algorithms.community.kernighan_lin.kernighan_lin_bisection"]], "asyn_lpa_communities() (in module networkx.algorithms.community.label_propagation)": [[380, "networkx.algorithms.community.label_propagation.asyn_lpa_communities"]], "label_propagation_communities() (in module networkx.algorithms.community.label_propagation)": [[381, "networkx.algorithms.community.label_propagation.label_propagation_communities"]], "louvain_communities() (in module networkx.algorithms.community.louvain)": [[382, "networkx.algorithms.community.louvain.louvain_communities"]], "louvain_partitions() (in module networkx.algorithms.community.louvain)": [[383, "networkx.algorithms.community.louvain.louvain_partitions"]], "lukes_partitioning() (in module networkx.algorithms.community.lukes)": [[384, "networkx.algorithms.community.lukes.lukes_partitioning"]], "greedy_modularity_communities() (in module networkx.algorithms.community.modularity_max)": [[385, "networkx.algorithms.community.modularity_max.greedy_modularity_communities"]], "naive_greedy_modularity_communities() (in module networkx.algorithms.community.modularity_max)": [[386, "networkx.algorithms.community.modularity_max.naive_greedy_modularity_communities"]], "modularity() (in module networkx.algorithms.community.quality)": [[387, "networkx.algorithms.community.quality.modularity"]], "partition_quality() (in module networkx.algorithms.community.quality)": [[388, "networkx.algorithms.community.quality.partition_quality"]], "articulation_points() (in module networkx.algorithms.components)": [[389, "networkx.algorithms.components.articulation_points"]], "attracting_components() (in module networkx.algorithms.components)": [[390, "networkx.algorithms.components.attracting_components"]], "biconnected_component_edges() (in module networkx.algorithms.components)": [[391, "networkx.algorithms.components.biconnected_component_edges"]], "biconnected_components() (in module networkx.algorithms.components)": [[392, "networkx.algorithms.components.biconnected_components"]], "condensation() (in module networkx.algorithms.components)": [[393, "networkx.algorithms.components.condensation"]], "connected_components() (in module networkx.algorithms.components)": [[394, "networkx.algorithms.components.connected_components"]], "is_attracting_component() (in module networkx.algorithms.components)": [[395, "networkx.algorithms.components.is_attracting_component"]], "is_biconnected() (in module networkx.algorithms.components)": [[396, "networkx.algorithms.components.is_biconnected"]], "is_connected() (in module networkx.algorithms.components)": [[397, "networkx.algorithms.components.is_connected"]], "is_semiconnected() (in module networkx.algorithms.components)": [[398, "networkx.algorithms.components.is_semiconnected"]], "is_strongly_connected() (in module networkx.algorithms.components)": [[399, "networkx.algorithms.components.is_strongly_connected"]], "is_weakly_connected() (in module networkx.algorithms.components)": [[400, "networkx.algorithms.components.is_weakly_connected"]], "kosaraju_strongly_connected_components() (in module networkx.algorithms.components)": [[401, "networkx.algorithms.components.kosaraju_strongly_connected_components"]], "node_connected_component() (in module networkx.algorithms.components)": [[402, "networkx.algorithms.components.node_connected_component"]], "number_attracting_components() (in module networkx.algorithms.components)": [[403, "networkx.algorithms.components.number_attracting_components"]], "number_connected_components() (in module networkx.algorithms.components)": [[404, "networkx.algorithms.components.number_connected_components"]], "number_strongly_connected_components() (in module networkx.algorithms.components)": [[405, "networkx.algorithms.components.number_strongly_connected_components"]], "number_weakly_connected_components() (in module networkx.algorithms.components)": [[406, "networkx.algorithms.components.number_weakly_connected_components"]], "strongly_connected_components() (in module networkx.algorithms.components)": [[407, "networkx.algorithms.components.strongly_connected_components"]], "strongly_connected_components_recursive() (in module networkx.algorithms.components)": [[408, "networkx.algorithms.components.strongly_connected_components_recursive"]], "weakly_connected_components() (in module networkx.algorithms.components)": [[409, "networkx.algorithms.components.weakly_connected_components"]], "all_pairs_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[410, "networkx.algorithms.connectivity.connectivity.all_pairs_node_connectivity"]], "average_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[411, "networkx.algorithms.connectivity.connectivity.average_node_connectivity"]], "edge_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[412, "networkx.algorithms.connectivity.connectivity.edge_connectivity"]], "local_edge_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[413, "networkx.algorithms.connectivity.connectivity.local_edge_connectivity"]], "local_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[414, "networkx.algorithms.connectivity.connectivity.local_node_connectivity"]], "node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[415, "networkx.algorithms.connectivity.connectivity.node_connectivity"]], "minimum_edge_cut() (in module networkx.algorithms.connectivity.cuts)": [[416, "networkx.algorithms.connectivity.cuts.minimum_edge_cut"]], "minimum_node_cut() (in module networkx.algorithms.connectivity.cuts)": [[417, "networkx.algorithms.connectivity.cuts.minimum_node_cut"]], "minimum_st_edge_cut() (in module networkx.algorithms.connectivity.cuts)": [[418, "networkx.algorithms.connectivity.cuts.minimum_st_edge_cut"]], "minimum_st_node_cut() (in module networkx.algorithms.connectivity.cuts)": [[419, "networkx.algorithms.connectivity.cuts.minimum_st_node_cut"]], "edge_disjoint_paths() (in module networkx.algorithms.connectivity.disjoint_paths)": [[420, "networkx.algorithms.connectivity.disjoint_paths.edge_disjoint_paths"]], "node_disjoint_paths() (in module networkx.algorithms.connectivity.disjoint_paths)": [[421, "networkx.algorithms.connectivity.disjoint_paths.node_disjoint_paths"]], "is_k_edge_connected() (in module networkx.algorithms.connectivity.edge_augmentation)": [[422, "networkx.algorithms.connectivity.edge_augmentation.is_k_edge_connected"]], "is_locally_k_edge_connected() (in module networkx.algorithms.connectivity.edge_augmentation)": [[423, "networkx.algorithms.connectivity.edge_augmentation.is_locally_k_edge_connected"]], "k_edge_augmentation() (in module networkx.algorithms.connectivity.edge_augmentation)": [[424, "networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation"]], "edgecomponentauxgraph (class in networkx.algorithms.connectivity.edge_kcomponents)": [[425, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph"]], "__init__() (edgecomponentauxgraph method)": [[425, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.__init__"]], "bridge_components() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[426, "networkx.algorithms.connectivity.edge_kcomponents.bridge_components"]], "k_edge_components() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[427, "networkx.algorithms.connectivity.edge_kcomponents.k_edge_components"]], "k_edge_subgraphs() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[428, "networkx.algorithms.connectivity.edge_kcomponents.k_edge_subgraphs"]], "k_components() (in module networkx.algorithms.connectivity.kcomponents)": [[429, "networkx.algorithms.connectivity.kcomponents.k_components"]], "all_node_cuts() (in module networkx.algorithms.connectivity.kcutsets)": [[430, "networkx.algorithms.connectivity.kcutsets.all_node_cuts"]], "stoer_wagner() (in module networkx.algorithms.connectivity.stoerwagner)": [[431, "networkx.algorithms.connectivity.stoerwagner.stoer_wagner"]], "build_auxiliary_edge_connectivity() (in module networkx.algorithms.connectivity.utils)": [[432, "networkx.algorithms.connectivity.utils.build_auxiliary_edge_connectivity"]], "build_auxiliary_node_connectivity() (in module networkx.algorithms.connectivity.utils)": [[433, "networkx.algorithms.connectivity.utils.build_auxiliary_node_connectivity"]], "core_number() (in module networkx.algorithms.core)": [[434, "networkx.algorithms.core.core_number"]], "k_core() (in module networkx.algorithms.core)": [[435, "networkx.algorithms.core.k_core"]], "k_corona() (in module networkx.algorithms.core)": [[436, "networkx.algorithms.core.k_corona"]], "k_crust() (in module networkx.algorithms.core)": [[437, "networkx.algorithms.core.k_crust"]], "k_shell() (in module networkx.algorithms.core)": [[438, "networkx.algorithms.core.k_shell"]], "k_truss() (in module networkx.algorithms.core)": [[439, "networkx.algorithms.core.k_truss"]], "onion_layers() (in module networkx.algorithms.core)": [[440, "networkx.algorithms.core.onion_layers"]], "is_edge_cover() (in module networkx.algorithms.covering)": [[441, "networkx.algorithms.covering.is_edge_cover"]], "min_edge_cover() (in module networkx.algorithms.covering)": [[442, "networkx.algorithms.covering.min_edge_cover"]], "boundary_expansion() (in module networkx.algorithms.cuts)": [[443, "networkx.algorithms.cuts.boundary_expansion"]], "conductance() (in module networkx.algorithms.cuts)": [[444, "networkx.algorithms.cuts.conductance"]], "cut_size() (in module networkx.algorithms.cuts)": [[445, "networkx.algorithms.cuts.cut_size"]], "edge_expansion() (in module networkx.algorithms.cuts)": [[446, "networkx.algorithms.cuts.edge_expansion"]], "mixing_expansion() (in module networkx.algorithms.cuts)": [[447, "networkx.algorithms.cuts.mixing_expansion"]], "node_expansion() (in module networkx.algorithms.cuts)": [[448, "networkx.algorithms.cuts.node_expansion"]], "normalized_cut_size() (in module networkx.algorithms.cuts)": [[449, "networkx.algorithms.cuts.normalized_cut_size"]], "volume() (in module networkx.algorithms.cuts)": [[450, "networkx.algorithms.cuts.volume"]], "cycle_basis() (in module networkx.algorithms.cycles)": [[451, "networkx.algorithms.cycles.cycle_basis"]], "find_cycle() (in module networkx.algorithms.cycles)": [[452, "networkx.algorithms.cycles.find_cycle"]], "minimum_cycle_basis() (in module networkx.algorithms.cycles)": [[453, "networkx.algorithms.cycles.minimum_cycle_basis"]], "recursive_simple_cycles() (in module networkx.algorithms.cycles)": [[454, "networkx.algorithms.cycles.recursive_simple_cycles"]], "simple_cycles() (in module networkx.algorithms.cycles)": [[455, "networkx.algorithms.cycles.simple_cycles"]], "d_separated() (in module networkx.algorithms.d_separation)": [[456, "networkx.algorithms.d_separation.d_separated"]], "all_topological_sorts() (in module networkx.algorithms.dag)": [[457, "networkx.algorithms.dag.all_topological_sorts"]], "ancestors() (in module networkx.algorithms.dag)": [[458, "networkx.algorithms.dag.ancestors"]], "antichains() (in module networkx.algorithms.dag)": [[459, "networkx.algorithms.dag.antichains"]], "dag_longest_path() (in module networkx.algorithms.dag)": [[460, "networkx.algorithms.dag.dag_longest_path"]], "dag_longest_path_length() (in module networkx.algorithms.dag)": [[461, "networkx.algorithms.dag.dag_longest_path_length"]], "dag_to_branching() (in module networkx.algorithms.dag)": [[462, "networkx.algorithms.dag.dag_to_branching"]], "descendants() (in module networkx.algorithms.dag)": [[463, "networkx.algorithms.dag.descendants"]], "is_aperiodic() (in module networkx.algorithms.dag)": [[464, "networkx.algorithms.dag.is_aperiodic"]], "is_directed_acyclic_graph() (in module networkx.algorithms.dag)": [[465, "networkx.algorithms.dag.is_directed_acyclic_graph"]], "lexicographical_topological_sort() (in module networkx.algorithms.dag)": [[466, "networkx.algorithms.dag.lexicographical_topological_sort"]], "topological_generations() (in module networkx.algorithms.dag)": [[467, "networkx.algorithms.dag.topological_generations"]], "topological_sort() (in module networkx.algorithms.dag)": [[468, "networkx.algorithms.dag.topological_sort"]], "transitive_closure() (in module networkx.algorithms.dag)": [[469, "networkx.algorithms.dag.transitive_closure"]], "transitive_closure_dag() (in module networkx.algorithms.dag)": [[470, "networkx.algorithms.dag.transitive_closure_dag"]], "transitive_reduction() (in module networkx.algorithms.dag)": [[471, "networkx.algorithms.dag.transitive_reduction"]], "barycenter() (in module networkx.algorithms.distance_measures)": [[472, "networkx.algorithms.distance_measures.barycenter"]], "center() (in module networkx.algorithms.distance_measures)": [[473, "networkx.algorithms.distance_measures.center"]], "diameter() (in module networkx.algorithms.distance_measures)": [[474, "networkx.algorithms.distance_measures.diameter"]], "eccentricity() (in module networkx.algorithms.distance_measures)": [[475, "networkx.algorithms.distance_measures.eccentricity"]], "periphery() (in module networkx.algorithms.distance_measures)": [[476, "networkx.algorithms.distance_measures.periphery"]], "radius() (in module networkx.algorithms.distance_measures)": [[477, "networkx.algorithms.distance_measures.radius"]], "resistance_distance() (in module networkx.algorithms.distance_measures)": [[478, "networkx.algorithms.distance_measures.resistance_distance"]], "global_parameters() (in module networkx.algorithms.distance_regular)": [[479, "networkx.algorithms.distance_regular.global_parameters"]], "intersection_array() (in module networkx.algorithms.distance_regular)": [[480, "networkx.algorithms.distance_regular.intersection_array"]], "is_distance_regular() (in module networkx.algorithms.distance_regular)": [[481, "networkx.algorithms.distance_regular.is_distance_regular"]], "is_strongly_regular() (in module networkx.algorithms.distance_regular)": [[482, "networkx.algorithms.distance_regular.is_strongly_regular"]], "dominance_frontiers() (in module networkx.algorithms.dominance)": [[483, "networkx.algorithms.dominance.dominance_frontiers"]], "immediate_dominators() (in module networkx.algorithms.dominance)": [[484, "networkx.algorithms.dominance.immediate_dominators"]], "dominating_set() (in module networkx.algorithms.dominating)": [[485, "networkx.algorithms.dominating.dominating_set"]], "is_dominating_set() (in module networkx.algorithms.dominating)": [[486, "networkx.algorithms.dominating.is_dominating_set"]], "efficiency() (in module networkx.algorithms.efficiency_measures)": [[487, "networkx.algorithms.efficiency_measures.efficiency"]], "global_efficiency() (in module networkx.algorithms.efficiency_measures)": [[488, "networkx.algorithms.efficiency_measures.global_efficiency"]], "local_efficiency() (in module networkx.algorithms.efficiency_measures)": [[489, "networkx.algorithms.efficiency_measures.local_efficiency"]], "eulerian_circuit() (in module networkx.algorithms.euler)": [[490, "networkx.algorithms.euler.eulerian_circuit"]], "eulerian_path() (in module networkx.algorithms.euler)": [[491, "networkx.algorithms.euler.eulerian_path"]], "eulerize() (in module networkx.algorithms.euler)": [[492, "networkx.algorithms.euler.eulerize"]], "has_eulerian_path() (in module networkx.algorithms.euler)": [[493, "networkx.algorithms.euler.has_eulerian_path"]], "is_eulerian() (in module networkx.algorithms.euler)": [[494, "networkx.algorithms.euler.is_eulerian"]], "is_semieulerian() (in module networkx.algorithms.euler)": [[495, "networkx.algorithms.euler.is_semieulerian"]], "boykov_kolmogorov() (in module networkx.algorithms.flow)": [[496, "networkx.algorithms.flow.boykov_kolmogorov"]], "build_residual_network() (in module networkx.algorithms.flow)": [[497, "networkx.algorithms.flow.build_residual_network"]], "capacity_scaling() (in module networkx.algorithms.flow)": [[498, "networkx.algorithms.flow.capacity_scaling"]], "cost_of_flow() (in module networkx.algorithms.flow)": [[499, "networkx.algorithms.flow.cost_of_flow"]], "dinitz() (in module networkx.algorithms.flow)": [[500, "networkx.algorithms.flow.dinitz"]], "edmonds_karp() (in module networkx.algorithms.flow)": [[501, "networkx.algorithms.flow.edmonds_karp"]], "gomory_hu_tree() (in module networkx.algorithms.flow)": [[502, "networkx.algorithms.flow.gomory_hu_tree"]], "max_flow_min_cost() (in module networkx.algorithms.flow)": [[503, "networkx.algorithms.flow.max_flow_min_cost"]], "maximum_flow() (in module networkx.algorithms.flow)": [[504, "networkx.algorithms.flow.maximum_flow"]], "maximum_flow_value() (in module networkx.algorithms.flow)": [[505, "networkx.algorithms.flow.maximum_flow_value"]], "min_cost_flow() (in module networkx.algorithms.flow)": [[506, "networkx.algorithms.flow.min_cost_flow"]], "min_cost_flow_cost() (in module networkx.algorithms.flow)": [[507, "networkx.algorithms.flow.min_cost_flow_cost"]], "minimum_cut() (in module networkx.algorithms.flow)": [[508, "networkx.algorithms.flow.minimum_cut"]], "minimum_cut_value() (in module networkx.algorithms.flow)": [[509, "networkx.algorithms.flow.minimum_cut_value"]], "network_simplex() (in module networkx.algorithms.flow)": [[510, "networkx.algorithms.flow.network_simplex"]], "preflow_push() (in module networkx.algorithms.flow)": [[511, "networkx.algorithms.flow.preflow_push"]], "shortest_augmenting_path() (in module networkx.algorithms.flow)": [[512, "networkx.algorithms.flow.shortest_augmenting_path"]], "weisfeiler_lehman_graph_hash() (in module networkx.algorithms.graph_hashing)": [[513, "networkx.algorithms.graph_hashing.weisfeiler_lehman_graph_hash"]], "weisfeiler_lehman_subgraph_hashes() (in module networkx.algorithms.graph_hashing)": [[514, "networkx.algorithms.graph_hashing.weisfeiler_lehman_subgraph_hashes"]], "is_digraphical() (in module networkx.algorithms.graphical)": [[515, "networkx.algorithms.graphical.is_digraphical"]], "is_graphical() (in module networkx.algorithms.graphical)": [[516, "networkx.algorithms.graphical.is_graphical"]], "is_multigraphical() (in module networkx.algorithms.graphical)": [[517, "networkx.algorithms.graphical.is_multigraphical"]], "is_pseudographical() (in module networkx.algorithms.graphical)": [[518, "networkx.algorithms.graphical.is_pseudographical"]], "is_valid_degree_sequence_erdos_gallai() (in module networkx.algorithms.graphical)": [[519, "networkx.algorithms.graphical.is_valid_degree_sequence_erdos_gallai"]], "is_valid_degree_sequence_havel_hakimi() (in module networkx.algorithms.graphical)": [[520, "networkx.algorithms.graphical.is_valid_degree_sequence_havel_hakimi"]], "flow_hierarchy() (in module networkx.algorithms.hierarchy)": [[521, "networkx.algorithms.hierarchy.flow_hierarchy"]], "is_kl_connected() (in module networkx.algorithms.hybrid)": [[522, "networkx.algorithms.hybrid.is_kl_connected"]], "kl_connected_subgraph() (in module networkx.algorithms.hybrid)": [[523, "networkx.algorithms.hybrid.kl_connected_subgraph"]], "is_isolate() (in module networkx.algorithms.isolate)": [[524, "networkx.algorithms.isolate.is_isolate"]], "isolates() (in module networkx.algorithms.isolate)": [[525, "networkx.algorithms.isolate.isolates"]], "number_of_isolates() (in module networkx.algorithms.isolate)": [[526, "networkx.algorithms.isolate.number_of_isolates"]], "__init__() (digraphmatcher method)": [[527, "networkx.algorithms.isomorphism.DiGraphMatcher.