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<li><a class="reference internal" href="#">Community detection for NetworkX’s documentation</a></li>
<li><a class="reference internal" href="#example">Example :</a><ul>
<li><a class="reference internal" href="#as-a-command-line-utility">As a command line utility :</a></li>
<li><a class="reference internal" href="#as-python-module">As python module :</a></li>
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<li><a class="reference internal" href="#changelog">Changelog :</a></li>
<li><a class="reference internal" href="#license">License :</a></li>
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<div class="section" id="community-detection-for-networkx-s-documentation">
<h1>Community detection for NetworkX’s documentation<a class="headerlink" href="#community-detection-for-networkx-s-documentation" title="Permalink to this headline">¶</a></h1>
<p>This module implements community detection.</p>
<p>It uses the louvain method described in
Fast unfolding of communities in large networks,
Vincent D Blondel, Jean-Loup Guillaume, Renaud Lambiotte, Renaud Lefebvre,
Journal of Statistical Mechanics: Theory and Experiment 2008(10), P10008 (12pp)</p>
<p>It depends on Networkx to handle graph operations :
<a class="reference external" href="http://networkx.lanl.gov/">http://networkx.lanl.gov/</a></p>
<p>The program can be found in a repository where you can also report bugs :</p>
<a href="https://github.com/taynaud/python-louvain">https://github.com/taynaud/python-louvain</a></div>
<div class="section" id="example">
<h1>Example :<a class="headerlink" href="#example" title="Permalink to this headline">¶</a></h1>
<div class="section" id="as-a-command-line-utility">
<h2>As a command line utility :<a class="headerlink" href="#as-a-command-line-utility" title="Permalink to this headline">¶</a></h2>
<p>You should consider using the cpp version at
<a class="reference external" href="http://findcommunities.googlepages.com/">http://findcommunities.googlepages.com/</a> !</p>
<p>./community.py file.bin > tree</p>
<p>where file.bin is a binary graph as generated by the convert utility of the cpp
version.</p>
<p>The generated file can then be used with the hierarchy utility of the cpp version.
Note that the program does not make many verifications about the arguments,
and is expecting a friendly use.</p>
</div>
<div class="section" id="as-python-module">
<h2>As python module :<a class="headerlink" href="#as-python-module" title="Permalink to this headline">¶</a></h2>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">community</span> <span class="k">as</span> <span class="nn">community_louvain</span>
<span class="kn">import</span> <span class="nn">matplotlib.cm</span> <span class="k">as</span> <span class="nn">cm</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">networkx</span> <span class="k">as</span> <span class="nn">nx</span>
<span class="c1"># load the karate club graph</span>
<span class="n">G</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">karate_club_graph</span><span class="p">()</span>
<span class="c1">#first compute the best partition</span>
<span class="n">partition</span> <span class="o">=</span> <span class="n">community_louvain</span><span class="o">.</span><span class="n">best_partition</span><span class="p">(</span><span class="n">G</span><span class="p">)</span>
<span class="c1"># compute the best partition</span>
<span class="n">partition</span> <span class="o">=</span> <span class="n">community_louvain</span><span class="o">.</span><span class="n">best_partition</span><span class="p">(</span><span class="n">G</span><span class="p">)</span>
<span class="c1"># draw the graph</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">G</span><span class="p">)</span>
<span class="c1"># color the nodes according to their partition</span>
<span class="n">cmap</span> <span class="o">=</span> <span class="n">cm</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="s1">'viridis'</span><span class="p">,</span> <span class="nb">max</span><span class="p">(</span><span class="n">partition</span><span class="o">.</span><span class="n">values</span><span class="p">())</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">nx</span><span class="o">.</span><span class="n">draw_networkx_nodes</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">pos</span><span class="p">,</span> <span class="n">partition</span><span class="o">.</span><span class="n">keys</span><span class="p">(),</span> <span class="n">node_size</span><span class="o">=</span><span class="mi">40</span><span class="p">,</span>
<span class="n">cmap</span><span class="o">=</span><span class="n">cmap</span><span class="p">,</span> <span class="n">node_color</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="n">partition</span><span class="o">.</span><span class="n">values</span><span class="p">()))</span>
<span class="n">nx</span><span class="o">.</span><span class="n">draw_networkx_edges</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">pos</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="section" id="changelog">
<h1>Changelog :<a class="headerlink" href="#changelog" title="Permalink to this headline">¶</a></h1>
<ul class="simple">
<li><p>2020-12-27 : 0.15, Stop relabelling stable partitions, tests on power, doc fixes</p></li>
<li><p>2020-04-06 : 0.14, bugfixes (on resolution parameter), optimization on random state</p></li>
<li><p>2018-12-21 : 0.13, better random state, some files missing included, communities always in 0..N-1</p></li>
<li><p>2018-05-22 : 0.11, stop forcing networkx<2.0 and expose module __version__</p></li>
<li><p>2018-01-02 : 0.10, bug fix: taking into account the node removal cost</p></li>
<li><p>2017-09-21 : 0.9, support networkx 2.0</p></li>
<li><p>2017-06-03 : 0.8, add randomization and bugfixes</p></li>
<li><p>2017-05-21 : 0.7, migrate to github, readthedocs and travis. Add resolution parameter to control community size, bugfixes</p></li>
<li><p>04/21/2011 : modifications to use networkx like documentation and use of test.</p></li>
<li><p>02/22/2011 : correction of a bug regarding edge weights</p></li>
<li><p>01/14/2010 : modification to use networkx 1.01 graph api and adding the possibility to start the algorithm with a given partition</p></li>
<li><p>04/10/2009 : increase of the speed of the detection by caching node degrees</p></li>
</ul>
</div>
<div class="section" id="license">
<h1>License :<a class="headerlink" href="#license" title="Permalink to this headline">¶</a></h1>
Copyright (c) 2009, Thomas Aynaud <<a href="mailto:thomas.aynaud@lip6.fr">thomas.aynaud@lip6.fr</a>><br>
All rights reserved.<br><br>
Redistribution and use in source and binary forms, with or without modification,
are permitted provided that the following conditions are met:<br><br>
* Redistributions of source code must retain the above copyright notice,
this list of conditions and the following disclaimer.<br><br>
* Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.<br><br>
* Neither the name of the python-louvain Developers nor the names of its
contributors may be used to endorse or promote products derived from this
software without specific prior written permission.<br><br><br>
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.</div>
<div class="section" id="indices-and-tables">
<h1>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Permalink to this headline">¶</a></h1>
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