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<section id="module-networkx.algorithms.approximation">
<span id="approximations-and-heuristics"></span><h1>Approximations and Heuristics<a class="headerlink" href="#module-networkx.algorithms.approximation" title="Permalink to this heading">#</a></h1>
<p>Approximations of graph properties and Heuristic methods for optimization.</p>
<p>The functions in this class are not imported into the top-level <code class="docutils literal notranslate"><span class="pre">networkx</span></code>
namespace so the easiest way to use them is with:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">networkx.algorithms</span> <span class="kn">import</span> <span class="n">approximation</span>
</pre></div>
</div>
<p>Another option is to import the specific function with
<code class="docutils literal notranslate"><span class="pre">from</span> <span class="pre">networkx.algorithms.approximation</span> <span class="pre">import</span> <span class="pre">function_name</span></code>.</p>
<section id="module-networkx.algorithms.approximation.connectivity">
<span id="connectivity"></span><h2>Connectivity<a class="headerlink" href="#module-networkx.algorithms.approximation.connectivity" title="Permalink to this heading">#</a></h2>
<p>Fast approximation for node connectivity</p>
<table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/networkx.algorithms.approximation.connectivity.all_pairs_node_connectivity.html#networkx.algorithms.approximation.connectivity.all_pairs_node_connectivity" title="networkx.algorithms.approximation.connectivity.all_pairs_node_connectivity"><code class="xref py py-obj docutils literal notranslate"><span class="pre">all_pairs_node_connectivity</span></code></a>(G[, nbunch, cutoff])</p></td>
<td><p>Compute node connectivity between all pairs of nodes.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/networkx.algorithms.approximation.connectivity.local_node_connectivity.html#networkx.algorithms.approximation.connectivity.local_node_connectivity" title="networkx.algorithms.approximation.connectivity.local_node_connectivity"><code class="xref py py-obj docutils literal notranslate"><span class="pre">local_node_connectivity</span></code></a>(G, source, target[, ...])</p></td>
<td><p>Compute node connectivity between source and target.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/networkx.algorithms.approximation.connectivity.node_connectivity.html#networkx.algorithms.approximation.connectivity.node_connectivity" title="networkx.algorithms.approximation.connectivity.node_connectivity"><code class="xref py py-obj docutils literal notranslate"><span class="pre">node_connectivity</span></code></a>(G[, s, t])</p></td>
<td><p>Returns an approximation for node connectivity for a graph or digraph G.</p></td>
</tr>
</tbody>
</table>
</section>
<section id="module-networkx.algorithms.approximation.kcomponents">
<span id="k-components"></span><h2>K-components<a class="headerlink" href="#module-networkx.algorithms.approximation.kcomponents" title="Permalink to this heading">#</a></h2>
<p>Fast approximation for k-component structure</p>
<table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/networkx.algorithms.approximation.kcomponents.k_components.html#networkx.algorithms.approximation.kcomponents.k_components" title="networkx.algorithms.approximation.kcomponents.k_components"><code class="xref py py-obj docutils literal notranslate"><span class="pre">k_components</span></code></a>(G[, min_density])</p></td>
<td><p>Returns the approximate k-component structure of a graph G.</p></td>
</tr>
</tbody>
</table>
</section>
<section id="module-networkx.algorithms.approximation.clique">
<span id="clique"></span><h2>Clique<a class="headerlink" href="#module-networkx.algorithms.approximation.clique" title="Permalink to this heading">#</a></h2>
<p>Functions for computing large cliques and maximum independent sets.</p>
<table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/networkx.algorithms.approximation.clique.maximum_independent_set.html#networkx.algorithms.approximation.clique.maximum_independent_set" title="networkx.algorithms.approximation.clique.maximum_independent_set"><code class="xref py py-obj docutils literal notranslate"><span class="pre">maximum_independent_set</span></code></a>(G)</p></td>
<td><p>Returns an approximate maximum independent set.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/networkx.algorithms.approximation.clique.max_clique.html#networkx.algorithms.approximation.clique.max_clique" title="networkx.algorithms.approximation.clique.max_clique"><code class="xref py py-obj docutils literal notranslate"><span class="pre">max_clique</span></code></a>(G)</p></td>