__init__"]], "candidate_pairs_iter() (digraphmatcher method)": [[528, "networkx.algorithms.isomorphism.DiGraphMatcher.candidate_pairs_iter"]], "initialize() (digraphmatcher method)": [[529, "networkx.algorithms.isomorphism.DiGraphMatcher.initialize"]], "is_isomorphic() (digraphmatcher method)": [[530, "networkx.algorithms.isomorphism.DiGraphMatcher.is_isomorphic"]], "isomorphisms_iter() (digraphmatcher method)": [[531, "networkx.algorithms.isomorphism.DiGraphMatcher.isomorphisms_iter"]], "match() (digraphmatcher method)": [[532, "networkx.algorithms.isomorphism.DiGraphMatcher.match"]], "semantic_feasibility() (digraphmatcher method)": [[533, "networkx.algorithms.isomorphism.DiGraphMatcher.semantic_feasibility"]], "subgraph_is_isomorphic() (digraphmatcher method)": [[534, "networkx.algorithms.isomorphism.DiGraphMatcher.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (digraphmatcher method)": [[535, "networkx.algorithms.isomorphism.DiGraphMatcher.subgraph_isomorphisms_iter"]], "syntactic_feasibility() (digraphmatcher method)": [[536, "networkx.algorithms.isomorphism.DiGraphMatcher.syntactic_feasibility"]], "__init__() (graphmatcher method)": [[537, "networkx.algorithms.isomorphism.GraphMatcher.__init__"]], "candidate_pairs_iter() (graphmatcher method)": [[538, "networkx.algorithms.isomorphism.GraphMatcher.candidate_pairs_iter"]], "initialize() (graphmatcher method)": [[539, "networkx.algorithms.isomorphism.GraphMatcher.initialize"]], "is_isomorphic() (graphmatcher method)": [[540, "networkx.algorithms.isomorphism.GraphMatcher.is_isomorphic"]], "isomorphisms_iter() (graphmatcher method)": [[541, "networkx.algorithms.isomorphism.GraphMatcher.isomorphisms_iter"]], "match() (graphmatcher method)": [[542, "networkx.algorithms.isomorphism.GraphMatcher.match"]], "semantic_feasibility() (graphmatcher method)": [[543, "networkx.algorithms.isomorphism.GraphMatcher.semantic_feasibility"]], "subgraph_is_isomorphic() (graphmatcher method)": [[544, "networkx.algorithms.isomorphism.GraphMatcher.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (graphmatcher method)": [[545, "networkx.algorithms.isomorphism.GraphMatcher.subgraph_isomorphisms_iter"]], "syntactic_feasibility() (graphmatcher method)": [[546, "networkx.algorithms.isomorphism.GraphMatcher.syntactic_feasibility"]], "ismags (class in networkx.algorithms.isomorphism)": [[547, "networkx.algorithms.isomorphism.ISMAGS"]], "__init__() (ismags method)": [[547, "networkx.algorithms.isomorphism.ISMAGS.__init__"]], "categorical_edge_match() (in module networkx.algorithms.isomorphism)": [[548, "networkx.algorithms.isomorphism.categorical_edge_match"]], "categorical_multiedge_match() (in module networkx.algorithms.isomorphism)": [[549, "networkx.algorithms.isomorphism.categorical_multiedge_match"]], "categorical_node_match() (in module networkx.algorithms.isomorphism)": [[550, "networkx.algorithms.isomorphism.categorical_node_match"]], "could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[551, "networkx.algorithms.isomorphism.could_be_isomorphic"]], "fast_could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[552, "networkx.algorithms.isomorphism.fast_could_be_isomorphic"]], "faster_could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[553, "networkx.algorithms.isomorphism.faster_could_be_isomorphic"]], "generic_edge_match() (in module networkx.algorithms.isomorphism)": [[554, "networkx.algorithms.isomorphism.generic_edge_match"]], "generic_multiedge_match() (in module networkx.algorithms.isomorphism)": [[555, "networkx.algorithms.isomorphism.generic_multiedge_match"]], "generic_node_match() (in module networkx.algorithms.isomorphism)": [[556, "networkx.algorithms.isomorphism.generic_node_match"]], "is_isomorphic() (in module networkx.algorithms.isomorphism)": [[557, "networkx.algorithms.isomorphism.is_isomorphic"]], "numerical_edge_match() (in module networkx.algorithms.isomorphism)": [[558, "networkx.algorithms.isomorphism.numerical_edge_match"]], "numerical_multiedge_match() (in module networkx.algorithms.isomorphism)": [[559, "networkx.algorithms.isomorphism.numerical_multiedge_match"]], "numerical_node_match() (in module networkx.algorithms.isomorphism)": [[560, "networkx.algorithms.isomorphism.numerical_node_match"]], "rooted_tree_isomorphism() (in module networkx.algorithms.isomorphism.tree_isomorphism)": [[561, "networkx.algorithms.isomorphism.tree_isomorphism.rooted_tree_isomorphism"]], "tree_isomorphism() (in module networkx.algorithms.isomorphism.tree_isomorphism)": [[562, "networkx.algorithms.isomorphism.tree_isomorphism.tree_isomorphism"]], "vf2pp_all_isomorphisms() (in module networkx.algorithms.isomorphism.vf2pp)": [[563, "networkx.algorithms.isomorphism.vf2pp.vf2pp_all_isomorphisms"]], "vf2pp_is_isomorphic() (in module networkx.algorithms.isomorphism.vf2pp)": [[564, "networkx.algorithms.isomorphism.vf2pp.vf2pp_is_isomorphic"]], "vf2pp_isomorphism() (in module networkx.algorithms.isomorphism.vf2pp)": [[565, "networkx.algorithms.isomorphism.vf2pp.vf2pp_isomorphism"]], "hits() (in module networkx.algorithms.link_analysis.hits_alg)": [[566, "networkx.algorithms.link_analysis.hits_alg.hits"]], "google_matrix() (in module networkx.algorithms.link_analysis.pagerank_alg)": [[567, "networkx.algorithms.link_analysis.pagerank_alg.google_matrix"]], "pagerank() (in module networkx.algorithms.link_analysis.pagerank_alg)": [[568, "networkx.algorithms.link_analysis.pagerank_alg.pagerank"]], "adamic_adar_index() (in module networkx.algorithms.link_prediction)": [[569, "networkx.algorithms.link_prediction.adamic_adar_index"]], "cn_soundarajan_hopcroft() (in module networkx.algorithms.link_prediction)": [[570, "networkx.algorithms.link_prediction.cn_soundarajan_hopcroft"]], "common_neighbor_centrality() (in module networkx.algorithms.link_prediction)": [[571, "networkx.algorithms.link_prediction.common_neighbor_centrality"]], "jaccard_coefficient() (in module networkx.algorithms.link_prediction)": [[572, "networkx.algorithms.link_prediction.jaccard_coefficient"]], "preferential_attachment() (in module networkx.algorithms.link_prediction)": [[573, "networkx.algorithms.link_prediction.preferential_attachment"]], "ra_index_soundarajan_hopcroft() (in module networkx.algorithms.link_prediction)": [[574, "networkx.algorithms.link_prediction.ra_index_soundarajan_hopcroft"]], "resource_allocation_index() (in module networkx.algorithms.link_prediction)": [[575, "networkx.algorithms.link_prediction.resource_allocation_index"]], "within_inter_cluster() (in module networkx.algorithms.link_prediction)": [[576, "networkx.algorithms.link_prediction.within_inter_cluster"]], "all_pairs_lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[577, "networkx.algorithms.lowest_common_ancestors.all_pairs_lowest_common_ancestor"]], "lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[578, "networkx.algorithms.lowest_common_ancestors.lowest_common_ancestor"]], "tree_all_pairs_lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[579, "networkx.algorithms.lowest_common_ancestors.tree_all_pairs_lowest_common_ancestor"]], "is_matching() (in module networkx.algorithms.matching)": [[580, "networkx.algorithms.matching.is_matching"]], "is_maximal_matching() (in module networkx.algorithms.matching)": [[581, "networkx.algorithms.matching.is_maximal_matching"]], "is_perfect_matching() (in module networkx.algorithms.matching)": [[582, "networkx.algorithms.matching.is_perfect_matching"]], "max_weight_matching() (in module networkx.algorithms.matching)": [[583, "networkx.algorithms.matching.max_weight_matching"]], "maximal_matching() (in module networkx.algorithms.matching)": [[584, "networkx.algorithms.matching.maximal_matching"]], "min_weight_matching() (in module networkx.algorithms.matching)": [[585, "networkx.algorithms.matching.min_weight_matching"]], "contracted_edge() (in module networkx.algorithms.minors)": [[586, "networkx.algorithms.minors.contracted_edge"]], "contracted_nodes() (in module networkx.algorithms.minors)": [[587, "networkx.algorithms.minors.contracted_nodes"]], "equivalence_classes() (in module networkx.algorithms.minors)": [[588, "networkx.algorithms.minors.equivalence_classes"]], "identified_nodes() (in module networkx.algorithms.minors)": [[589, "networkx.algorithms.minors.identified_nodes"]], "quotient_graph() (in module networkx.algorithms.minors)": [[590, "networkx.algorithms.minors.quotient_graph"]], "maximal_independent_set() (in module networkx.algorithms.mis)": [[591, "networkx.algorithms.mis.maximal_independent_set"]], "moral_graph() (in module networkx.algorithms.moral)": [[592, "networkx.algorithms.moral.moral_graph"]], "harmonic_function() (in module networkx.algorithms.node_classification)": [[593, "networkx.algorithms.node_classification.harmonic_function"]], "local_and_global_consistency() (in module networkx.algorithms.node_classification)": [[594, "networkx.algorithms.node_classification.local_and_global_consistency"]], "non_randomness() (in module networkx.algorithms.non_randomness)": [[595, "networkx.algorithms.non_randomness.non_randomness"]], "compose_all() (in module networkx.algorithms.operators.all)": [[596, "networkx.algorithms.operators.all.compose_all"]], "disjoint_union_all() (in module networkx.algorithms.operators.all)": [[597, "networkx.algorithms.operators.all.disjoint_union_all"]], "intersection_all() (in module networkx.algorithms.operators.all)": [[598, "networkx.algorithms.operators.all.intersection_all"]], "union_all() (in module networkx.algorithms.operators.all)": [[599, "networkx.algorithms.operators.all.union_all"]], "compose() (in module networkx.algorithms.operators.binary)": [[600, "networkx.algorithms.operators.binary.compose"]], "difference() (in module networkx.algorithms.operators.binary)": [[601, "networkx.algorithms.operators.binary.difference"]], "disjoint_union() (in module networkx.algorithms.operators.binary)": [[602, "networkx.algorithms.operators.binary.disjoint_union"]], "full_join() (in module networkx.algorithms.operators.binary)": [[603, "networkx.algorithms.operators.binary.full_join"]], "intersection() (in module networkx.algorithms.operators.binary)": [[604, "networkx.algorithms.operators.binary.intersection"]], "symmetric_difference() (in module networkx.algorithms.operators.binary)": [[605, "networkx.algorithms.operators.binary.symmetric_difference"]], "union() (in module networkx.algorithms.operators.binary)": [[606, "networkx.algorithms.operators.binary.union"]], "cartesian_product() (in module networkx.algorithms.operators.product)": [[607, "networkx.algorithms.operators.product.cartesian_product"]], "corona_product() (in module networkx.algorithms.operators.product)": [[608, "networkx.algorithms.operators.product.corona_product"]], "lexicographic_product() (in module networkx.algorithms.operators.product)": [[609, "networkx.algorithms.operators.product.lexicographic_product"]], "power() (in module networkx.algorithms.operators.product)": [[610, "networkx.algorithms.operators.product.power"]], "rooted_product() (in module networkx.algorithms.operators.product)": [[611, "networkx.algorithms.operators.product.rooted_product"]], "strong_product() (in module networkx.algorithms.operators.product)": [[612, "networkx.algorithms.operators.product.strong_product"]], "tensor_product() (in module networkx.algorithms.operators.product)": [[613, "networkx.algorithms.operators.product.tensor_product"]], "complement() (in module networkx.algorithms.operators.unary)": [[614, "networkx.algorithms.operators.unary.complement"]], "reverse() (in module networkx.algorithms.operators.unary)": [[615, "networkx.algorithms.operators.unary.reverse"]], "combinatorial_embedding_to_pos() (in module networkx.algorithms.planar_drawing)": [[616, "networkx.algorithms.planar_drawing.combinatorial_embedding_to_pos"]], "planarembedding (class in networkx.algorithms.planarity)": [[617, "networkx.algorithms.planarity.PlanarEmbedding"]], "__init__() (planarembedding method)": [[617, "networkx.algorithms.planarity.PlanarEmbedding.