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  <section id="node-disjoint-paths">
<h1>node_disjoint_paths<a class="headerlink" href="#node-disjoint-paths" title="Permalink to this heading">#</a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="networkx.algorithms.connectivity.disjoint_paths.node_disjoint_paths">
<span class="sig-name descname"><span class="pre">node_disjoint_paths</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">G</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">s</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flow_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cutoff</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">auxiliary</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">residual</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/networkx/algorithms/connectivity/disjoint_paths.html#node_disjoint_paths"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#networkx.algorithms.connectivity.disjoint_paths.node_disjoint_paths" title="Permalink to this definition">#</a></dt>
<dd><p>Computes node disjoint paths between source and target.</p>
<p>Node disjoint paths are paths that only share their first and last
nodes. The number of node independent paths between two nodes is
equal to their local node connectivity.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>G</strong><span class="classifier">NetworkX graph</span></dt><dd></dd>
<dt><strong>s</strong><span class="classifier">node</span></dt><dd><p>Source node.</p>
</dd>
<dt><strong>t</strong><span class="classifier">node</span></dt><dd><p>Target node.</p>
</dd>
<dt><strong>flow_func</strong><span class="classifier">function</span></dt><dd><p>A function for computing the maximum flow among a pair of nodes.
The function has to accept at least three parameters: a Digraph,
a source node, and a target node. And return a residual network
that follows NetworkX conventions (see <code class="xref py py-meth docutils literal notranslate"><span class="pre">maximum_flow()</span></code> for
details). If flow_func is None, the default maximum flow function
(<code class="xref py py-meth docutils literal notranslate"><span class="pre">edmonds_karp()</span></code>) is used. See below for details. The choice
of the default function may change from version to version and
should not be relied on. Default value: None.</p>
</dd>
<dt><strong>cutoff</strong><span class="classifier">integer or None (default: None)</span></dt><dd><p>Maximum number of paths to yield. If specified, the maximum flow
algorithm will terminate when the flow value reaches or exceeds the
cutoff. This only works for flows that support the cutoff parameter
(most do) and is ignored otherwise.</p>
</dd>
<dt><strong>auxiliary</strong><span class="classifier">NetworkX DiGraph</span></dt><dd><p>Auxiliary digraph to compute flow based node connectivity. It has
to have a graph attribute called mapping with a dictionary mapping
node names in G and in the auxiliary digraph. If provided
it will be reused instead of recreated. Default value: None.</p>
</dd>
<dt><strong>residual</strong><span class="classifier">NetworkX DiGraph</span></dt><dd><p>Residual network to compute maximum flow. If provided it will be
reused instead of recreated. Default value: None.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>paths</strong><span class="classifier">generator</span></dt><dd><p>Generator of node disjoint paths.</p>
</dd>
</dl>
</dd>
<dt class="field-odd">Raises<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt>NetworkXNoPath</dt><dd><p>If there is no path between source and target.</p>
</dd>
<dt>NetworkXError</dt><dd><p>If source or target are not in the graph G.</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="networkx.algorithms.connectivity.disjoint_paths.edge_disjoint_paths.html#networkx.algorithms.connectivity.disjoint_paths.edge_disjoint_paths" title="networkx.algorithms.connectivity.disjoint_paths.edge_disjoint_paths"><code class="xref py py-meth docutils literal notranslate"><span class="pre">edge_disjoint_paths()</span></code></a></dt><dd></dd>
<dt><code class="xref py py-meth docutils literal notranslate"><span class="pre">node_connectivity()</span></code></dt><dd></dd>
<dt><code class="xref py py-meth docutils literal notranslate"><span class="pre">maximum_flow()</span></code></dt><dd></dd>
<dt><code class="xref py py-meth docutils literal notranslate"><span class="pre">edmonds_karp()</span></code></dt><dd></dd>
<dt><code class="xref py py-meth docutils literal notranslate"><span class="pre">preflow_push()</span></code></dt><dd></dd>
<dt><code class="xref py py-meth docutils literal notranslate"><span class="pre">shortest_augmenting_path()</span></code></dt><dd></dd>
</dl>
</div>
<p class="rubric">Notes</p>
<p>This is a flow based implementation of node disjoint paths. We compute
the maximum flow between source and target on an auxiliary directed
network. The saturated edges in the residual network after running the
maximum flow algorithm correspond to node disjoint paths between source
and target in the original network. This function handles both directed
and undirected graphs, and can use all flow algorithms from NetworkX flow
package.</p>
<p class="rubric">Examples</p>
<p>We use in this example the platonic icosahedral graph, which has node
connectivity 5, thus there are 5 node disjoint paths between any pair
of non neighbor nodes.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">G</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">icosahedral_graph</span><span class="p">()</span>
<span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">nx</span><span class="o">.</span><span class="n">node_disjoint_paths</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">6</span><span class="p">)))</span>
<span class="go">5</span>
</pre></div>
</div>
<p>If you need to compute node disjoint paths between several pairs of
nodes in the same graph, it is recommended that you reuse the
data structures that NetworkX uses in the computation: the
auxiliary digraph for node connectivity and node cuts, and the
residual network for the underlying maximum flow computation.</p>
<p>Example of how to compute node disjoint paths reusing the data
structures:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="c1"># You also have to explicitly import the function for</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># building the auxiliary digraph from the connectivity package</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">networkx.algorithms.connectivity</span> <span class="kn">import</span> <span class="n">build_auxiliary_node_connectivity</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">H</span> <span class="o">=</span> <span class="n">build_auxiliary_node_connectivity</span><span class="p">(</span><span class="n">G</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># And the function for building the residual network from the</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># flow package</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">networkx.algorithms.flow</span> <span class="kn">import</span> <span class="n">build_residual_network</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># Note that the auxiliary digraph has an edge attribute named capacity</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">R</span> <span class="o">=</span> <span class="n">build_residual_network</span><span class="p">(</span><span class="n">H</span><span class="p">,</span> <span class="s2">&quot;capacity&quot;</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># Reuse the auxiliary digraph and the residual network by passing them</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># as arguments</span>
<span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">nx</span><span class="o">.</span><span class="n">node_disjoint_paths</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="n">auxiliary</span><span class="o">=</span><span class="n">H</span><span class="p">,</span> <span class="n">residual</span><span class="o">=</span><span class="n">R</span><span class="p">)))</span>
<span class="go">5</span>
</pre></div>
</div>
<p>You can also use alternative flow algorithms for computing node disjoint
paths. For instance, in dense networks the algorithm
<code class="xref py py-meth docutils literal notranslate"><span class="pre">shortest_augmenting_path()</span></code> will usually perform better than
the default <code class="xref py py-meth docutils literal notranslate"><span class="pre">edmonds_karp()</span></code> which is faster for sparse
networks with highly skewed degree distributions. Alternative flow
functions have to be explicitly imported from the flow package.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">networkx.algorithms.flow</span> <span class="kn">import</span> <span class="n">shortest_augmenting_path</span>
<span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">nx</span><span class="o">.</span><span class="n">node_disjoint_paths</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="n">flow_func</span><span class="o">=</span><span class="n">shortest_augmenting_path</span><span class="p">)))</span>
<span class="go">5</span>
</pre></div>
</div>
</dd></dl>

</section>


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