<td><p>Find the Maximum Clique</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/networkx.algorithms.approximation.clique.clique_removal.html#networkx.algorithms.approximation.clique.clique_removal" title="networkx.algorithms.approximation.clique.clique_removal"><code class="xref py py-obj docutils literal notranslate"><span class="pre">clique_removal</span></code></a>(G)</p></td>
<td><p>Repeatedly remove cliques from the graph.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/networkx.algorithms.approximation.clique.large_clique_size.html#networkx.algorithms.approximation.clique.large_clique_size" title="networkx.algorithms.approximation.clique.large_clique_size"><code class="xref py py-obj docutils literal notranslate"><span class="pre">large_clique_size</span></code></a>(G)</p></td>
<td><p>Find the size of a large clique in a graph.</p></td>
</tr>
</tbody>
</table>
</section>
<section id="module-networkx.algorithms.approximation.clustering_coefficient">
<span id="clustering"></span><h2>Clustering<a class="headerlink" href="#module-networkx.algorithms.approximation.clustering_coefficient" title="Permalink to this heading">#</a></h2>
<table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/networkx.algorithms.approximation.clustering_coefficient.average_clustering.html#networkx.algorithms.approximation.clustering_coefficient.average_clustering" title="networkx.algorithms.approximation.clustering_coefficient.average_clustering"><code class="xref py py-obj docutils literal notranslate"><span class="pre">average_clustering</span></code></a>(G[, trials, seed])</p></td>
<td><p>Estimates the average clustering coefficient of G.</p></td>
</tr>
</tbody>
</table>
</section>
<section id="module-networkx.algorithms.approximation.distance_measures">
<span id="distance-measures"></span><h2>Distance Measures<a class="headerlink" href="#module-networkx.algorithms.approximation.distance_measures" title="Permalink to this heading">#</a></h2>
<p>Distance measures approximated metrics.</p>
<table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/networkx.algorithms.approximation.distance_measures.diameter.html#networkx.algorithms.approximation.distance_measures.diameter" title="networkx.algorithms.approximation.distance_measures.diameter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">diameter</span></code></a>(G[, seed])</p></td>
<td><p>Returns a lower bound on the diameter of the graph G.</p></td>
</tr>
</tbody>
</table>
</section>
<section id="module-networkx.algorithms.approximation.dominating_set">
<span id="dominating-set"></span><h2>Dominating Set<a class="headerlink" href="#module-networkx.algorithms.approximation.dominating_set" title="Permalink to this heading">#</a></h2>
<p>Functions for finding node and edge dominating sets.</p>
<p>A <a class="reference external" href="https://en.wikipedia.org/wiki/Dominating_set">dominating set</a> for an undirected graph <em>G</em> with vertex set <em>V</em>
and edge set <em>E</em> is a subset <em>D</em> of <em>V</em> such that every vertex not in
<em>D</em> is adjacent to at least one member of <em>D</em>. An <a class="reference external" href="https://en.wikipedia.org/wiki/Edge_dominating_set">edge dominating set</a>
is a subset <em>F</em> of <em>E</em> such that every edge not in <em>F</em> is
incident to an endpoint of at least one edge in <em>F</em>.</p>
<table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/networkx.algorithms.approximation.dominating_set.min_weighted_dominating_set.html#networkx.algorithms.approximation.dominating_set.min_weighted_dominating_set" title="networkx.algorithms.approximation.dominating_set.min_weighted_dominating_set"><code class="xref py py-obj docutils literal notranslate"><span class="pre">min_weighted_dominating_set</span></code></a>(G[, weight])</p></td>
<td><p>Returns a dominating set that approximates the minimum weight node dominating set.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/networkx.algorithms.approximation.dominating_set.min_edge_dominating_set.html#networkx.algorithms.approximation.dominating_set.min_edge_dominating_set" title="networkx.algorithms.approximation.dominating_set.min_edge_dominating_set"><code class="xref py py-obj docutils literal notranslate"><span class="pre">min_edge_dominating_set</span></code></a>(G)</p></td>
<td><p>Returns minimum cardinality edge dominating set.</p></td>
</tr>
</tbody>
</table>
</section>
<section id="module-networkx.algorithms.approximation.matching">
<span id="matching"></span><h2>Matching<a class="headerlink" href="#module-networkx.algorithms.approximation.matching" title="Permalink to this heading">#</a></h2>
<p>Given a graph G = (V,E), a matching M in G is a set of pairwise non-adjacent
edges; that is, no two edges share a common vertex.</p>
<p><a class="reference external" href="https://en.wikipedia.org/wiki/Matching_(graph_theory)">Wikipedia: Matching</a></p>