__init__"]], "check_planarity() (in module networkx.algorithms.planarity)": [[618, "networkx.algorithms.planarity.check_planarity"]], "is_planar() (in module networkx.algorithms.planarity)": [[619, "networkx.algorithms.planarity.is_planar"]], "chromatic_polynomial() (in module networkx.algorithms.polynomials)": [[620, "networkx.algorithms.polynomials.chromatic_polynomial"]], "tutte_polynomial() (in module networkx.algorithms.polynomials)": [[621, "networkx.algorithms.polynomials.tutte_polynomial"]], "overall_reciprocity() (in module networkx.algorithms.reciprocity)": [[622, "networkx.algorithms.reciprocity.overall_reciprocity"]], "reciprocity() (in module networkx.algorithms.reciprocity)": [[623, "networkx.algorithms.reciprocity.reciprocity"]], "is_k_regular() (in module networkx.algorithms.regular)": [[624, "networkx.algorithms.regular.is_k_regular"]], "is_regular() (in module networkx.algorithms.regular)": [[625, "networkx.algorithms.regular.is_regular"]], "k_factor() (in module networkx.algorithms.regular)": [[626, "networkx.algorithms.regular.k_factor"]], "rich_club_coefficient() (in module networkx.algorithms.richclub)": [[627, "networkx.algorithms.richclub.rich_club_coefficient"]], "astar_path() (in module networkx.algorithms.shortest_paths.astar)": [[628, "networkx.algorithms.shortest_paths.astar.astar_path"]], "astar_path_length() (in module networkx.algorithms.shortest_paths.astar)": [[629, "networkx.algorithms.shortest_paths.astar.astar_path_length"]], "floyd_warshall() (in module networkx.algorithms.shortest_paths.dense)": [[630, "networkx.algorithms.shortest_paths.dense.floyd_warshall"]], "floyd_warshall_numpy() (in module networkx.algorithms.shortest_paths.dense)": [[631, "networkx.algorithms.shortest_paths.dense.floyd_warshall_numpy"]], "floyd_warshall_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.dense)": [[632, "networkx.algorithms.shortest_paths.dense.floyd_warshall_predecessor_and_distance"]], "reconstruct_path() (in module networkx.algorithms.shortest_paths.dense)": [[633, "networkx.algorithms.shortest_paths.dense.reconstruct_path"]], "all_shortest_paths() (in module networkx.algorithms.shortest_paths.generic)": [[634, "networkx.algorithms.shortest_paths.generic.all_shortest_paths"]], "average_shortest_path_length() (in module networkx.algorithms.shortest_paths.generic)": [[635, "networkx.algorithms.shortest_paths.generic.average_shortest_path_length"]], "has_path() (in module networkx.algorithms.shortest_paths.generic)": [[636, "networkx.algorithms.shortest_paths.generic.has_path"]], "shortest_path() (in module networkx.algorithms.shortest_paths.generic)": [[637, "networkx.algorithms.shortest_paths.generic.shortest_path"]], "shortest_path_length() (in module networkx.algorithms.shortest_paths.generic)": [[638, "networkx.algorithms.shortest_paths.generic.shortest_path_length"]], "all_pairs_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[639, "networkx.algorithms.shortest_paths.unweighted.all_pairs_shortest_path"]], "all_pairs_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[640, "networkx.algorithms.shortest_paths.unweighted.all_pairs_shortest_path_length"]], "bidirectional_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[641, "networkx.algorithms.shortest_paths.unweighted.bidirectional_shortest_path"]], "predecessor() (in module networkx.algorithms.shortest_paths.unweighted)": [[642, "networkx.algorithms.shortest_paths.unweighted.predecessor"]], "single_source_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[643, "networkx.algorithms.shortest_paths.unweighted.single_source_shortest_path"]], "single_source_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[644, "networkx.algorithms.shortest_paths.unweighted.single_source_shortest_path_length"]], "single_target_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[645, "networkx.algorithms.shortest_paths.unweighted.single_target_shortest_path"]], "single_target_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[646, "networkx.algorithms.shortest_paths.unweighted.single_target_shortest_path_length"]], "all_pairs_bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[647, "networkx.algorithms.shortest_paths.weighted.all_pairs_bellman_ford_path"]], "all_pairs_bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[648, "networkx.algorithms.shortest_paths.weighted.all_pairs_bellman_ford_path_length"]], "all_pairs_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[649, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra"]], "all_pairs_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[650, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra_path"]], "all_pairs_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[651, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra_path_length"]], "bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[652, "networkx.algorithms.shortest_paths.weighted.bellman_ford_path"]], "bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[653, "networkx.algorithms.shortest_paths.weighted.bellman_ford_path_length"]], "bellman_ford_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.weighted)": [[654, "networkx.algorithms.shortest_paths.weighted.bellman_ford_predecessor_and_distance"]], "bidirectional_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[655, "networkx.algorithms.shortest_paths.weighted.bidirectional_dijkstra"]], "dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[656, "networkx.algorithms.shortest_paths.weighted.dijkstra_path"]], "dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[657, "networkx.algorithms.shortest_paths.weighted.dijkstra_path_length"]], "dijkstra_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.weighted)": [[658, "networkx.algorithms.shortest_paths.weighted.dijkstra_predecessor_and_distance"]], "find_negative_cycle() (in module networkx.algorithms.shortest_paths.weighted)": [[659, "networkx.algorithms.shortest_paths.weighted.find_negative_cycle"]], "goldberg_radzik() (in module networkx.algorithms.shortest_paths.weighted)": [[660, "networkx.algorithms.shortest_paths.weighted.goldberg_radzik"]], "johnson() (in module networkx.algorithms.shortest_paths.weighted)": [[661, "networkx.algorithms.shortest_paths.weighted.johnson"]], "multi_source_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[662, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra"]], "multi_source_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[663, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra_path"]], "multi_source_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[664, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra_path_length"]], "negative_edge_cycle() (in module networkx.algorithms.shortest_paths.weighted)": [[665, "networkx.algorithms.shortest_paths.weighted.negative_edge_cycle"]], "single_source_bellman_ford() (in module networkx.algorithms.shortest_paths.weighted)": [[666, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford"]], "single_source_bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[667, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford_path"]], "single_source_bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[668, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford_path_length"]], "single_source_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[669, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra"]], "single_source_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[670, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra_path"]], "single_source_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[671, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra_path_length"]], "generate_random_paths() (in module networkx.algorithms.similarity)": [[672, "networkx.algorithms.similarity.generate_random_paths"]], "graph_edit_distance() (in module networkx.algorithms.similarity)": [[673, "networkx.algorithms.similarity.graph_edit_distance"]], "optimal_edit_paths() (in module networkx.algorithms.similarity)": [[674, "networkx.algorithms.similarity.optimal_edit_paths"]], "optimize_edit_paths() (in module networkx.algorithms.similarity)": [[675, "networkx.algorithms.similarity.optimize_edit_paths"]], "optimize_graph_edit_distance() (in module networkx.algorithms.similarity)": [[676, "networkx.algorithms.similarity.optimize_graph_edit_distance"]], "panther_similarity() (in module networkx.algorithms.similarity)": [[677, "networkx.algorithms.similarity.panther_similarity"]], "simrank_similarity() (in module networkx.algorithms.similarity)": [[678, "networkx.algorithms.similarity.simrank_similarity"]], "all_simple_edge_paths() (in module networkx.algorithms.simple_paths)": [[679, "networkx.algorithms.simple_paths.all_simple_edge_paths"]], "all_simple_paths() (in module networkx.algorithms.simple_paths)": [[680, "networkx.algorithms.simple_paths.all_simple_paths"]], "is_simple_path() (in module networkx.algorithms.simple_paths)": [[681, "networkx.algorithms.simple_paths.is_simple_path"]], "shortest_simple_paths() (in module networkx.algorithms.simple_paths)": [[682, "networkx.algorithms.simple_paths.shortest_simple_paths"]], "lattice_reference() (in module networkx.algorithms.smallworld)": [[683, "networkx.algorithms.smallworld.lattice_reference"]], "omega() (in module networkx.algorithms.smallworld)": [[684, "networkx.algorithms.smallworld.omega"]], "random_reference() (in module networkx.algorithms.smallworld)": [[685, "networkx.algorithms.smallworld.random_reference"]], "sigma() (in module networkx.algorithms.smallworld)": [[686, "networkx.algorithms.smallworld.sigma"]], "s_metric() (in module networkx.algorithms.smetric)": [[687, "networkx.algorithms.smetric.s_metric"]], "spanner() (in module networkx.algorithms.sparsifiers)": [[688, "networkx.algorithms.sparsifiers.spanner"]], "constraint() (in module networkx.algorithms.structuralholes)": [[689, "networkx.algorithms.structuralholes.constraint"]], "effective_size() (in module networkx.algorithms.structuralholes)": [[690, "networkx.algorithms.structuralholes.effective_size"]], "local_constraint() (in module networkx.algorithms.structuralholes)": [[691, "networkx.algorithms.structuralholes.local_constraint"]], "dedensify() (in module networkx.algorithms.summarization)": [[692, "networkx.algorithms.summarization.dedensify"]], "snap_aggregation() (in module networkx.algorithms.summarization)": [[693, "networkx.algorithms.summarization.snap_aggregation"]], "connected_double_edge_swap() (in module networkx.algorithms.swap)": [[694, "networkx.algorithms.swap.connected_double_edge_swap"]], "directed_edge_swap() (in module networkx.algorithms.swap)": [[695, "networkx.algorithms.swap.directed_edge_swap"]], "double_edge_swap() (in module networkx.algorithms.swap)": [[696, "networkx.algorithms.swap.double_edge_swap"]], "find_threshold_graph() (in module networkx.algorithms.threshold)": [[697, "networkx.algorithms.threshold.find_threshold_graph"]], "is_threshold_graph() (in module networkx.algorithms.threshold)": [[698, "networkx.algorithms.threshold.is_threshold_graph"]], "hamiltonian_path() (in module networkx.algorithms.tournament)": [[699, "networkx.algorithms.tournament.hamiltonian_path"]], "is_reachable() (in module networkx.algorithms.tournament)": [[700, "networkx.algorithms.tournament.is_reachable"]], "is_strongly_connected() (in module networkx.algorithms.tournament)": [[701, "networkx.algorithms.tournament.is_strongly_connected"]], "is_tournament() (in module networkx.algorithms.tournament)": [[702, "networkx.algorithms.tournament.is_tournament"]], "random_tournament() (in module networkx.algorithms.tournament)": [[703, "networkx.algorithms.tournament.random_tournament"]], "score_sequence() (in module networkx.algorithms.tournament)": [[704, "networkx.algorithms.tournament.score_sequence"]], "bfs_beam_edges() (in module networkx.algorithms.traversal.beamsearch)": [[705, "networkx.algorithms.traversal.beamsearch.bfs_beam_edges"]], "bfs_edges() (in module networkx.algorithms.traversal.breadth_first_search)": [[706, "networkx.algorithms.traversal.breadth_first_search.bfs_edges"]], "bfs_layers() (in module networkx.algorithms.traversal.breadth_first_search)": [[707, "networkx.algorithms.traversal.breadth_first_search.bfs_layers"]], "bfs_predecessors() (in module networkx.algorithms.traversal.breadth_first_search)": [[708, "networkx.algorithms.traversal.breadth_first_search.bfs_predecessors"]], "bfs_successors() (in module networkx.algorithms.traversal.breadth_first_search)": [[709, "networkx.algorithms.traversal.breadth_first_search.bfs_successors"]], "bfs_tree() (in module networkx.algorithms.traversal.breadth_first_search)": [[710, "networkx.algorithms.traversal.breadth_first_search.bfs_tree"]], "descendants_at_distance() (in module networkx.algorithms.traversal.breadth_first_search)": [[711, "networkx.algorithms.traversal.breadth_first_search.descendants_at_distance"]], "dfs_edges() (in module networkx.algorithms.traversal.depth_first_search)": [[712, "networkx.algorithms.traversal.depth_first_search.dfs_edges"]], "dfs_labeled_edges() (in module networkx.algorithms.traversal.depth_first_search)": [[713, "networkx.algorithms.traversal.depth_first_search.dfs_labeled_edges"]], "dfs_postorder_nodes() (in module networkx.algorithms.traversal.depth_first_search)": [[714, "networkx.algorithms.traversal.depth_first_search.dfs_postorder_nodes"]], "dfs_predecessors() (in module networkx.algorithms.traversal.depth_first_search)": [[715, "networkx.algorithms.traversal.depth_first_search.dfs_predecessors"]], "dfs_preorder_nodes() (in module networkx.algorithms.traversal.depth_first_search)": [[716, "networkx.algorithms.traversal.depth_first_search.dfs_preorder_nodes"]], "dfs_successors() (in module networkx.algorithms.traversal.depth_first_search)": [[717, "networkx.algorithms.traversal.depth_first_search.dfs_successors"]], "dfs_tree() (in module networkx.algorithms.traversal.depth_first_search)": [[718, "networkx.algorithms.traversal.depth_first_search.dfs_tree"]], "edge_bfs() (in module networkx.algorithms.traversal.edgebfs)": [[719, "networkx.algorithms.traversal.edgebfs.edge_bfs"]], "edge_dfs() (in module networkx.algorithms.traversal.edgedfs)": [[720, "networkx.algorithms.traversal.edgedfs.edge_dfs"]], "arborescenceiterator (class in networkx.algorithms.tree.branchings)": [[721, "networkx.algorithms.tree.branchings.ArborescenceIterator"]], "__init__() (arborescenceiterator method)": [[721, "networkx.algorithms.tree.branchings.ArborescenceIterator.__init__"]], "edmonds (class in networkx.algorithms.tree.branchings)": [[722, "networkx.algorithms.tree.branchings.Edmonds"]], "__init__() (edmonds method)": [[722, "networkx.algorithms.tree.branchings.Edmonds.__init__"]], "branching_weight() (in module networkx.algorithms.tree.branchings)": [[723, "networkx.algorithms.tree.branchings.branching_weight"]], "greedy_branching() (in module networkx.algorithms.tree.branchings)": [[724, "networkx.algorithms.tree.branchings.greedy_branching"]], "maximum_branching() (in module networkx.algorithms.tree.branchings)": [[725, "networkx.algorithms.tree.branchings.maximum_branching"]], "maximum_spanning_arborescence() (in module networkx.algorithms.tree.branchings)": [[726, "networkx.algorithms.tree.branchings.maximum_spanning_arborescence"]], "minimum_branching() (in module networkx.algorithms.tree.branchings)": [[727, "networkx.algorithms.tree.branchings.minimum_branching"]], "minimum_spanning_arborescence() (in module networkx.algorithms.tree.branchings)": [[728, "networkx.algorithms.tree.branchings.minimum_spanning_arborescence"]], "notatree": [[729, "networkx.algorithms.tree.coding.NotATree"]], "from_nested_tuple() (in module networkx.algorithms.tree.coding)": [[730, "networkx.algorithms.tree.coding.from_nested_tuple"]], "from_prufer_sequence() (in module networkx.algorithms.tree.coding)": [[731, "networkx.algorithms.tree.coding.from_prufer_sequence"]], "to_nested_tuple() (in module networkx.algorithms.tree.coding)": [[732, "networkx.algorithms.tree.coding.to_nested_tuple"]], "to_prufer_sequence() (in module networkx.algorithms.tree.coding)": [[733, "networkx.algorithms.tree.coding.to_prufer_sequence"]], "junction_tree() (in module networkx.algorithms.tree.decomposition)": [[734, "networkx.algorithms.tree.decomposition.junction_tree"]], "spanningtreeiterator (class in networkx.algorithms.tree.mst)": [[735, "networkx.algorithms.tree.mst.SpanningTreeIterator"]], "__init__() (spanningtreeiterator method)": [[735, "networkx.algorithms.tree.mst.SpanningTreeIterator.__init__"]], "maximum_spanning_edges() (in module networkx.algorithms.tree.mst)": [[736, "networkx.algorithms.tree.mst.maximum_spanning_edges"]], "maximum_spanning_tree() (in module networkx.algorithms.tree.mst)": [[737, "networkx.algorithms.tree.mst.maximum_spanning_tree"]], "minimum_spanning_edges() (in module networkx.algorithms.tree.mst)": [[738, "networkx.algorithms.tree.mst.minimum_spanning_edges"]], "minimum_spanning_tree() (in module networkx.algorithms.tree.mst)": [[739, "networkx.algorithms.tree.mst.minimum_spanning_tree"]], "random_spanning_tree() (in module networkx.algorithms.tree.mst)": [[740, "networkx.algorithms.tree.mst.random_spanning_tree"]], "join() (in module networkx.algorithms.tree.operations)": [[741, "networkx.algorithms.tree.operations.join"]], "is_arborescence() (in module networkx.algorithms.tree.recognition)": [[742, "networkx.algorithms.tree.recognition.is_arborescence"]], "is_branching() (in module networkx.algorithms.tree.recognition)": [[743, "networkx.algorithms.tree.recognition.is_branching"]], "is_forest() (in module networkx.algorithms.tree.recognition)": [[744, "networkx.algorithms.tree.recognition.is_forest"]], "is_tree() (in module networkx.algorithms.tree.recognition)": [[745, "networkx.algorithms.tree.recognition.is_tree"]], "all_triads() (in module networkx.algorithms.triads)": [[746, "networkx.algorithms.triads.all_triads"]], "all_triplets() (in module networkx.algorithms.triads)": [[747, "networkx.algorithms.triads.all_triplets"]], "is_triad() (in module networkx.algorithms.triads)": [[748, "networkx.algorithms.triads.is_triad"]], "random_triad() (in module networkx.algorithms.triads)": [[749, "networkx.algorithms.triads.random_triad"]], "triad_type() (in module networkx.algorithms.triads)": [[750, "networkx.algorithms.triads.triad_type"]], "triadic_census() (in module networkx.algorithms.triads)": [[751, "networkx.algorithms.triads.triadic_census"]], "triads_by_type() (in module networkx.algorithms.triads)": [[752, "networkx.algorithms.triads.triads_by_type"]], "closeness_vitality() (in module networkx.algorithms.vitality)": [[753, "networkx.algorithms.vitality.closeness_vitality"]], "voronoi_cells() (in module networkx.algorithms.voronoi)": [[754, "networkx.algorithms.voronoi.voronoi_cells"]], "wiener_index() (in module networkx.algorithms.wiener)": [[755, "networkx.algorithms.wiener.wiener_index"]], "networkx.algorithms.graph_hashing": [[756, "module-networkx.algorithms.graph_hashing"]], "networkx.algorithms.graphical": [[757, "module-networkx.algorithms.graphical"]], "networkx.algorithms.hierarchy": [[758, "module-networkx.algorithms.hierarchy"]], "networkx.algorithms.hybrid": [[759, "module-networkx.algorithms.hybrid"]], "networkx.algorithms.isolate": [[761, "module-networkx.algorithms.isolate"]], "networkx.algorithms.isomorphism": [[762, "module-networkx.algorithms.isomorphism"]], "networkx.algorithms.isomorphism.tree_isomorphism": [[762, "module-networkx.algorithms.isomorphism.tree_isomorphism"]], "networkx.algorithms.isomorphism.vf2pp": [[762, "module-networkx.algorithms.isomorphism.vf2pp"]], "networkx.algorithms.isomorphism.ismags": [[763, "module-networkx.algorithms.isomorphism.ismags"]], "networkx.algorithms.isomorphism.isomorphvf2": [[764, "module-networkx.algorithms.isomorphism.isomorphvf2"]], "networkx.algorithms.link_analysis.hits_alg": [[765, "module-networkx.algorithms.link_analysis.hits_alg"]], "networkx.algorithms.link_analysis.pagerank_alg": [[765, "module-networkx.algorithms.link_analysis.pagerank_alg"]], "networkx.algorithms.link_prediction": [[766, "module-networkx.algorithms.link_prediction"]], "networkx.algorithms.lowest_common_ancestors": [[767, "module-networkx.algorithms.lowest_common_ancestors"]], "networkx.algorithms.matching": [[768, "module-networkx.algorithms.matching"]], "networkx.algorithms.minors": [[769, "module-networkx.algorithms.minors"]], "networkx.algorithms.mis": [[770, "module-networkx.algorithms.mis"]], "networkx.algorithms.moral": [[771, "module-networkx.algorithms.moral"]], "networkx.algorithms.node_classification": [[772, "module-networkx.algorithms.node_classification"]], "networkx.algorithms.non_randomness": [[773, "module-networkx.algorithms.non_randomness"]], "networkx.algorithms.operators.all": [[774, "module-networkx.algorithms.operators.all"]], "networkx.algorithms.operators.binary": [[774, "module-networkx.algorithms.operators.binary"]], "networkx.algorithms.operators.product": [[774, "module-networkx.algorithms.operators.product"]], "networkx.algorithms.operators.unary": [[774, "module-networkx.algorithms.operators.unary"]], "networkx.algorithms.planar_drawing": [[775, "module-networkx.algorithms.planar_drawing"]], "networkx.algorithms.planarity": [[776, "module-networkx.algorithms.planarity"]], "networkx.algorithms.polynomials": [[777, "module-networkx.algorithms.polynomials"]], "networkx.algorithms.reciprocity": [[778, "module-networkx.algorithms.reciprocity"]], "networkx.algorithms.regular": [[779, "module-networkx.algorithms.regular"]], "networkx.algorithms.richclub": [[780, "module-networkx.algorithms.richclub"]], "networkx.algorithms.shortest_paths.astar": [[781, "module-networkx.algorithms.shortest_paths.astar"]], "networkx.algorithms.shortest_paths.dense": [[781, "module-networkx.algorithms.shortest_paths.dense"]], "networkx.algorithms.shortest_paths.generic": [[781, "module-networkx.algorithms.shortest_paths.generic"]], "networkx.algorithms.shortest_paths.unweighted": [[781, "module-networkx.algorithms.shortest_paths.unweighted"]], "networkx.algorithms.shortest_paths.weighted": [[781, "module-networkx.algorithms.shortest_paths.weighted"]], "networkx.algorithms.similarity": [[782, "module-networkx.algorithms.similarity"]], "networkx.algorithms.simple_paths": [[783, "module-networkx.algorithms.simple_paths"]], "networkx.algorithms.smallworld": [[784, "module-networkx.algorithms.smallworld"]], "networkx.algorithms.smetric": [[785, "module-networkx.algorithms.smetric"]], "networkx.algorithms.sparsifiers": [[786, "module-networkx.algorithms.sparsifiers"]], "networkx.algorithms.structuralholes": [[787, "module-networkx.algorithms.structuralholes"]], "networkx.algorithms.summarization": [[788, "module-networkx.algorithms.summarization"]], "networkx.algorithms.swap": [[789, "module-networkx.algorithms.swap"]], "networkx.algorithms.threshold": [[790, "module-networkx.algorithms.threshold"]], "networkx.algorithms.tournament": [[791, "module-networkx.algorithms.tournament"]], "networkx.algorithms.traversal.beamsearch": [[792, "module-networkx.algorithms.traversal.beamsearch"]], "networkx.algorithms.traversal.breadth_first_search": [[792, "module-networkx.algorithms.traversal.breadth_first_search"]], "networkx.algorithms.traversal.depth_first_search": [[792, "module-networkx.algorithms.traversal.depth_first_search"]], "networkx.algorithms.traversal.edgebfs": [[792, "module-networkx.algorithms.traversal.edgebfs"]], "networkx.algorithms.traversal.edgedfs": [[792, "module-networkx.algorithms.traversal.edgedfs"]], "networkx.algorithms.tree.branchings": [[793, "module-networkx.algorithms.tree.branchings"]], "networkx.algorithms.tree.coding": [[793, "module-networkx.algorithms.tree.coding"]], "networkx.algorithms.tree.decomposition": [[793, "module-networkx.algorithms.tree.decomposition"]], "networkx.algorithms.tree.mst": [[793, "module-networkx.algorithms.tree.mst"]], "networkx.algorithms.tree.operations": [[793, "module-networkx.algorithms.tree.operations"]], "networkx.algorithms.tree.recognition": [[793, "module-networkx.algorithms.tree.recognition"]], "networkx.algorithms.triads": [[794, "module-networkx.algorithms.triads"]], "networkx.algorithms.vitality": [[795, "module-networkx.algorithms.vitality"]], "networkx.algorithms.voronoi": [[796, "module-networkx.algorithms.voronoi"]], "networkx.algorithms.wiener": [[797, "module-networkx.algorithms.wiener"]], "digraph (class in networkx)": [[798, "networkx.DiGraph"]], "copy() (adjacencyview method)": [[799, "networkx.classes.coreviews.AdjacencyView.copy"]], "get() (adjacencyview method)": [[800, "networkx.classes.coreviews.AdjacencyView.get"]], "items() (adjacencyview method)": [[801, "networkx.classes.coreviews.AdjacencyView.items"]], "keys() (adjacencyview method)": [[802, "networkx.classes.coreviews.AdjacencyView.keys"]], "values() (adjacencyview method)": [[803, "networkx.classes.coreviews.AdjacencyView.values"]], "copy() (atlasview method)": [[804, "networkx.classes.coreviews.AtlasView.copy"]], "get() (atlasview method)": [[805, 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"networkx.NetworkXUnfeasible"]], "nodenotfound (class in networkx)": [[1046, "networkx.NodeNotFound"]], "poweriterationfailedconvergence (class in networkx)": [[1046, "networkx.PowerIterationFailedConvergence"]], "networkx.exception": [[1046, "module-networkx.exception"]], "networkx.classes.function": [[1047, "module-networkx.classes.function"]], "assemble() (argmap method)": [[1048, "networkx.utils.decorators.argmap.assemble"]], "compile() (argmap method)": [[1049, "networkx.utils.decorators.argmap.compile"]], "signature() (argmap class method)": [[1050, "networkx.utils.decorators.argmap.signature"]], "pop() (mappedqueue method)": [[1051, "networkx.utils.mapped_queue.MappedQueue.pop"]], "push() (mappedqueue method)": [[1052, "networkx.utils.mapped_queue.MappedQueue.push"]], "remove() (mappedqueue method)": [[1053, "networkx.utils.mapped_queue.MappedQueue.remove"]], "update() (mappedqueue method)": [[1054, "networkx.utils.mapped_queue.MappedQueue.update"]], "add_cycle() (in module networkx.classes.function)": [[1055, "networkx.classes.function.add_cycle"]], "add_path() (in module networkx.classes.function)": [[1056, "networkx.classes.function.add_path"]], "add_star() (in module networkx.classes.function)": [[1057, "networkx.classes.function.add_star"]], "all_neighbors() (in module networkx.classes.function)": [[1058, "networkx.classes.function.all_neighbors"]], "common_neighbors() (in module networkx.classes.function)": [[1059, "networkx.classes.function.common_neighbors"]], "create_empty_copy() (in module networkx.classes.function)": [[1060, "networkx.classes.function.create_empty_copy"]], "degree() (in module networkx.classes.function)": [[1061, "networkx.classes.function.degree"]], "degree_histogram() (in module networkx.classes.function)": [[1062, "networkx.classes.function.degree_histogram"]], "density() (in module networkx.classes.function)": [[1063, "networkx.classes.function.density"]], "edge_subgraph() (in module networkx.classes.function)": [[1064, "networkx.classes.function.edge_subgraph"]], "edges() (in module networkx.classes.function)": [[1065, "networkx.classes.function.edges"]], "freeze() (in module networkx.classes.function)": [[1066, "networkx.classes.function.freeze"]], "get_edge_attributes() (in module networkx.classes.function)": [[1067, "networkx.classes.function.get_edge_attributes"]], "get_node_attributes() (in module networkx.classes.function)": [[1068, "networkx.classes.function.get_node_attributes"]], "induced_subgraph() (in module networkx.classes.function)": [[1069, "networkx.classes.function.induced_subgraph"]], "is_directed() (in module networkx.classes.function)": [[1070, "networkx.classes.function.is_directed"]], "is_empty() (in module networkx.classes.function)": [[1071, "networkx.classes.function.is_empty"]], "is_frozen() (in module networkx.classes.function)": [[1072, "networkx.classes.function.is_frozen"]], "is_negatively_weighted() (in module networkx.classes.function)": [[1073, "networkx.classes.function.is_negatively_weighted"]], "is_path() (in module networkx.classes.function)": [[1074, "networkx.classes.function.is_path"]], "is_weighted() (in module networkx.classes.function)": [[1075, "networkx.classes.function.is_weighted"]], "neighbors() (in module networkx.classes.function)": [[1076, "networkx.classes.function.neighbors"]], "nodes() (in module networkx.classes.function)": [[1077, "networkx.classes.function.nodes"]], "nodes_with_selfloops() (in module networkx.classes.function)": [[1078, "networkx.classes.function.nodes_with_selfloops"]], "non_edges() (in module networkx.classes.function)": [[1079, "networkx.classes.function.non_edges"]], "non_neighbors() (in module networkx.classes.function)": [[1080, "networkx.classes.function.non_neighbors"]], "number_of_edges() (in module networkx.classes.function)": [[1081, "networkx.classes.function.number_of_edges"]], "number_of_nodes() (in module networkx.classes.function)": [[1082, "networkx.classes.function.number_of_nodes"]], "number_of_selfloops() (in module networkx.classes.function)": [[1083, "networkx.classes.function.number_of_selfloops"]], "path_weight() (in module networkx.classes.function)": [[1084, "networkx.classes.function.path_weight"]], "restricted_view() (in module networkx.classes.function)": [[1085, "networkx.classes.function.restricted_view"]], "reverse_view() (in module networkx.classes.function)": [[1086, "networkx.classes.function.reverse_view"]], "selfloop_edges() (in module networkx.classes.function)": [[1087, "networkx.classes.function.selfloop_edges"]], "set_edge_attributes() (in module networkx.classes.function)": [[1088, "networkx.classes.function.set_edge_attributes"]], "set_node_attributes() (in module networkx.classes.function)": [[1089, "networkx.classes.function.set_node_attributes"]], "subgraph() (in module networkx.classes.function)": [[1090, "networkx.classes.function.subgraph"]], "subgraph_view() (in module networkx.classes.function)": [[1091, "networkx.classes.function.subgraph_view"]], "to_directed() (in module networkx.classes.function)": [[1092, "networkx.classes.function.to_directed"]], "to_undirected() (in module networkx.classes.function)": [[1093, "networkx.classes.function.to_undirected"]], "from_dict_of_dicts() (in module networkx.convert)": [[1094, "networkx.convert.from_dict_of_dicts"]], "from_dict_of_lists() (in module networkx.convert)": [[1095, "networkx.convert.from_dict_of_lists"]], "from_edgelist() (in module networkx.convert)": [[1096, "networkx.convert.from_edgelist"]], "to_dict_of_dicts() (in module networkx.convert)": [[1097, "networkx.convert.to_dict_of_dicts"]], "to_dict_of_lists() (in module networkx.convert)": [[1098, "networkx.convert.to_dict_of_lists"]], "to_edgelist() (in module networkx.convert)": [[1099, "networkx.convert.to_edgelist"]], "to_networkx_graph() (in module networkx.convert)": [[1100, "networkx.convert.to_networkx_graph"]], "from_numpy_array() (in module networkx.convert_matrix)": [[1101, "networkx.convert_matrix.from_numpy_array"]], "from_pandas_adjacency() (in module networkx.convert_matrix)": [[1102, "networkx.convert_matrix.from_pandas_adjacency"]], "from_pandas_edgelist() (in module networkx.convert_matrix)": [[1103, "networkx.convert_matrix.from_pandas_edgelist"]], "from_scipy_sparse_array() (in module networkx.convert_matrix)": [[1104, "networkx.convert_matrix.from_scipy_sparse_array"]], "to_numpy_array() (in module networkx.convert_matrix)": [[1105, "networkx.convert_matrix.to_numpy_array"]], "to_pandas_adjacency() (in module networkx.convert_matrix)": [[1106, "networkx.convert_matrix.to_pandas_adjacency"]], "to_pandas_edgelist() (in module networkx.convert_matrix)": [[1107, "networkx.convert_matrix.to_pandas_edgelist"]], "to_scipy_sparse_array() (in module networkx.convert_matrix)": [[1108, "networkx.convert_matrix.to_scipy_sparse_array"]], "bipartite_layout() (in module networkx.drawing.layout)": [[1109, 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module networkx.drawing.nx_pylab)": [[1137, "networkx.drawing.nx_pylab.draw_circular"]], "draw_kamada_kawai() (in module networkx.drawing.nx_pylab)": [[1138, "networkx.drawing.nx_pylab.draw_kamada_kawai"]], "draw_networkx() (in module networkx.drawing.nx_pylab)": [[1139, "networkx.drawing.nx_pylab.draw_networkx"]], "draw_networkx_edge_labels() (in module networkx.drawing.nx_pylab)": [[1140, "networkx.drawing.nx_pylab.draw_networkx_edge_labels"]], "draw_networkx_edges() (in module networkx.drawing.nx_pylab)": [[1141, "networkx.drawing.nx_pylab.draw_networkx_edges"]], "draw_networkx_labels() (in module networkx.drawing.nx_pylab)": [[1142, "networkx.drawing.nx_pylab.draw_networkx_labels"]], "draw_networkx_nodes() (in module networkx.drawing.nx_pylab)": [[1143, "networkx.drawing.nx_pylab.draw_networkx_nodes"]], "draw_planar() (in module networkx.drawing.nx_pylab)": [[1144, "networkx.drawing.nx_pylab.draw_planar"]], "draw_random() (in module networkx.drawing.nx_pylab)": [[1145, 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networkx.generators.community)": [[1171, "networkx.generators.community.LFR_benchmark_graph"]], "caveman_graph() (in module networkx.generators.community)": [[1172, "networkx.generators.community.caveman_graph"]], "connected_caveman_graph() (in module networkx.generators.community)": [[1173, "networkx.generators.community.connected_caveman_graph"]], "gaussian_random_partition_graph() (in module networkx.generators.community)": [[1174, "networkx.generators.community.gaussian_random_partition_graph"]], "planted_partition_graph() (in module networkx.generators.community)": [[1175, "networkx.generators.community.planted_partition_graph"]], "random_partition_graph() (in module networkx.generators.community)": [[1176, "networkx.generators.community.random_partition_graph"]], "relaxed_caveman_graph() (in module networkx.generators.community)": [[1177, "networkx.generators.community.relaxed_caveman_graph"]], "ring_of_cliques() (in module networkx.generators.community)": [[1178, 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networkx.generators.internet_as_graphs)": [[1208, "networkx.generators.internet_as_graphs.random_internet_as_graph"]], "general_random_intersection_graph() (in module networkx.generators.intersection)": [[1209, "networkx.generators.intersection.general_random_intersection_graph"]], "k_random_intersection_graph() (in module networkx.generators.intersection)": [[1210, "networkx.generators.intersection.k_random_intersection_graph"]], "uniform_random_intersection_graph() (in module networkx.generators.intersection)": [[1211, "networkx.generators.intersection.uniform_random_intersection_graph"]], "interval_graph() (in module networkx.generators.interval_graph)": [[1212, "networkx.generators.interval_graph.interval_graph"]], "directed_joint_degree_graph() (in module networkx.generators.joint_degree_seq)": [[1213, "networkx.generators.joint_degree_seq.directed_joint_degree_graph"]], "is_valid_directed_joint_degree() (in module networkx.generators.joint_degree_seq)": [[1214, "networkx.generators.joint_degree_seq.is_valid_directed_joint_degree"]], "is_valid_joint_degree() (in module networkx.generators.joint_degree_seq)": [[1215, "networkx.generators.joint_degree_seq.is_valid_joint_degree"]], "joint_degree_graph() (in module networkx.generators.joint_degree_seq)": [[1216, "networkx.generators.joint_degree_seq.joint_degree_graph"]], "grid_2d_graph() (in module networkx.generators.lattice)": [[1217, "networkx.generators.lattice.grid_2d_graph"]], "grid_graph() (in module networkx.generators.lattice)": [[1218, "networkx.generators.lattice.grid_graph"]], "hexagonal_lattice_graph() (in module networkx.generators.lattice)": [[1219, "networkx.generators.lattice.hexagonal_lattice_graph"]], "hypercube_graph() (in module networkx.generators.lattice)": [[1220, "networkx.generators.lattice.hypercube_graph"]], "triangular_lattice_graph() (in module networkx.generators.lattice)": [[1221, "networkx.generators.lattice.triangular_lattice_graph"]], "inverse_line_graph() (in module networkx.generators.line)": [[1222, "networkx.generators.line.inverse_line_graph"]], "line_graph() (in module