<table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/networkx.algorithms.approximation.matching.min_maximal_matching.html#networkx.algorithms.approximation.matching.min_maximal_matching" title="networkx.algorithms.approximation.matching.min_maximal_matching"><code class="xref py py-obj docutils literal notranslate"><span class="pre">min_maximal_matching</span></code></a>(G)</p></td>
<td><p>Returns the minimum maximal matching of G.</p></td>
</tr>
</tbody>
</table>
</section>
<section id="module-networkx.algorithms.approximation.ramsey">
<span id="ramsey"></span><h2>Ramsey<a class="headerlink" href="#module-networkx.algorithms.approximation.ramsey" title="Permalink to this heading">#</a></h2>
<p>Ramsey numbers.</p>
<table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/networkx.algorithms.approximation.ramsey.ramsey_R2.html#networkx.algorithms.approximation.ramsey.ramsey_R2" title="networkx.algorithms.approximation.ramsey.ramsey_R2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ramsey_R2</span></code></a>(G)</p></td>
<td><p>Compute the largest clique and largest independent set in <code class="xref py py-obj docutils literal notranslate"><span class="pre">G</span></code>.</p></td>
</tr>
</tbody>
</table>
</section>
<section id="module-networkx.algorithms.approximation.steinertree">
<span id="steiner-tree"></span><h2>Steiner Tree<a class="headerlink" href="#module-networkx.algorithms.approximation.steinertree" title="Permalink to this heading">#</a></h2>
<table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/networkx.algorithms.approximation.steinertree.metric_closure.html#networkx.algorithms.approximation.steinertree.metric_closure" title="networkx.algorithms.approximation.steinertree.metric_closure"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metric_closure</span></code></a>(G[, weight])</p></td>
<td><p>Return the metric closure of a graph.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/networkx.algorithms.approximation.steinertree.steiner_tree.html#networkx.algorithms.approximation.steinertree.steiner_tree" title="networkx.algorithms.approximation.steinertree.steiner_tree"><code class="xref py py-obj docutils literal notranslate"><span class="pre">steiner_tree</span></code></a>(G, terminal_nodes[, weight, method])</p></td>
<td><p>Return an approximation to the minimum Steiner tree of a graph.</p></td>
</tr>
</tbody>
</table>
</section>
<section id="module-networkx.algorithms.approximation.traveling_salesman">
<span id="traveling-salesman"></span><h2>Traveling Salesman<a class="headerlink" href="#module-networkx.algorithms.approximation.traveling_salesman" title="Permalink to this heading">#</a></h2>
<section id="travelling-salesman-problem-tsp">
<h3>Travelling Salesman Problem (TSP)<a class="headerlink" href="#travelling-salesman-problem-tsp" title="Permalink to this heading">#</a></h3>
<p>Implementation of approximate algorithms
for solving and approximating the TSP problem.</p>
<p>Categories of algorithms which are implemented:</p>
<ul class="simple">
<li><p>Christofides (provides a 3/2-approximation of TSP)</p></li>
<li><p>Greedy</p></li>
<li><p>Simulated Annealing (SA)</p></li>
<li><p>Threshold Accepting (TA)</p></li>
<li><p>Asadpour Asymmetric Traveling Salesman Algorithm</p></li>
</ul>
<p>The Travelling Salesman Problem tries to find, given the weight
(distance) between all points where a salesman has to visit, the
route so that:</p>
<ul class="simple">
<li><p>The total distance (cost) which the salesman travels is minimized.</p></li>
<li><p>The salesman returns to the starting point.</p></li>
<li><p>Note that for a complete graph, the salesman visits each point once.</p></li>
</ul>
<p>The function <code class="xref py py-obj docutils literal notranslate"><span class="pre">travelling_salesman_problem</span></code> allows for incomplete
graphs by finding all-pairs shortest paths, effectively converting
the problem to a complete graph problem. It calls one of the
approximate methods on that problem and then converts the result
back to the original graph using the previously found shortest paths.</p>
<p>TSP is an NP-hard problem in combinatorial optimization,
important in operations research and theoretical computer science.</p>
<p><a class="reference external" href="http://en.wikipedia.org/wiki/Travelling_salesman_problem">http://en.wikipedia.org/wiki/Travelling_salesman_problem</a></p>
</section>
<table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/networkx.algorithms.approximation.traveling_salesman.christofides.html#networkx.algorithms.approximation.traveling_salesman.christofides" title="networkx.algorithms.approximation.traveling_salesman.christofides"><code class="xref py py-obj docutils literal notranslate"><span class="pre">christofides</span></code></a>(G[, weight, tree])</p></td>