networkx.generators.line)": [[1223, "networkx.generators.line.line_graph"]], "mycielski_graph() (in module networkx.generators.mycielski)": [[1224, "networkx.generators.mycielski.mycielski_graph"]], "mycielskian() (in module networkx.generators.mycielski)": [[1225, "networkx.generators.mycielski.mycielskian"]], "nonisomorphic_trees() (in module networkx.generators.nonisomorphic_trees)": [[1226, "networkx.generators.nonisomorphic_trees.nonisomorphic_trees"]], "number_of_nonisomorphic_trees() (in module networkx.generators.nonisomorphic_trees)": [[1227, "networkx.generators.nonisomorphic_trees.number_of_nonisomorphic_trees"]], "random_clustered_graph() (in module networkx.generators.random_clustered)": [[1228, "networkx.generators.random_clustered.random_clustered_graph"]], "barabasi_albert_graph() (in module networkx.generators.random_graphs)": [[1229, "networkx.generators.random_graphs.barabasi_albert_graph"]], "binomial_graph() (in module networkx.generators.random_graphs)": [[1230, "networkx.generators.random_graphs.binomial_graph"]], "connected_watts_strogatz_graph() (in module networkx.generators.random_graphs)": [[1231, "networkx.generators.random_graphs.connected_watts_strogatz_graph"]], "dense_gnm_random_graph() (in module networkx.generators.random_graphs)": [[1232, "networkx.generators.random_graphs.dense_gnm_random_graph"]], "dual_barabasi_albert_graph() (in module networkx.generators.random_graphs)": [[1233, "networkx.generators.random_graphs.dual_barabasi_albert_graph"]], "erdos_renyi_graph() (in module networkx.generators.random_graphs)": [[1234, "networkx.generators.random_graphs.erdos_renyi_graph"]], "extended_barabasi_albert_graph() (in module networkx.generators.random_graphs)": [[1235, "networkx.generators.random_graphs.extended_barabasi_albert_graph"]], "fast_gnp_random_graph() (in module networkx.generators.random_graphs)": [[1236, "networkx.generators.random_graphs.fast_gnp_random_graph"]], "gnm_random_graph() (in module networkx.generators.random_graphs)": [[1237, "networkx.generators.random_graphs.gnm_random_graph"]], "gnp_random_graph() (in module networkx.generators.random_graphs)": [[1238, "networkx.generators.random_graphs.gnp_random_graph"]], "newman_watts_strogatz_graph() (in module networkx.generators.random_graphs)": [[1239, "networkx.generators.random_graphs.newman_watts_strogatz_graph"]], "powerlaw_cluster_graph() (in module networkx.generators.random_graphs)": [[1240, "networkx.generators.random_graphs.powerlaw_cluster_graph"]], "random_kernel_graph() (in module networkx.generators.random_graphs)": [[1241, "networkx.generators.random_graphs.random_kernel_graph"]], "random_lobster() (in module networkx.generators.random_graphs)": [[1242, "networkx.generators.random_graphs.random_lobster"]], "random_powerlaw_tree() (in module networkx.generators.random_graphs)": [[1243, "networkx.generators.random_graphs.random_powerlaw_tree"]], "random_powerlaw_tree_sequence() (in module networkx.generators.random_graphs)": [[1244, "networkx.generators.random_graphs.random_powerlaw_tree_sequence"]], "random_regular_graph() (in module networkx.generators.random_graphs)": [[1245, "networkx.generators.random_graphs.random_regular_graph"]], "random_shell_graph() (in module networkx.generators.random_graphs)": [[1246, "networkx.generators.random_graphs.random_shell_graph"]], "watts_strogatz_graph() (in module networkx.generators.random_graphs)": [[1247, "networkx.generators.random_graphs.watts_strogatz_graph"]], "lcf_graph() (in module networkx.generators.small)": [[1248, "networkx.generators.small.LCF_graph"]], "bull_graph() (in module networkx.generators.small)": [[1249, "networkx.generators.small.bull_graph"]], "chvatal_graph() (in module networkx.generators.small)": [[1250, "networkx.generators.small.chvatal_graph"]], "cubical_graph() (in module networkx.generators.small)": [[1251, "networkx.generators.small.cubical_graph"]], "desargues_graph() (in module networkx.generators.small)": [[1252, "networkx.generators.small.desargues_graph"]], "diamond_graph() (in module networkx.generators.small)": [[1253, "networkx.generators.small.diamond_graph"]], "dodecahedral_graph() (in module networkx.generators.small)": [[1254, "networkx.generators.small.dodecahedral_graph"]], "frucht_graph() (in module networkx.generators.small)": [[1255, "networkx.generators.small.frucht_graph"]], "heawood_graph() (in module networkx.generators.small)": [[1256, "networkx.generators.small.heawood_graph"]], "hoffman_singleton_graph() (in module networkx.generators.small)": [[1257, "networkx.generators.small.hoffman_singleton_graph"]], "house_graph() (in module networkx.generators.small)": [[1258, "networkx.generators.small.house_graph"]], "house_x_graph() (in module networkx.generators.small)": [[1259, "networkx.generators.small.house_x_graph"]], "icosahedral_graph() (in module networkx.generators.small)": [[1260, "networkx.generators.small.icosahedral_graph"]], "krackhardt_kite_graph() (in module networkx.generators.small)": [[1261, "networkx.generators.small.krackhardt_kite_graph"]], "moebius_kantor_graph() (in module networkx.generators.small)": [[1262, "networkx.generators.small.moebius_kantor_graph"]], "octahedral_graph() (in module networkx.generators.small)": [[1263, "networkx.generators.small.octahedral_graph"]], "pappus_graph() (in module networkx.generators.small)": [[1264, "networkx.generators.small.pappus_graph"]], "petersen_graph() (in module networkx.generators.small)": [[1265, "networkx.generators.small.petersen_graph"]], "sedgewick_maze_graph() (in module networkx.generators.small)": [[1266, "networkx.generators.small.sedgewick_maze_graph"]], "tetrahedral_graph() (in module networkx.generators.small)": [[1267, "networkx.generators.small.tetrahedral_graph"]], "truncated_cube_graph() (in module networkx.generators.small)": [[1268, "networkx.generators.small.truncated_cube_graph"]], "truncated_tetrahedron_graph() (in module networkx.generators.small)": [[1269, "networkx.generators.small.truncated_tetrahedron_graph"]], "tutte_graph() (in module networkx.generators.small)": [[1270, "networkx.generators.small.tutte_graph"]], "davis_southern_women_graph() (in module networkx.generators.social)": [[1271, "networkx.generators.social.davis_southern_women_graph"]], "florentine_families_graph() (in module networkx.generators.social)": [[1272, "networkx.generators.social.florentine_families_graph"]], "karate_club_graph() (in module networkx.generators.social)": [[1273, "networkx.generators.social.karate_club_graph"]], "les_miserables_graph() (in module networkx.generators.social)": [[1274, "networkx.generators.social.les_miserables_graph"]], "spectral_graph_forge() (in module networkx.generators.spectral_graph_forge)": [[1275, "networkx.generators.spectral_graph_forge.spectral_graph_forge"]], "stochastic_graph() (in module networkx.generators.stochastic)": [[1276, "networkx.generators.stochastic.stochastic_graph"]], "sudoku_graph() (in module networkx.generators.sudoku)": [[1277, "networkx.generators.sudoku.sudoku_graph"]], "prefix_tree() (in module networkx.generators.trees)": [[1278, "networkx.generators.trees.prefix_tree"]], "random_tree() (in module networkx.generators.trees)": [[1279, "networkx.generators.trees.random_tree"]], "triad_graph() (in module networkx.generators.triads)": [[1280, "networkx.generators.triads.triad_graph"]], "algebraic_connectivity() (in module networkx.linalg.algebraicconnectivity)": [[1281, "networkx.linalg.algebraicconnectivity.algebraic_connectivity"]], "fiedler_vector() (in module networkx.linalg.algebraicconnectivity)": [[1282, "networkx.linalg.algebraicconnectivity.fiedler_vector"]], "spectral_ordering() (in module networkx.linalg.algebraicconnectivity)": [[1283, "networkx.linalg.algebraicconnectivity.spectral_ordering"]], "attr_matrix() (in module networkx.linalg.attrmatrix)": [[1284, "networkx.linalg.attrmatrix.attr_matrix"]], "attr_sparse_matrix() (in module networkx.linalg.attrmatrix)": [[1285, "networkx.linalg.attrmatrix.attr_sparse_matrix"]], "bethe_hessian_matrix() (in module networkx.linalg.bethehessianmatrix)": [[1286, "networkx.linalg.bethehessianmatrix.bethe_hessian_matrix"]], "adjacency_matrix() (in module networkx.linalg.graphmatrix)": [[1287, "networkx.linalg.graphmatrix.adjacency_matrix"]], "incidence_matrix() (in module networkx.linalg.graphmatrix)": [[1288, "networkx.linalg.graphmatrix.incidence_matrix"]], "directed_combinatorial_laplacian_matrix() (in module networkx.linalg.laplacianmatrix)": [[1289, "networkx.linalg.laplacianmatrix.directed_combinatorial_laplacian_matrix"]], "directed_laplacian_matrix() (in module networkx.linalg.laplacianmatrix)": [[1290, "networkx.linalg.laplacianmatrix.directed_laplacian_matrix"]], "laplacian_matrix() (in module networkx.linalg.laplacianmatrix)": [[1291, "networkx.linalg.laplacianmatrix.laplacian_matrix"]], "normalized_laplacian_matrix() (in module networkx.linalg.laplacianmatrix)": [[1292, "networkx.linalg.laplacianmatrix.normalized_laplacian_matrix"]], "directed_modularity_matrix() (in module networkx.linalg.modularitymatrix)": [[1293, "networkx.linalg.modularitymatrix.directed_modularity_matrix"]], "modularity_matrix() (in module networkx.linalg.modularitymatrix)": [[1294, "networkx.linalg.modularitymatrix.modularity_matrix"]], "adjacency_spectrum() (in module networkx.linalg.spectrum)": [[1295, "networkx.linalg.spectrum.adjacency_spectrum"]], "bethe_hessian_spectrum() (in module networkx.linalg.spectrum)": [[1296, "networkx.linalg.spectrum.bethe_hessian_spectrum"]], "laplacian_spectrum() (in module networkx.linalg.spectrum)": [[1297, "networkx.linalg.spectrum.laplacian_spectrum"]], "modularity_spectrum() (in module networkx.linalg.spectrum)": [[1298, "networkx.linalg.spectrum.modularity_spectrum"]], "normalized_laplacian_spectrum() (in module networkx.linalg.spectrum)": [[1299, "networkx.linalg.spectrum.normalized_laplacian_spectrum"]], "convert_node_labels_to_integers() (in module networkx.relabel)": [[1300, "networkx.relabel.convert_node_labels_to_integers"]], "relabel_nodes() (in module networkx.relabel)": [[1301, "networkx.relabel.relabel_nodes"]], "__init__() (argmap method)": [[1302, "networkx.utils.decorators.argmap.__init__"]], "argmap (class in networkx.utils.decorators)": [[1302, "networkx.utils.decorators.argmap"]], "nodes_or_number() (in module networkx.utils.decorators)": [[1303, "networkx.utils.decorators.nodes_or_number"]], "not_implemented_for() (in module networkx.utils.decorators)": [[1304, "networkx.utils.decorators.not_implemented_for"]], "np_random_state() (in module networkx.utils.decorators)": [[1305, "networkx.utils.decorators.np_random_state"]], "open_file() (in module networkx.utils.decorators)": [[1306, "networkx.utils.decorators.open_file"]], "py_random_state() (in module networkx.utils.decorators)": [[1307, "networkx.utils.decorators.py_random_state"]], "mappedqueue (class in networkx.utils.mapped_queue)": [[1308, "networkx.utils.mapped_queue.MappedQueue"]], "__init__() (mappedqueue method)": [[1308, "networkx.utils.mapped_queue.MappedQueue.__init__"]], "arbitrary_element() (in module networkx.utils.misc)": [[1309, "networkx.utils.misc.arbitrary_element"]], "create_py_random_state() (in module networkx.utils.misc)": [[1310, "networkx.utils.misc.create_py_random_state"]], "create_random_state() (in module networkx.utils.misc)": [[1311, "networkx.utils.misc.create_random_state"]], "dict_to_numpy_array() (in module networkx.utils.misc)": [[1312, "networkx.utils.misc.dict_to_numpy_array"]], "edges_equal() (in module networkx.utils.misc)": [[1313, "networkx.utils.misc.edges_equal"]], "flatten() (in module networkx.utils.misc)": [[1314, "networkx.utils.misc.flatten"]], "graphs_equal() (in module networkx.utils.misc)": [[1315, "networkx.utils.misc.graphs_equal"]], "groups() (in module networkx.utils.misc)": [[1316, "networkx.utils.misc.groups"]], "make_list_of_ints() (in module networkx.utils.misc)": [[1317, "networkx.utils.misc.make_list_of_ints"]], "nodes_equal() (in module networkx.utils.misc)": [[1318, "networkx.utils.misc.nodes_equal"]], "pairwise() (in module networkx.utils.misc)": [[1319, "networkx.utils.misc.pairwise"]], "cumulative_distribution() (in module networkx.utils.random_sequence)": [[1320, "networkx.utils.random_sequence.cumulative_distribution"]], "discrete_sequence() (in module networkx.utils.random_sequence)": [[1321, "networkx.utils.random_sequence.discrete_sequence"]], "powerlaw_sequence() (in module networkx.utils.random_sequence)": [[1322, "networkx.utils.random_sequence.powerlaw_sequence"]], "random_weighted_sample() (in module networkx.utils.random_sequence)": [[1323, "networkx.utils.random_sequence.random_weighted_sample"]], "weighted_choice() (in module networkx.utils.random_sequence)": [[1324, "networkx.utils.random_sequence.weighted_choice"]], "zipf_rv() (in module networkx.utils.random_sequence)": [[1325, "networkx.utils.random_sequence.zipf_rv"]], "cuthill_mckee_ordering() (in module networkx.utils.rcm)": [[1326, "networkx.utils.rcm.cuthill_mckee_ordering"]], "reverse_cuthill_mckee_ordering() (in module networkx.utils.rcm)": [[1327, "networkx.utils.rcm.reverse_cuthill_mckee_ordering"]], "union() (unionfind method)": [[1328, "networkx.utils.union_find.UnionFind.union"]], "networkx.generators.atlas": [[1329, "module-networkx.generators.atlas"]], "networkx.generators.classic": [[1329, "module-networkx.generators.classic"]], "networkx.generators.cographs": [[1329, "module-networkx.generators.cographs"]], "networkx.generators.community": [[1329, "module-networkx.generators.community"]], "networkx.generators.degree_seq": [[1329, "module-networkx.generators.degree_seq"]], "networkx.generators.directed": [[1329, "module-networkx.generators.directed"]], "networkx.generators.duplication": [[1329, "module-networkx.generators.duplication"]], "networkx.generators.ego": [[1329, "module-networkx.generators.ego"]], "networkx.generators.expanders": [[1329, "module-networkx.generators.expanders"]], "networkx.generators.geometric": [[1329, "module-networkx.generators.geometric"]], "networkx.generators.harary_graph": [[1329, "module-networkx.generators.harary_graph"]], "networkx.generators.internet_as_graphs": [[1329, "module-networkx.generators.internet_as_graphs"]], "networkx.generators.intersection": [[1329, "module-networkx.generators.intersection"]], "networkx.generators.interval_graph": [[1329, "module-networkx.generators.interval_graph"]], "networkx.generators.joint_degree_seq": [[1329, "module-networkx.generators.joint_degree_seq"]], "networkx.generators.lattice": [[1329, "module-networkx.generators.lattice"]], "networkx.generators.line": [[1329, "module-networkx.generators.line"]], "networkx.generators.mycielski": [[1329, "module-networkx.generators.mycielski"]], "networkx.generators.nonisomorphic_trees": [[1329, "module-networkx.generators.nonisomorphic_trees"]], "networkx.generators.random_clustered": [[1329, "module-networkx.generators.random_clustered"]], "networkx.generators.random_graphs": [[1329, "module-networkx.generators.random_graphs"]], "networkx.generators.small": [[1329, "module-networkx.generators.small"]], "networkx.generators.social": [[1329, "module-networkx.generators.social"]], "networkx.generators.spectral_graph_forge": [[1329, "module-networkx.generators.spectral_graph_forge"]], "networkx.generators.stochastic": [[1329, "module-networkx.generators.stochastic"]], "networkx.generators.sudoku": [[1329, "module-networkx.generators.sudoku"]], "networkx.generators.trees": [[1329, "module-networkx.generators.trees"]], "networkx.generators.triads": [[1329, "module-networkx.generators.triads"]], "dictionary": [[1330, "term-dictionary"]], "ebunch": [[1330, "term-ebunch"]], "edge": [[1330, "term-edge"]], "edge attribute": [[1330, "term-edge-attribute"]], "nbunch": [[1330, "term-nbunch"]], "node": [[1330, "term-node"]], "node attribute": [[1330, "term-node-attribute"]], "networkx.linalg.algebraicconnectivity": [[1333, "module-networkx.linalg.algebraicconnectivity"]], "networkx.linalg.attrmatrix": [[1333, "module-networkx.linalg.attrmatrix"]], "networkx.linalg.bethehessianmatrix": [[1333, "module-networkx.linalg.bethehessianmatrix"]], "networkx.linalg.graphmatrix": [[1333, "module-networkx.linalg.graphmatrix"]], "networkx.linalg.laplacianmatrix": [[1333, "module-networkx.linalg.laplacianmatrix"]], "networkx.linalg.modularitymatrix": [[1333, "module-networkx.linalg.modularitymatrix"]], "networkx.linalg.spectrum": [[1333, "module-networkx.linalg.spectrum"]], "networkx.readwrite.adjlist": [[1335, "module-networkx.readwrite.adjlist"]], "networkx.readwrite.edgelist": [[1336, "module-networkx.readwrite.edgelist"]], "generate_adjlist() (in module networkx.readwrite.adjlist)": [[1337, "networkx.readwrite.adjlist.generate_adjlist"]], "parse_adjlist() (in module networkx.readwrite.adjlist)": [[1338, "networkx.readwrite.adjlist.parse_adjlist"]], "read_adjlist() (in module networkx.readwrite.adjlist)": [[1339, "networkx.readwrite.adjlist.read_adjlist"]], "write_adjlist() (in module networkx.readwrite.adjlist)": [[1340, "networkx.readwrite.adjlist.write_adjlist"]], "generate_edgelist() (in module networkx.readwrite.edgelist)": [[1341, "networkx.readwrite.edgelist.generate_edgelist"]], "parse_edgelist() (in module networkx.readwrite.edgelist)": [[1342, "networkx.readwrite.edgelist.parse_edgelist"]], "read_edgelist() (in module networkx.readwrite.edgelist)": [[1343, "networkx.readwrite.edgelist.read_edgelist"]], "read_weighted_edgelist() (in module networkx.readwrite.edgelist)": [[1344, "networkx.readwrite.edgelist.read_weighted_edgelist"]], "write_edgelist() (in module networkx.readwrite.edgelist)": [[1345, "networkx.readwrite.edgelist.write_edgelist"]], "write_weighted_edgelist() (in module networkx.readwrite.edgelist)": [[1346, "networkx.readwrite.edgelist.write_weighted_edgelist"]], "generate_gexf() (in module networkx.readwrite.gexf)": [[1347, "networkx.readwrite.gexf.generate_gexf"]], "read_gexf() (in module networkx.readwrite.gexf)": [[1348, "networkx.readwrite.gexf.read_gexf"]], "relabel_gexf_graph() (in module networkx.readwrite.gexf)": [[1349, "networkx.readwrite.gexf.relabel_gexf_graph"]], "write_gexf() (in module networkx.readwrite.gexf)": [[1350, "networkx.readwrite.gexf.write_gexf"]], "generate_gml() (in module networkx.readwrite.gml)": [[1351, "networkx.readwrite.gml.generate_gml"]], "literal_destringizer() (in module networkx.readwrite.gml)": [[1352, "networkx.readwrite.gml.literal_destringizer"]], "literal_stringizer() (in module networkx.readwrite.gml)": [[1353, "networkx.readwrite.gml.literal_stringizer"]], "parse_gml() (in module networkx.readwrite.gml)": [[1354, "networkx.readwrite.gml.parse_gml"]], "read_gml() (in module networkx.readwrite.gml)": [[1355, "networkx.readwrite.gml.read_gml"]], "write_gml() (in module networkx.readwrite.gml)": [[1356, "networkx.readwrite.gml.write_gml"]], "from_graph6_bytes() (in module networkx.readwrite.graph6)": [[1357, "networkx.readwrite.graph6.from_graph6_bytes"]], "read_graph6() (in module networkx.readwrite.graph6)": [[1358, "networkx.readwrite.graph6.read_graph6"]], "to_graph6_bytes() (in module networkx.readwrite.graph6)": [[1359, "networkx.readwrite.graph6.to_graph6_bytes"]], "write_graph6() (in module networkx.readwrite.graph6)": [[1360, "networkx.readwrite.graph6.write_graph6"]], "generate_graphml() (in module networkx.readwrite.graphml)": [[1361, "networkx.readwrite.graphml.generate_graphml"]], "parse_graphml() (in module networkx.readwrite.graphml)": [[1362, "networkx.readwrite.graphml.parse_graphml"]], "read_graphml() (in module networkx.readwrite.graphml)": [[1363, "networkx.readwrite.graphml.read_graphml"]], "write_graphml() (in module networkx.readwrite.graphml)": [[1364, "networkx.readwrite.graphml.write_graphml"]], "adjacency_data() (in module networkx.readwrite.json_graph)": [[1365, "networkx.readwrite.json_graph.adjacency_data"]], "adjacency_graph() (in module networkx.readwrite.json_graph)": [[1366, "networkx.readwrite.json_graph.adjacency_graph"]], "cytoscape_data() (in module networkx.readwrite.json_graph)": [[1367, "networkx.readwrite.json_graph.cytoscape_data"]], "cytoscape_graph() (in module networkx.readwrite.json_graph)": [[1368, "networkx.readwrite.json_graph.cytoscape_graph"]], "node_link_data() (in module networkx.readwrite.json_graph)": [[1369, "networkx.readwrite.json_graph.node_link_data"]], "node_link_graph() (in module networkx.readwrite.json_graph)": [[1370, "networkx.readwrite.json_graph.node_link_graph"]], "tree_data() (in module networkx.readwrite.json_graph)": [[1371, "networkx.readwrite.json_graph.tree_data"]], "tree_graph() (in module networkx.readwrite.json_graph)": [[1372, "networkx.readwrite.json_graph.tree_graph"]], "parse_leda() (in module networkx.readwrite.leda)": [[1373, "networkx.readwrite.leda.parse_leda"]], "read_leda() (in module networkx.readwrite.leda)": [[1374, "networkx.readwrite.leda.read_leda"]], "generate_multiline_adjlist() (in module networkx.readwrite.multiline_adjlist)": [[1375, "networkx.readwrite.multiline_adjlist.generate_multiline_adjlist"]], "parse_multiline_adjlist() (in module networkx.readwrite.multiline_adjlist)": [[1376, "networkx.readwrite.multiline_adjlist.parse_multiline_adjlist"]], "read_multiline_adjlist() (in module networkx.readwrite.multiline_adjlist)": [[1377, "networkx.readwrite.multiline_adjlist.read_multiline_adjlist"]], "write_multiline_adjlist() (in module networkx.readwrite.multiline_adjlist)": [[1378, "networkx.readwrite.multiline_adjlist.write_multiline_adjlist"]], "generate_pajek() (in module networkx.readwrite.pajek)": [[1379, "networkx.readwrite.pajek.generate_pajek"]], "parse_pajek() (in module networkx.readwrite.pajek)": [[1380, "networkx.readwrite.pajek.parse_pajek"]], "read_pajek() (in module networkx.readwrite.pajek)": [[1381, "networkx.readwrite.pajek.read_pajek"]], "write_pajek() (in module networkx.readwrite.pajek)": [[1382, "networkx.readwrite.pajek.write_pajek"]], "from_sparse6_bytes() (in module networkx.readwrite.sparse6)": [[1383, "networkx.readwrite.sparse6.from_sparse6_bytes"]], "read_sparse6() (in module networkx.readwrite.sparse6)": [[1384, "networkx.readwrite.sparse6.read_sparse6"]], "to_sparse6_bytes() (in module networkx.readwrite.sparse6)": [[1385, "networkx.readwrite.sparse6.to_sparse6_bytes"]], "write_sparse6() (in module networkx.readwrite.sparse6)": [[1386, "networkx.readwrite.sparse6.write_sparse6"]], "generate_network_text() (in module networkx.readwrite.text)": [[1387, "networkx.readwrite.text.generate_network_text"]], "write_network_text() (in module networkx.readwrite.text)": [[1388, "networkx.readwrite.text.write_network_text"]], "networkx.readwrite.gexf": [[1389, "module-networkx.readwrite.gexf"]], "networkx.readwrite.gml": [[1390, "module-networkx.readwrite.gml"]], "networkx.readwrite.graphml": [[1391, "module-networkx.readwrite.graphml"]], "networkx.readwrite.json_graph": [[1393, "module-networkx.readwrite.json_graph"]], "networkx.readwrite.leda": [[1394, "module-networkx.readwrite.leda"]], "networkx.readwrite.multiline_adjlist": [[1396, "module-networkx.readwrite.multiline_adjlist"]], "networkx.readwrite.pajek": [[1397, "module-networkx.readwrite.pajek"]], "networkx.readwrite.graph6": [[1398, "module-networkx.readwrite.graph6"]], "networkx.readwrite.sparse6": [[1398, "module-networkx.readwrite.sparse6"]], "networkx.readwrite.text": [[1399, "module-networkx.readwrite.text"]], "networkx.relabel": [[1400, "module-networkx.relabel"]], "networkx.utils": [[1401, "module-networkx.utils"]], "networkx.utils.decorators": [[1401, "module-networkx.utils.decorators"]], "networkx.utils.mapped_queue": [[1401, "module-networkx.utils.mapped_queue"]], "networkx.utils.misc": [[1401, "module-networkx.utils.misc"]], "networkx.utils.random_sequence": [[1401, "module-networkx.utils.random_sequence"]], "networkx.utils.rcm": [[1401, "module-networkx.utils.rcm"]], "networkx.utils.union_find": [[1401, "module-networkx.utils.union_find"]]}}) \ No newline at end of file
diff --git a/tutorial-34.pdf b/tutorial-34.pdf
index 53e84e62..2ec0f9b3 100644
--- a/tutorial-34.pdf
+++ b/tutorial-34.pdf
Binary files differ
diff --git a/tutorial-35.pdf b/tutorial-35.pdf
index 371177a0..1a4ea902 100644
--- a/tutorial-35.pdf
+++ b/tutorial-35.pdf
Binary files differ
diff --git a/tutorial-36.pdf b/tutorial-36.pdf
index 34b4a5c6..75da5a5e 100644
--- a/tutorial-36.pdf
+++ b/tutorial-36.pdf
Binary files differ
diff --git a/tutorial.ipynb b/tutorial.ipynb
index 35064095..9f5ce5b0 100644
--- a/tutorial.ipynb
+++ b/tutorial.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "markdown",
- "id": "faf90572",
+ "id": "34932fcf",
"metadata": {},
"source": [
"## Tutorial\n",
@@ -17,7 +17,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "509e60e1",
+ "id": "cd473e7d",
"metadata": {},
"outputs": [],
"source": [
@@ -27,7 +27,7 @@
},
{
"cell_type": "markdown",
- "id": "8f16129b",
+ "id": "7ec0c903",
"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": "bd6562e0",
+ "id": "e41d4029",
"metadata": {},
"outputs": [],
"source": [
@@ -56,7 +56,7 @@
},
{
"cell_type": "markdown",
- "id": "2d825f0d",
+ "id": "66c6ec7a",
"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": "fbb02b73",
+ "id": "69712d73",
"metadata": {},
"outputs": [],
"source": [
@@ -74,7 +74,7 @@
},
{
"cell_type": "markdown",
- "id": "c1329bd5",
+ "id": "cb7b2f97",
"metadata": {},
"source": [
"You can also add nodes along with node\n",
@@ -96,7 +96,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "9f7ed036",
+ "id": "1c7f7b4d",
"metadata": {},
"outputs": [],
"source": [
@@ -106,7 +106,7 @@
},
{
"cell_type": "markdown",
- "id": "352e5eb2",
+ "id": "5cdb5f87",
"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": "70997bf3",
+ "id": "13fe2579",
"metadata": {},
"outputs": [],
"source": [
@@ -125,7 +125,7 @@
},
{
"cell_type": "markdown",
- "id": "d6b38e18",
+ "id": "667e7ee1",
"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": "9eea36b7",
+ "id": "8f1ea633",
"metadata": {},
"outputs": [],
"source": [
@@ -154,7 +154,7 @@
},
{
"cell_type": "markdown",
- "id": "b464b93d",
+ "id": "afd13aa3",
"metadata": {},
"source": [
"by adding a list of edges,"
@@ -163,7 +163,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "34f152ab",
+ "id": "b255bc0a",
"metadata": {},
"outputs": [],
"source": [
@@ -172,7 +172,7 @@
},
{
"cell_type": "markdown",
- "id": "9f21d2b4",
+ "id": "4f7607ce",
"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": "043d9118",
+ "id": "e8759a73",
"metadata": {},
"outputs": [],
"source": [
@@ -194,7 +194,7 @@
},
{
"cell_type": "markdown",
- "id": "b0e6b3d0",
+ "id": "e55e991c",
"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": "a7803e90",
+ "id": "721e72aa",
"metadata": {},
"outputs": [],
"source": [
@@ -213,7 +213,7 @@
},
{
"cell_type": "markdown",
- "id": "ddf6cae4",
+ "id": "785468a4",
"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": "7200f52c",
+ "id": "bbd2a149",
"metadata": {},
"outputs": [],
"source": [
@@ -237,7 +237,7 @@
},
{
"cell_type": "markdown",
- "id": "b5b9201c",
+ "id": "b7ab412e",
"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": "a5ee0237",
+ "id": "cca6ffec",
"metadata": {},
"outputs": [],
"source": [
@@ -257,7 +257,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "f3096427",
+ "id": "eb8820e7",
"metadata": {},
"outputs": [],
"source": [
@@ -272,7 +272,7 @@
},
{
"cell_type": "markdown",
- "id": "f2250e7f",
+ "id": "5eece4bf",
"metadata": {},
"source": [
"# Examining elements of a graph\n",
@@ -292,7 +292,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "50bbc2f9",
+ "id": "600d36b9",
"metadata": {},
"outputs": [],
"source": [
@@ -304,7 +304,7 @@
},
{
"cell_type": "markdown",
- "id": "af406d27",
+ "id": "eb62ccb2",
"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": "b9a24473",
+ "id": "02948235",
"metadata": {},
"outputs": [],
"source": [
@@ -326,7 +326,7 @@
},
{
"cell_type": "markdown",
- "id": "9277ad57",
+ "id": "c0da2d03",
"metadata": {},
"source": [
"# Removing elements from a graph\n",
@@ -343,7 +343,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "2e9d83af",
+ "id": "16a7ac54",
"metadata": {},
"outputs": [],
"source": [
@@ -355,7 +355,7 @@
},
{
"cell_type": "markdown",
- "id": "20eaa24a",
+ "id": "9ad77ea0",
"metadata": {},
"source": [
"# Using the graph constructors\n",
@@ -370,7 +370,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "086e518e",
+ "id": "3a4f5043",
"metadata": {},
"outputs": [],
"source": [
@@ -387,7 +387,7 @@
},
{
"cell_type": "markdown",
- "id": "715a10d5",
+ "id": "d561f022",
"metadata": {},
"source": [
"# What to use as nodes and edges\n",
@@ -416,7 +416,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "ef2ac6b1",
+ "id": "258013e9",
"metadata": {},
"outputs": [],
"source": [
@@ -428,7 +428,7 @@
},
{
"cell_type": "markdown",
- "id": "3dc01db8",
+ "id": "de58f6ba",
"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": "9a28c088",
+ "id": "b8ca122e",
"metadata": {},
"outputs": [],
"source": [
@@ -450,7 +450,7 @@
},
{
"cell_type": "markdown",
- "id": "5d9371d4",
+ "id": "6d44fd70",
"metadata": {},
"source": [
"Fast examination of all (node, adjacency) pairs is achieved using\n",
@@ -461,7 +461,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "4a1ceee5",
+ "id": "edf3809c",
"metadata": {},
"outputs": [],
"source": [
@@ -475,7 +475,7 @@
},
{
"cell_type": "markdown",
- "id": "a85bf73b",
+ "id": "4de66c10",
"metadata": {},
"source": [
"Convenient access to all edges is achieved with the edges property."
@@ -484,7 +484,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "a83a3c2c",
+ "id": "4cd1760c",
"metadata": {},
"outputs": [],
"source": [
@@ -495,7 +495,7 @@
},
{
"cell_type": "markdown",
- "id": "b485a1c1",
+ "id": "eba6152a",
"metadata": {},
"source": [
"# Adding attributes to graphs, nodes, and edges\n",
@@ -517,7 +517,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "1937ca75",
+ "id": "65e41d33",
"metadata": {},
"outputs": [],
"source": [
@@ -527,7 +527,7 @@
},
{
"cell_type": "markdown",
- "id": "96061e33",
+ "id": "a8900dcd",
"metadata": {},
"source": [
"Or you can modify attributes later"
@@ -536,7 +536,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "bdc65098",
+ "id": "4aed51f0",
"metadata": {},
"outputs": [],
"source": [
@@ -546,7 +546,7 @@
},
{
"cell_type": "markdown",
- "id": "013ee034",
+ "id": "f1ae5957",
"metadata": {},
"source": [
"# Node attributes\n",
@@ -557,7 +557,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "5f30fb34",
+ "id": "41e0f958",
"metadata": {},
"outputs": [],
"source": [
@@ -570,7 +570,7 @@
},
{
"cell_type": "markdown",
- "id": "e6cdd75a",
+ "id": "6d1541aa",
"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": "0d67c3ae",
+ "id": "405ad7d8",
"metadata": {},
"outputs": [],
"source": [
@@ -598,7 +598,7 @@
},
{
"cell_type": "markdown",
- "id": "96fa928f",
+ "id": "89ae6128",
"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": "83d74f74",
+ "id": "f1a70f53",
"metadata": {},
"outputs": [],
"source": [
@@ -633,7 +633,7 @@
},
{
"cell_type": "markdown",
- "id": "be22350a",
+ "id": "31d5f5b3",
"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": "7f9d17cf",
+ "id": "a917af82",
"metadata": {},
"outputs": [],
"source": [
@@ -655,7 +655,7 @@
},
{
"cell_type": "markdown",
- "id": "258a0a35",
+ "id": "88026fea",
"metadata": {},
"source": [
"# Multigraphs\n",
@@ -675,7 +675,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "b7d8bd66",
+ "id": "057492be",
"metadata": {},
"outputs": [],
"source": [
@@ -693,7 +693,7 @@
},
{
"cell_type": "markdown",
- "id": "c8621f50",
+ "id": "a884bc8b",
"metadata": {},
"source": [
"# Graph generators and graph operations\n",
@@ -713,7 +713,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "3e5e0ff5",
+ "id": "7d6536ed",
"metadata": {},
"outputs": [],
"source": [
@@ -725,7 +725,7 @@
},
{
"cell_type": "markdown",
- "id": "3943038c",
+ "id": "ae1c1e80",
"metadata": {},
"source": [
"# 4. Using a stochastic graph generator, e.g,\n",
@@ -736,7 +736,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "439a61bd",
+ "id": "e79c3dcf",
"metadata": {},
"outputs": [],
"source": [
@@ -748,7 +748,7 @@
},
{
"cell_type": "markdown",
- "id": "7bc4b49a",
+ "id": "ae0d8913",
"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": "740569d3",
+ "id": "1e573388",
"metadata": {},
"outputs": [],
"source": [
@@ -770,7 +770,7 @@
},
{
"cell_type": "markdown",
- "id": "5ecfcaf4",
+ "id": "408a62d1",
"metadata": {},
"source": [
"For details on graph formats see Reading and writing graphs\n",
@@ -785,7 +785,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "f8750762",
+ "id": "e34295a8",
"metadata": {},
"outputs": [],
"source": [
@@ -799,7 +799,7 @@
},
{
"cell_type": "markdown",
- "id": "77d7137b",
+ "id": "a61c4b28",
"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": "b96e8565",
+ "id": "3a801a03",
"metadata": {},
"outputs": [],
"source": [
@@ -819,7 +819,7 @@
},
{
"cell_type": "markdown",
- "id": "13102485",
+ "id": "ffc65f46",
"metadata": {},
"source": [
"See Algorithms for details on graph algorithms\n",
@@ -838,7 +838,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "0d23c39c",
+ "id": "e9d65ba2",
"metadata": {},
"outputs": [],
"source": [
@@ -847,7 +847,7 @@
},
{
"cell_type": "markdown",
- "id": "3cea4268",
+ "id": "7785478f",
"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": "12db14b2",
+ "id": "96c82e83",
"metadata": {},
"outputs": [],
"source": [
@@ -870,7 +870,7 @@
},
{
"cell_type": "markdown",
- "id": "d528e97f",
+ "id": "9852dd9d",
"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": "a261f16e",
+ "id": "a9969348",
"metadata": {},
"outputs": [],
"source": [
@@ -889,7 +889,7 @@
},
{
"cell_type": "markdown",
- "id": "01288888",
+ "id": "c87db54f",
"metadata": {},
"source": [
"command if you are not using matplotlib in interactive mode."