<td><p>Approximate a solution of the traveling salesman problem</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/networkx.algorithms.approximation.traveling_salesman.traveling_salesman_problem.html#networkx.algorithms.approximation.traveling_salesman.traveling_salesman_problem" title="networkx.algorithms.approximation.traveling_salesman.traveling_salesman_problem"><code class="xref py py-obj docutils literal notranslate"><span class="pre">traveling_salesman_problem</span></code></a>(G[, weight, ...])</p></td>
<td><p>Find the shortest path in <code class="xref py py-obj docutils literal notranslate"><span class="pre">G</span></code> connecting specified nodes</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/networkx.algorithms.approximation.traveling_salesman.greedy_tsp.html#networkx.algorithms.approximation.traveling_salesman.greedy_tsp" title="networkx.algorithms.approximation.traveling_salesman.greedy_tsp"><code class="xref py py-obj docutils literal notranslate"><span class="pre">greedy_tsp</span></code></a>(G[, weight, source])</p></td>
<td><p>Return a low cost cycle starting at <code class="xref py py-obj docutils literal notranslate"><span class="pre">source</span></code> and its cost.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/networkx.algorithms.approximation.traveling_salesman.simulated_annealing_tsp.html#networkx.algorithms.approximation.traveling_salesman.simulated_annealing_tsp" title="networkx.algorithms.approximation.traveling_salesman.simulated_annealing_tsp"><code class="xref py py-obj docutils literal notranslate"><span class="pre">simulated_annealing_tsp</span></code></a>(G, init_cycle[, ...])</p></td>
<td><p>Returns an approximate solution to the traveling salesman problem.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/networkx.algorithms.approximation.traveling_salesman.threshold_accepting_tsp.html#networkx.algorithms.approximation.traveling_salesman.threshold_accepting_tsp" title="networkx.algorithms.approximation.traveling_salesman.threshold_accepting_tsp"><code class="xref py py-obj docutils literal notranslate"><span class="pre">threshold_accepting_tsp</span></code></a>(G, init_cycle[, ...])</p></td>
<td><p>Returns an approximate solution to the traveling salesman problem.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/networkx.algorithms.approximation.traveling_salesman.asadpour_atsp.html#networkx.algorithms.approximation.traveling_salesman.asadpour_atsp" title="networkx.algorithms.approximation.traveling_salesman.asadpour_atsp"><code class="xref py py-obj docutils literal notranslate"><span class="pre">asadpour_atsp</span></code></a>(G[, weight, seed, source])</p></td>
<td><p>Returns an approximate solution to the traveling salesman problem.</p></td>
</tr>
</tbody>
</table>
</section>
<section id="module-networkx.algorithms.approximation.treewidth">
<span id="treewidth"></span><h2>Treewidth<a class="headerlink" href="#module-networkx.algorithms.approximation.treewidth" title="Permalink to this heading">#</a></h2>
<p>Functions for computing treewidth decomposition.</p>
<p>Treewidth of an undirected graph is a number associated with the graph.
It can be defined as the size of the largest vertex set (bag) in a tree
decomposition of the graph minus one.</p>
<p><a class="reference external" href="https://en.wikipedia.org/wiki/Treewidth">Wikipedia: Treewidth</a></p>
<p>The notions of treewidth and tree decomposition have gained their
attractiveness partly because many graph and network problems that are
intractable (e.g., NP-hard) on arbitrary graphs become efficiently
solvable (e.g., with a linear time algorithm) when the treewidth of the
input graphs is bounded by a constant <a class="reference internal" href="#rfd2b568a4a59-1" id="id2">[1]</a> <a class="reference internal" href="#rfd2b568a4a59-2" id="id3">[2]</a>.</p>
<p>There are two different functions for computing a tree decomposition:
<a class="reference internal" href="generated/networkx.algorithms.approximation.treewidth.treewidth_min_degree.html#networkx.algorithms.approximation.treewidth.treewidth_min_degree" title="networkx.algorithms.approximation.treewidth.treewidth_min_degree"><code class="xref py py-func docutils literal notranslate"><span class="pre">treewidth_min_degree()</span></code></a> and <a class="reference internal" href="generated/networkx.algorithms.approximation.treewidth.treewidth_min_fill_in.html#networkx.algorithms.approximation.treewidth.treewidth_min_fill_in" title="networkx.algorithms.approximation.treewidth.treewidth_min_fill_in"><code class="xref py py-func docutils literal notranslate"><span class="pre">treewidth_min_fill_in()</span></code></a>.</p>
<div role="list" class="citation-list">
<div class="citation" id="rfd2b568a4a59-1" role="doc-biblioentry">
<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">1</a><span class="fn-bracket">]</span></span>
<p>Hans L. Bodlaender and Arie M. C. A. Koster. 2010. “Treewidth
computations I.Upper bounds”. Inf. Comput. 208, 3 (March 2010),259-275.