@@ -898,7 +898,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "64936191",
+ "id": "0f6044ed",
"metadata": {},
"outputs": [],
"source": [
@@ -919,7 +919,7 @@
},
{
"cell_type": "markdown",
- "id": "0091f94a",
+ "id": "d9538900",
"metadata": {},
"source": [
"You can find additional options via `draw_networkx()` and\n",
@@ -930,7 +930,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "52b43d16",
+ "id": "3d0ed48a",
"metadata": {},
"outputs": [],
"source": [
@@ -941,7 +941,7 @@
},
{
"cell_type": "markdown",
- "id": "d883d5f7",
+ "id": "71d93c4e",
"metadata": {},
"source": [
"To save drawings to a file, use, for example"
@@ -950,7 +950,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "6ce05699",
+ "id": "61c0860c",
"metadata": {},
"outputs": [],
"source": [
@@ -960,7 +960,7 @@
},
{
"cell_type": "markdown",
- "id": "4cb4ddfe",
+ "id": "b386763c",
"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": "4da90442",
+ "id": "03e31283",
"metadata": {},
"outputs": [],
"source": [
@@ -985,7 +985,7 @@
},
{
"cell_type": "markdown",
- "id": "259ad362",
+ "id": "a8d17590",
"metadata": {},
"source": [
"See Drawing for additional details.\n",
diff --git a/tutorial_full.ipynb b/tutorial_full.ipynb
index c9f8fd54..c4ca80ec 100644
--- a/tutorial_full.ipynb
+++ b/tutorial_full.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "markdown",
- "id": "faf90572",
+ "id": "34932fcf",
"metadata": {},
"source": [
"## Tutorial\n",
@@ -17,13 +17,13 @@
{
"cell_type": "code",
"execution_count": 1,
- "id": "509e60e1",
+ "id": "cd473e7d",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-26T01:59:52.206669Z",
- "iopub.status.busy": "2023-02-26T01:59:52.206408Z",
- "iopub.status.idle": "2023-02-26T01:59:52.279141Z",
- "shell.execute_reply": "2023-02-26T01:59:52.278097Z"
+ "iopub.execute_input": "2023-02-27T20:27:40.528300Z",
+ "iopub.status.busy": "2023-02-27T20:27:40.528094Z",
+ "iopub.status.idle": "2023-02-27T20:27:40.596239Z",
+ "shell.execute_reply": "2023-02-27T20:27:40.595599Z"
}
},
"outputs": [],
@@ -34,7 +34,7 @@
},
{
"cell_type": "markdown",
- "id": "8f16129b",
+ "id": "7ec0c903",
"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": "bd6562e0",
+ "id": "e41d4029",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-26T01:59:52.282115Z",
- "iopub.status.busy": "2023-02-26T01:59:52.281759Z",
- "iopub.status.idle": "2023-02-26T01:59:52.285051Z",
- "shell.execute_reply": "2023-02-26T01:59:52.284426Z"
+ "iopub.execute_input": "2023-02-27T20:27:40.599589Z",
+ "iopub.status.busy": "2023-02-27T20:27:40.599053Z",
+ "iopub.status.idle": "2023-02-27T20:27:40.602634Z",
+ "shell.execute_reply": "2023-02-27T20:27:40.602047Z"
}
},
"outputs": [],
@@ -70,7 +70,7 @@
},
{
"cell_type": "markdown",
- "id": "2d825f0d",
+ "id": "66c6ec7a",
"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": "fbb02b73",
+ "id": "69712d73",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-26T01:59:52.287701Z",
- "iopub.status.busy": "2023-02-26T01:59:52.287170Z",
- "iopub.status.idle": "2023-02-26T01:59:52.291191Z",
- "shell.execute_reply": "2023-02-26T01:59:52.290472Z"
+ "iopub.execute_input": "2023-02-27T20:27:40.605279Z",
+ "iopub.status.busy": "2023-02-27T20:27:40.604967Z",
+ "iopub.status.idle": "2023-02-27T20:27:40.608511Z",
+ "shell.execute_reply": "2023-02-27T20:27:40.607962Z"
}
},
"outputs": [],
@@ -95,7 +95,7 @@
},
{
"cell_type": "markdown",
- "id": "c1329bd5",
+ "id": "cb7b2f97",
"metadata": {},
"source": [
"You can also add nodes along with node\n",
@@ -117,13 +117,13 @@
{
"cell_type": "code",
"execution_count": 4,
- "id": "9f7ed036",
+ "id": "1c7f7b4d",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-26T01:59:52.293884Z",
- "iopub.status.busy": "2023-02-26T01:59:52.293540Z",
- "iopub.status.idle": "2023-02-26T01:59:52.297161Z",
- "shell.execute_reply": "2023-02-26T01:59:52.296521Z"
+ "iopub.execute_input": "2023-02-27T20:27:40.611052Z",
+ "iopub.status.busy": "2023-02-27T20:27:40.610595Z",
+ "iopub.status.idle": "2023-02-27T20:27:40.615441Z",
+ "shell.execute_reply": "2023-02-27T20:27:40.614890Z"
}
},
"outputs": [],
@@ -134,7 +134,7 @@
},
{
"cell_type": "markdown",
- "id": "352e5eb2",
+ "id": "5cdb5f87",
"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": "70997bf3",
+ "id": "13fe2579",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-26T01:59:52.299658Z",
- "iopub.status.busy": "2023-02-26T01:59:52.299302Z",
- "iopub.status.idle": "2023-02-26T01:59:52.302395Z",
- "shell.execute_reply": "2023-02-26T01:59:52.301772Z"
+ "iopub.execute_input": "2023-02-27T20:27:40.617703Z",
+ "iopub.status.busy": "2023-02-27T20:27:40.617382Z",
+ "iopub.status.idle": "2023-02-27T20:27:40.620969Z",
+ "shell.execute_reply": "2023-02-27T20:27:40.620415Z"
}
},
"outputs": [],
@@ -160,7 +160,7 @@
},
{
"cell_type": "markdown",
- "id": "d6b38e18",
+ "id": "667e7ee1",
"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": "9eea36b7",
+ "id": "8f1ea633",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-26T01:59:52.304943Z",
- "iopub.status.busy": "2023-02-26T01:59:52.304441Z",
- "iopub.status.idle": "2023-02-26T01:59:52.308548Z",
- "shell.execute_reply": "2023-02-26T01:59:52.307908Z"
+ "iopub.execute_input": "2023-02-27T20:27:40.623172Z",
+ "iopub.status.busy": "2023-02-27T20:27:40.622861Z",
+ "iopub.status.idle": "2023-02-27T20:27:40.626764Z",
+ "shell.execute_reply": "2023-02-27T20:27:40.626180Z"
}
},
"outputs": [],
@@ -196,7 +196,7 @@
},
{
"cell_type": "markdown",
- "id": "b464b93d",
+ "id": "afd13aa3",
"metadata": {},
"source": [
"by adding a list of edges,"
@@ -205,13 +205,13 @@
{
"cell_type": "code",
"execution_count": 7,
- "id": "34f152ab",
+ "id": "b255bc0a",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-26T01:59:52.311034Z",
- "iopub.status.busy": "2023-02-26T01:59:52.310697Z",
- "iopub.status.idle": "2023-02-26T01:59:52.313879Z",
- "shell.execute_reply": "2023-02-26T01:59:52.313254Z"
+ "iopub.execute_input": "2023-02-27T20:27:40.629297Z",
+ "iopub.status.busy": "2023-02-27T20:27:40.628978Z",
+ "iopub.status.idle": "2023-02-27T20:27:40.632067Z",
+ "shell.execute_reply": "2023-02-27T20:27:40.631473Z"
}
},
"outputs": [],
@@ -221,7 +221,7 @@
},
{
"cell_type": "markdown",
- "id": "9f21d2b4",
+ "id": "4f7607ce",
"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": "043d9118",
+ "id": "e8759a73",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-26T01:59:52.316595Z",
- "iopub.status.busy": "2023-02-26T01:59:52.316102Z",
- "iopub.status.idle": "2023-02-26T01:59:52.319164Z",
- "shell.execute_reply": "2023-02-26T01:59:52.318540Z"
+ "iopub.execute_input": "2023-02-27T20:27:40.634273Z",
+ "iopub.status.busy": "2023-02-27T20:27:40.633938Z",
+ "iopub.status.idle": "2023-02-27T20:27:40.636894Z",
+ "shell.execute_reply": "2023-02-27T20:27:40.636314Z"
}
},
"outputs": [],
@@ -250,7 +250,7 @@
},
{
"cell_type": "markdown",
- "id": "b0e6b3d0",
+ "id": "e55e991c",
"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": "a7803e90",
+ "id": "721e72aa",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-26T01:59:52.321789Z",
- "iopub.status.busy": "2023-02-26T01:59:52.321300Z",
- "iopub.status.idle": "2023-02-26T01:59:52.324297Z",
- "shell.execute_reply": "2023-02-26T01:59:52.323673Z"
+ "iopub.execute_input": "2023-02-27T20:27:40.639281Z",
+ "iopub.status.busy": "2023-02-27T20:27:40.638778Z",
+ "iopub.status.idle": "2023-02-27T20:27:40.641711Z",
+ "shell.execute_reply": "2023-02-27T20:27:40.641149Z"
}
},
"outputs": [],
@@ -276,7 +276,7 @@
},
{
"cell_type": "markdown",
- "id": "ddf6cae4",
+ "id": "785468a4",
"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": "7200f52c",
+ "id": "bbd2a149",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-26T01:59:52.326927Z",
- "iopub.status.busy": "2023-02-26T01:59:52.326437Z",
- "iopub.status.idle": "2023-02-26T01:59:52.330383Z",
- "shell.execute_reply": "2023-02-26T01:59:52.329740Z"
+ "iopub.execute_input": "2023-02-27T20:27:40.644442Z",
+ "iopub.status.busy": "2023-02-27T20:27:40.643992Z",
+ "iopub.status.idle": "2023-02-27T20:27:40.648254Z",
+ "shell.execute_reply": "2023-02-27T20:27:40.647710Z"
}
},
"outputs": [],
@@ -307,7 +307,7 @@
},
{
"cell_type": "markdown",
- "id": "b5b9201c",
+ "id": "b7ab412e",
"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": "a5ee0237",
+ "id": "cca6ffec",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-26T01:59:52.332928Z",
- "iopub.status.busy": "2023-02-26T01:59:52.332411Z",
- "iopub.status.idle": "2023-02-26T01:59:52.338974Z",
- "shell.execute_reply": "2023-02-26T01:59:52.338339Z"
+ "iopub.execute_input": "2023-02-27T20:27:40.650870Z",
+ "iopub.status.busy": "2023-02-27T20:27:40.650444Z",
+ "iopub.status.idle": "2023-02-27T20:27:40.658394Z",
+ "shell.execute_reply": "2023-02-27T20:27:40.657842Z"
}
},
"outputs": [
@@ -345,13 +345,13 @@
{
"cell_type": "code",
"execution_count": 12,
- "id": "f3096427",
+ "id": "eb8820e7",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-26T01:59:52.341954Z",
- "iopub.status.busy": "2023-02-26T01:59:52.341618Z",
- "iopub.status.idle": "2023-02-26T01:59:52.346091Z",
- "shell.execute_reply": "2023-02-26T01:59:52.345468Z"
+ "iopub.execute_input": "2023-02-27T20:27:40.660970Z",
+ "iopub.status.busy": "2023-02-27T20:27:40.660543Z",
+ "iopub.status.idle": "2023-02-27T20:27:40.665296Z",
+ "shell.execute_reply": "2023-02-27T20:27:40.664752Z"
}
},
"outputs": [],
@@ -367,7 +367,7 @@
},
{
"cell_type": "markdown",
- "id": "f2250e7f",
+ "id": "5eece4bf",
"metadata": {},
"source": [
"# Examining elements of a graph\n",
@@ -387,13 +387,13 @@
{
"cell_type": "code",
"execution_count": 13,
- "id": "50bbc2f9",
+ "id": "600d36b9",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-26T01:59:52.348439Z",
- "iopub.status.busy": "2023-02-26T01:59:52.348105Z",
- "iopub.status.idle": "2023-02-26T01:59:52.352947Z",
- "shell.execute_reply": "2023-02-26T01:59:52.352304Z"
+ "iopub.execute_input": "2023-02-27T20:27:40.667912Z",
+ "iopub.status.busy": "2023-02-27T20:27:40.667490Z",
+ "iopub.status.idle": "2023-02-27T20:27:40.672152Z",
+ "shell.execute_reply": "2023-02-27T20:27:40.671547Z"
}
},
"outputs": [
@@ -417,7 +417,7 @@
},
{
"cell_type": "markdown",
- "id": "af406d27",
+ "id": "eb62ccb2",
"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": "b9a24473",
+ "id": "02948235",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-26T01:59:52.355518Z",
- "iopub.status.busy": "2023-02-26T01:59:52.355282Z",
- "iopub.status.idle": "2023-02-26T01:59:52.359752Z",
- "shell.execute_reply": "2023-02-26T01:59:52.359111Z"
+ "iopub.execute_input": "2023-02-27T20:27:40.674518Z",
+ "iopub.status.busy": "2023-02-27T20:27:40.674209Z",
+ "iopub.status.idle": "2023-02-27T20:27:40.680311Z",
+ "shell.execute_reply": "2023-02-27T20:27:40.679397Z"
}
},
"outputs": [
@@ -457,7 +457,7 @@
},
{
"cell_type": "markdown",
- "id": "9277ad57",
+ "id": "c0da2d03",
"metadata": {},
"source": [
"# Removing elements from a graph\n",
@@ -474,13 +474,13 @@
{
"cell_type": "code",
"execution_count": 15,
- "id": "2e9d83af",
+ "id": "16a7ac54",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-26T01:59:52.362428Z",
- "iopub.status.busy": "2023-02-26T01:59:52.362093Z",
- "iopub.status.idle": "2023-02-26T01:59:52.365483Z",
- "shell.execute_reply": "2023-02-26T01:59:52.364859Z"
+ "iopub.execute_input": "2023-02-27T20:27:40.682683Z",
+ "iopub.status.busy": "2023-02-27T20:27:40.682380Z",
+ "iopub.status.idle": "2023-02-27T20:27:40.686391Z",
+ "shell.execute_reply": "2023-02-27T20:27:40.685850Z"
}
},
"outputs": [],
@@ -493,7 +493,7 @@
},
{
"cell_type": "markdown",
- "id": "20eaa24a",
+ "id": "9ad77ea0",
"metadata": {},
"source": [
"# Using the graph constructors\n",
@@ -508,13 +508,13 @@
{
"cell_type": "code",
"execution_count": 16,
- "id": "086e518e",
+ "id": "3a4f5043",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-26T01:59:52.367959Z",
- "iopub.status.busy": "2023-02-26T01:59:52.367624Z",
- "iopub.status.idle": "2023-02-26T01:59:52.628642Z",
- "shell.execute_reply": "2023-02-26T01:59:52.627632Z"
+ "iopub.execute_input": "2023-02-27T20:27:40.688803Z",
+ "iopub.status.busy": "2023-02-27T20:27:40.688587Z",
+ "iopub.status.idle": "2023-02-27T20:27:40.932822Z",
+ "shell.execute_reply": "2023-02-27T20:27:40.931861Z"
}
},
"outputs": [
@@ -543,7 +543,7 @@
},
{
"cell_type": "markdown",
- "id": "715a10d5",
+ "id": "d561f022",
"metadata": {},
"source": [
"# What to use as nodes and edges\n",
@@ -572,13 +572,13 @@
{
"cell_type": "code",
"execution_count": 17,
- "id": "ef2ac6b1",
+ "id": "258013e9",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-26T01:59:52.631638Z",
- "iopub.status.busy": "2023-02-26T01:59:52.631127Z",
- "iopub.status.idle": "2023-02-26T01:59:52.638095Z",
- "shell.execute_reply": "2023-02-26T01:59:52.637316Z"
+ "iopub.execute_input": "2023-02-27T20:27:40.935659Z",
+ "iopub.status.busy": "2023-02-27T20:27:40.935133Z",
+ "iopub.status.idle": "2023-02-27T20:27:40.941431Z",
+ "shell.execute_reply": "2023-02-27T20:27:40.940892Z"
}
},
"outputs": [
@@ -602,7 +602,7 @@
},
{
"cell_type": "markdown",
- "id": "3dc01db8",
+ "id": "de58f6ba",
"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": "9a28c088",
+ "id": "b8ca122e",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-26T01:59:52.640966Z",
- "iopub.status.busy": "2023-02-26T01:59:52.640434Z",
- "iopub.status.idle": "2023-02-26T01:59:52.646943Z",
- "shell.execute_reply": "2023-02-26T01:59:52.645780Z"
+ "iopub.execute_input": "2023-02-27T20:27:40.943951Z",
+ "iopub.status.busy": "2023-02-27T20:27:40.943626Z",
+ "iopub.status.idle": "2023-02-27T20:27:40.948291Z",
+ "shell.execute_reply": "2023-02-27T20:27:40.947682Z"
}
},
"outputs": [
@@ -642,7 +642,7 @@
},
{
"cell_type": "markdown",
- "id": "5d9371d4",
+ "id": "6d44fd70",
"metadata": {},
"source": [
"Fast examination of all (node, adjacency) pairs is achieved using\n",
@@ -653,13 +653,13 @@
{
"cell_type": "code",
"execution_count": 19,
- "id": "4a1ceee5",
+ "id": "edf3809c",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-26T01:59:52.649797Z",
- "iopub.status.busy": "2023-02-26T01:59:52.649382Z",
- "iopub.status.idle": "2023-02-26T01:59:52.655123Z",
- "shell.execute_reply": "2023-02-26T01:59:52.654479Z"
+ "iopub.execute_input": "2023-02-27T20:27:40.950906Z",
+ "iopub.status.busy": "2023-02-27T20:27:40.950585Z",
+ "iopub.status.idle": "2023-02-27T20:27:40.955410Z",
+ "shell.execute_reply": "2023-02-27T20:27:40.954775Z"
}
},
"outputs": [
@@ -685,7 +685,7 @@
},
{
"cell_type": "markdown",
- "id": "a85bf73b",
+ "id": "4de66c10",
"metadata": {},
"source": [
"Convenient access to all edges is achieved with the edges property."
@@ -694,13 +694,13 @@
{
"cell_type": "code",
"execution_count": 20,
- "id": "a83a3c2c",
+ "id": "4cd1760c",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-26T01:59:52.657586Z",
- "iopub.status.busy": "2023-02-26T01:59:52.657262Z",
- "iopub.status.idle": "2023-02-26T01:59:52.661858Z",
- "shell.execute_reply": "2023-02-26T01:59:52.661210Z"
+ "iopub.execute_input": "2023-02-27T20:27:40.958302Z",
+ "iopub.status.busy": "2023-02-27T20:27:40.957799Z",
+ "iopub.status.idle": "2023-02-27T20:27:40.961577Z",
+ "shell.execute_reply": "2023-02-27T20:27:40.960948Z"
}
},
"outputs": [
@@ -721,7 +721,7 @@
},
{
"cell_type": "markdown",
- "id": "b485a1c1",
+ "id": "eba6152a",
"metadata": {},
"source": [
"# Adding attributes to graphs, nodes, and edges\n",
@@ -743,13 +743,13 @@
{
"cell_type": "code",
"execution_count": 21,
- "id": "1937ca75",
+ "id": "65e41d33",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-26T01:59:52.664478Z",
- "iopub.status.busy": "2023-02-26T01:59:52.663980Z",
- "iopub.status.idle": "2023-02-26T01:59:52.668249Z",
- "shell.execute_reply": "2023-02-26T01:59:52.667612Z"
+ "iopub.execute_input": "2023-02-27T20:27:40.964233Z",
+ "iopub.status.busy": "2023-02-27T20:27:40.963907Z",
+ "iopub.status.idle": "2023-02-27T20:27:40.968043Z",
+ "shell.execute_reply": "2023-02-27T20:27:40.967444Z"
}
},
"outputs": [
@@ -771,7 +771,7 @@
},
{
"cell_type": "markdown",
- "id": "96061e33",
+ "id": "a8900dcd",
"metadata": {},
"source": [
"Or you can modify attributes later"
@@ -780,13 +780,13 @@
{
"cell_type": "code",
"execution_count": 22,
- "id": "bdc65098",
+ "id": "4aed51f0",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-26T01:59:52.671346Z",
- "iopub.status.busy": "2023-02-26T01:59:52.670812Z",
- "iopub.status.idle": "2023-02-26T01:59:52.675091Z",
- "shell.execute_reply": "2023-02-26T01:59:52.674440Z"
+ "iopub.execute_input": "2023-02-27T20:27:40.970602Z",
+ "iopub.status.busy": "2023-02-27T20:27:40.970274Z",
+ "iopub.status.idle": "2023-02-27T20:27:40.974394Z",
+ "shell.execute_reply": "2023-02-27T20:27:40.973773Z"
}
},
"outputs": [
@@ -808,7 +808,7 @@
},
{
"cell_type": "markdown",
- "id": "013ee034",
+ "id": "f1ae5957",
"metadata": {},
"source": [
"# Node attributes\n",
@@ -819,13 +819,13 @@
{
"cell_type": "code",
"execution_count": 23,
- "id": "5f30fb34",
+ "id": "41e0f958",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-26T01:59:52.677560Z",
- "iopub.status.busy": "2023-02-26T01:59:52.677153Z",
- "iopub.status.idle": "2023-02-26T01:59:52.682171Z",
- "shell.execute_reply": "2023-02-26T01:59:52.681509Z"
+ "iopub.execute_input": "2023-02-27T20:27:40.977128Z",
+ "iopub.status.busy": "2023-02-27T20:27:40.976662Z",
+ "iopub.status.idle": "2023-02-27T20:27:40.981552Z",
+ "shell.execute_reply": "2023-02-27T20:27:40.980937Z"
}
},
"outputs": [
@@ -850,7 +850,7 @@
},
{
"cell_type": "markdown",
- "id": "e6cdd75a",
+ "id": "6d1541aa",
"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": "0d67c3ae",
+ "id": "405ad7d8",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-26T01:59:52.684960Z",
- "iopub.status.busy": "2023-02-26T01:59:52.684623Z",
- "iopub.status.idle": "2023-02-26T01:59:52.688859Z",
- "shell.execute_reply": "2023-02-26T01:59:52.688231Z"
+ "iopub.execute_input": "2023-02-27T20:27:40.984007Z",
+ "iopub.status.busy": "2023-02-27T20:27:40.983681Z",
+ "iopub.status.idle": "2023-02-27T20:27:40.988411Z",
+ "shell.execute_reply": "2023-02-27T20:27:40.987852Z"
}
},
"outputs": [],
@@ -885,7 +885,7 @@
},
{
"cell_type": "markdown",
- "id": "96fa928f",
+ "id": "89ae6128",
"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": "83d74f74",
+ "id": "f1a70f53",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-26T01:59:52.691700Z",
- "iopub.status.busy": "2023-02-26T01:59:52.691271Z",
- "iopub.status.idle": "2023-02-26T01:59:52.696779Z",
- "shell.execute_reply": "2023-02-26T01:59:52.696133Z"
+ "iopub.execute_input": "2023-02-27T20:27:40.991045Z",
+ "iopub.status.busy": "2023-02-27T20:27:40.990608Z",
+ "iopub.status.idle": "2023-02-27T20:27:40.995950Z",
+ "shell.execute_reply": "2023-02-27T20:27:40.995337Z"
}
},
"outputs": [
@@ -938,7 +938,7 @@
},
{
"cell_type": "markdown",
- "id": "be22350a",
+ "id": "31d5f5b3",
"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": "7f9d17cf",
+ "id": "a917af82",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-26T01:59:52.699771Z",
- "iopub.status.busy": "2023-02-26T01:59:52.699353Z",
- "iopub.status.idle": "2023-02-26T01:59:52.702771Z",
- "shell.execute_reply": "2023-02-26T01:59:52.702136Z"
+ "iopub.execute_input": "2023-02-27T20:27:40.998446Z",
+ "iopub.status.busy": "2023-02-27T20:27:40.998241Z",
+ "iopub.status.idle": "2023-02-27T20:27:41.001360Z",
+ "shell.execute_reply": "2023-02-27T20:27:41.000754Z"
}
},
"outputs": [],
@@ -967,7 +967,7 @@
},
{
"cell_type": "markdown",
- "id": "258a0a35",
+ "id": "88026fea",
"metadata": {},
"source": [
"# Multigraphs\n",
@@ -987,13 +987,13 @@
{
"cell_type": "code",
"execution_count": 27,
- "id": "b7d8bd66",
+ "id": "057492be",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-26T01:59:52.705221Z",
- "iopub.status.busy": "2023-02-26T01:59:52.704881Z",
- "iopub.status.idle": "2023-02-26T01:59:52.711499Z",
- "shell.execute_reply": "2023-02-26T01:59:52.710832Z"
+ "iopub.execute_input": "2023-02-27T20:27:41.003642Z",
+ "iopub.status.busy": "2023-02-27T20:27:41.003329Z",
+ "iopub.status.idle": "2023-02-27T20:27:41.009737Z",
+ "shell.execute_reply": "2023-02-27T20:27:41.009124Z"
}
},
"outputs": [
@@ -1023,7 +1023,7 @@
},
{
"cell_type": "markdown",
- "id": "c8621f50",
+ "id": "a884bc8b",
"metadata": {},
"source": [
"# Graph generators and graph operations\n",
@@ -1043,13 +1043,13 @@
{
"cell_type": "code",
"execution_count": 28,
- "id": "3e5e0ff5",
+ "id": "7d6536ed",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-26T01:59:52.714527Z",
- "iopub.status.busy": "2023-02-26T01:59:52.714013Z",
- "iopub.status.idle": "2023-02-26T01:59:52.718384Z",
- "shell.execute_reply": "2023-02-26T01:59:52.717741Z"
+ "iopub.execute_input": "2023-02-27T20:27:41.012449Z",
+ "iopub.status.busy": "2023-02-27T20:27:41.011965Z",
+ "iopub.status.idle": "2023-02-27T20:27:41.016263Z",
+ "shell.execute_reply": "2023-02-27T20:27:41.015647Z"
}
},
"outputs": [],
@@ -1062,7 +1062,7 @@
},
{
"cell_type": "markdown",
- "id": "3943038c",
+ "id": "ae1c1e80",
"metadata": {},
"source": [
"# 4. Using a stochastic graph generator, e.g,\n",
@@ -1073,13 +1073,13 @@
{
"cell_type": "code",
"execution_count": 29,
- "id": "439a61bd",
+ "id": "e79c3dcf",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-26T01:59:52.720828Z",
- "iopub.status.busy": "2023-02-26T01:59:52.720487Z",
- "iopub.status.idle": "2023-02-26T01:59:52.735072Z",
- "shell.execute_reply": "2023-02-26T01:59:52.733943Z"
+ "iopub.execute_input": "2023-02-27T20:27:41.018707Z",
+ "iopub.status.busy": "2023-02-27T20:27:41.018379Z",
+ "iopub.status.idle": "2023-02-27T20:27:41.040762Z",
+ "shell.execute_reply": "2023-02-27T20:27:41.040170Z"
}
},
"outputs": [],
@@ -1092,7 +1092,7 @@
},
{
"cell_type": "markdown",
- "id": "7bc4b49a",
+ "id": "ae0d8913",
"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": "740569d3",
+ "id": "1e573388",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-26T01:59:52.737694Z",
- "iopub.status.busy": "2023-02-26T01:59:52.737345Z",
- "iopub.status.idle": "2023-02-26T01:59:53.058128Z",
- "shell.execute_reply": "2023-02-26T01:59:53.057088Z"
+ "iopub.execute_input": "2023-02-27T20:27:41.043148Z",
+ "iopub.status.busy": "2023-02-27T20:27:41.042833Z",
+ "iopub.status.idle": "2023-02-27T20:27:41.854636Z",
+ "shell.execute_reply": "2023-02-27T20:27:41.854019Z"
}
},
"outputs": [],
@@ -1121,7 +1121,7 @@
},
{
"cell_type": "markdown",
- "id": "5ecfcaf4",
+ "id": "408a62d1",
"metadata": {},
"source": [
"For details on graph formats see Reading and writing graphs\n",
@@ -1136,13 +1136,13 @@
{
"cell_type": "code",
"execution_count": 31,
- "id": "f8750762",
+ "id": "e34295a8",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-26T01:59:53.061833Z",
- "iopub.status.busy": "2023-02-26T01:59:53.061350Z",
- "iopub.status.idle": "2023-02-26T01:59:53.067737Z",
- "shell.execute_reply": "2023-02-26T01:59:53.067050Z"
+ "iopub.execute_input": "2023-02-27T20:27:41.857733Z",
+ "iopub.status.busy": "2023-02-27T20:27:41.857489Z",
+ "iopub.status.idle": "2023-02-27T20:27:41.864641Z",
+ "shell.execute_reply": "2023-02-27T20:27:41.864067Z"
}
},
"outputs": [
@@ -1168,7 +1168,7 @@
},
{
"cell_type": "markdown",
- "id": "77d7137b",
+ "id": "a61c4b28",
"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": "b96e8565",
+ "id": "3a801a03",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-26T01:59:53.070685Z",
- "iopub.status.busy": "2023-02-26T01:59:53.070293Z",
- "iopub.status.idle": "2023-02-26T01:59:53.076571Z",
- "shell.execute_reply": "2023-02-26T01:59:53.075668Z"
+ "iopub.execute_input": "2023-02-27T20:27:41.867044Z",
+ "iopub.status.busy": "2023-02-27T20:27:41.866709Z",
+ "iopub.status.idle": "2023-02-27T20:27:41.871794Z",
+ "shell.execute_reply": "2023-02-27T20:27:41.871210Z"
}
},
"outputs": [
@@ -1206,7 +1206,7 @@
},
{
"cell_type": "markdown",
- "id": "13102485",
+ "id": "ffc65f46",
"metadata": {},
"source": [
"See Algorithms for details on graph algorithms\n",
@@ -1225,13 +1225,13 @@
{
"cell_type": "code",
"execution_count": 33,
- "id": "0d23c39c",
+ "id": "e9d65ba2",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-26T01:59:53.079488Z",
- "iopub.status.busy": "2023-02-26T01:59:53.078967Z",
- "iopub.status.idle": "2023-02-26T01:59:53.394606Z",
- "shell.execute_reply": "2023-02-26T01:59:53.393169Z"
+ "iopub.execute_input": "2023-02-27T20:27:41.874033Z",
+ "iopub.status.busy": "2023-02-27T20:27:41.873712Z",
+ "iopub.status.idle": "2023-02-27T20:27:42.227907Z",
+ "shell.execute_reply": "2023-02-27T20:27:42.227231Z"
}
},
"outputs": [],
@@ -1241,7 +1241,7 @@
},
{
"cell_type": "markdown",
- "id": "3cea4268",
+ "id": "7785478f",
"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": "12db14b2",
+ "id": "96c82e83",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-26T01:59:53.398051Z",
- "iopub.status.busy": "2023-02-26T01:59:53.397540Z",
- "iopub.status.idle": "2023-02-26T01:59:53.633388Z",
- "shell.execute_reply": "2023-02-26T01:59:53.632795Z"
+ "iopub.execute_input": "2023-02-27T20:27:42.231373Z",
+ "iopub.status.busy": "2023-02-27T20:27:42.230727Z",
+ "iopub.status.idle": "2023-02-27T20:27:42.454194Z",
+ "shell.execute_reply": "2023-02-27T20:27:42.453572Z"
}
},
"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": "d528e97f",
+ "id": "9852dd9d",
"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": "a261f16e",
+ "id": "a9969348",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-26T01:59:53.636566Z",
- "iopub.status.busy": "2023-02-26T01:59:53.636122Z",
- "iopub.status.idle": "2023-02-26T01:59:53.639293Z",
- "shell.execute_reply": "2023-02-26T01:59:53.638784Z"
+ "iopub.execute_input": "2023-02-27T20:27:42.457029Z",
+ "iopub.status.busy": "2023-02-27T20:27:42.456673Z",
+ "iopub.status.idle": "2023-02-27T20:27:42.459812Z",
+ "shell.execute_reply": "2023-02-27T20:27:42.459197Z"
}
},
"outputs": [],
@@ -1308,7 +1308,7 @@
},
{
"cell_type": "markdown",
- "id": "01288888",
+ "id": "c87db54f",
"metadata": {},
"source": [
"command if you are not using matplotlib in interactive mode."
@@ -1317,19 +1317,19 @@
{
"cell_type": "code",
"execution_count": 36,
- "id": "64936191",
+ "id": "0f6044ed",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-26T01:59:53.641819Z",
- "iopub.status.busy": "2023-02-26T01:59:53.641404Z",
- "iopub.status.idle": "2023-02-26T01:59:53.984715Z",
- "shell.execute_reply": "2023-02-26T01:59:53.983950Z"
+ "iopub.execute_input": "2023-02-27T20:27:42.462361Z",
+ "iopub.status.busy": "2023-02-27T20:27:42.461903Z",
+ "iopub.status.idle": "2023-02-27T20:27:42.785906Z",
+ "shell.execute_reply": "2023-02-27T20:27:42.785191Z"
}
},
"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": "0091f94a",
+ "id": "d9538900",
"metadata": {},
"source": [
"You can find additional options via `draw_networkx()` and\n",
@@ -1367,13 +1367,13 @@
{
"cell_type": "code",
"execution_count": 37,
- "id": "52b43d16",
+ "id": "3d0ed48a",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-26T01:59:53.987815Z",
- "iopub.status.busy": "2023-02-26T01:59:53.987362Z",
- "iopub.status.idle": "2023-02-26T01:59:54.112065Z",
- "shell.execute_reply": "2023-02-26T01:59:54.111405Z"
+ "iopub.execute_input": "2023-02-27T20:27:42.789077Z",
+ "iopub.status.busy": "2023-02-27T20:27:42.788559Z",
+ "iopub.status.idle": "2023-02-27T20:27:42.905981Z",
+ "shell.execute_reply": "2023-02-27T20:27:42.905345Z"
}
},
"outputs": [
@@ -1396,7 +1396,7 @@
},
{
"cell_type": "markdown",
- "id": "d883d5f7",
+ "id": "71d93c4e",
"metadata": {},
"source": [
"To save drawings to a file, use, for example"
@@ -1405,19 +1405,19 @@
{
"cell_type": "code",
"execution_count": 38,
- "id": "6ce05699",
+ "id": "61c0860c",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-26T01:59:54.115130Z",
- "iopub.status.busy": "2023-02-26T01:59:54.114723Z",
- "iopub.status.idle": "2023-02-26T01:59:54.335882Z",
- "shell.execute_reply": "2023-02-26T01:59:54.335264Z"
+ "iopub.execute_input": "2023-02-27T20:27:42.908903Z",
+ "iopub.status.busy": "2023-02-27T20:27:42.908462Z",
+ "iopub.status.idle": "2023-02-27T20:27:43.055706Z",
+ "shell.execute_reply": "2023-02-27T20:27:43.055067Z"
}
},
"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": "4cb4ddfe",
+ "id": "b386763c",
"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": "4da90442",
+ "id": "03e31283",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-26T01:59:54.339196Z",
- "iopub.status.busy": "2023-02-26T01:59:54.338506Z",
- "iopub.status.idle": "2023-02-26T01:59:54.490351Z",
- "shell.execute_reply": "2023-02-26T01:59:54.489775Z"
+ "iopub.execute_input": "2023-02-27T20:27:43.058591Z",
+ "iopub.status.busy": "2023-02-27T20:27:43.058255Z",
+ "iopub.status.idle": "2023-02-27T20:27:43.204812Z",
+ "shell.execute_reply": "2023-02-27T20:27:43.204169Z"
}
},
"outputs": [
@@ -1476,7 +1476,7 @@
},
{
"cell_type": "markdown",
- "id": "259ad362",
+ "id": "a8d17590",
"metadata": {},
"source": [
"See Drawing for additional details.\n",