<a class="reference external" href="http://dx.doi.org/10.1016/j.ic.2009.03.008">http://dx.doi.org/10.1016/j.ic.2009.03.008</a></p>
</div>
<div class="citation" id="rfd2b568a4a59-2" role="doc-biblioentry">
<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id3">2</a><span class="fn-bracket">]</span></span>
<p>Hans L. Bodlaender. “Discovering Treewidth”. Institute of Information
and Computing Sciences, Utrecht University.
Technical Report UU-CS-2005-018.
<a class="reference external" href="http://www.cs.uu.nl">http://www.cs.uu.nl</a></p>
</div>
<div class="citation" id="rfd2b568a4a59-3" role="doc-biblioentry">
<span class="label"><span class="fn-bracket">[</span>3<span class="fn-bracket">]</span></span>
<p>K. Wang, Z. Lu, and J. Hicks <em>Treewidth</em>.
<a class="reference external" href="https://web.archive.org/web/20210507025929/http://web.eecs.utk.edu/~cphill25/cs594_spring2015_projects/treewidth.pdf">https://web.archive.org/web/20210507025929/http://web.eecs.utk.edu/~cphill25/cs594_spring2015_projects/treewidth.pdf</a></p>
</div>
</div>
<table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/networkx.algorithms.approximation.treewidth.treewidth_min_degree.html#networkx.algorithms.approximation.treewidth.treewidth_min_degree" title="networkx.algorithms.approximation.treewidth.treewidth_min_degree"><code class="xref py py-obj docutils literal notranslate"><span class="pre">treewidth_min_degree</span></code></a>(G)</p></td>
<td><p>Returns a treewidth decomposition using the Minimum Degree heuristic.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/networkx.algorithms.approximation.treewidth.treewidth_min_fill_in.html#networkx.algorithms.approximation.treewidth.treewidth_min_fill_in" title="networkx.algorithms.approximation.treewidth.treewidth_min_fill_in"><code class="xref py py-obj docutils literal notranslate"><span class="pre">treewidth_min_fill_in</span></code></a>(G)</p></td>
<td><p>Returns a treewidth decomposition using the Minimum Fill-in heuristic.</p></td>
</tr>
</tbody>
</table>
</section>
<section id="module-networkx.algorithms.approximation.vertex_cover">
<span id="vertex-cover"></span><h2>Vertex Cover<a class="headerlink" href="#module-networkx.algorithms.approximation.vertex_cover" title="Permalink to this heading">#</a></h2>
<p>Functions for computing an approximate minimum weight vertex cover.</p>
<p>A <a class="reference external" href="https://en.wikipedia.org/wiki/Vertex_cover"><em>vertex cover</em></a> is a subset of nodes such that each edge in the graph
is incident to at least one node in the subset.</p>
<table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/networkx.algorithms.approximation.vertex_cover.min_weighted_vertex_cover.html#networkx.algorithms.approximation.vertex_cover.min_weighted_vertex_cover" title="networkx.algorithms.approximation.vertex_cover.min_weighted_vertex_cover"><code class="xref py py-obj docutils literal notranslate"><span class="pre">min_weighted_vertex_cover</span></code></a>(G[, weight])</p></td>
<td><p>Returns an approximate minimum weighted vertex cover.</p></td>
</tr>
</tbody>
</table>
</section>
<section id="module-networkx.algorithms.approximation.maxcut">
<span id="max-cut"></span><h2>Max Cut<a class="headerlink" href="#module-networkx.algorithms.approximation.maxcut" title="Permalink to this heading">#</a></h2>
<table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/networkx.algorithms.approximation.maxcut.randomized_partitioning.html#networkx.algorithms.approximation.maxcut.randomized_partitioning" title="networkx.algorithms.approximation.maxcut.randomized_partitioning"><code class="xref py py-obj docutils literal notranslate"><span class="pre">randomized_partitioning</span></code></a>(G[, seed, p, weight])</p></td>
<td><p>Compute a random partitioning of the graph nodes and its cut value.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/networkx.algorithms.approximation.maxcut.one_exchange.html#networkx.algorithms.approximation.maxcut.one_exchange" title="networkx.algorithms.approximation.maxcut.one_exchange"><code class="xref py py-obj docutils literal notranslate"><span class="pre">one_exchange</span></code></a>(G[, initial_cut, seed, weight])</p></td>
<td><p>Compute a partitioning of the graphs nodes and the corresponding cut value.</p></td>
</tr>
</tbody>
</table>
</section>
</section>
</article>
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