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<div class="section" id="blockmodel">
<h1>blockmodel<a class="headerlink" href="#blockmodel" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="networkx.algorithms.block.blockmodel">
<tt class="descname">blockmodel</tt><big>(</big><em>G</em>, <em>partitions</em>, <em>multigraph=False</em><big>)</big><a class="reference internal" href="../../_modules/networkx/algorithms/block.html#blockmodel"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#networkx.algorithms.block.blockmodel" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a reduced graph constructed using the generalized block modeling
technique.</p>
<p>The blockmodel technique collapses nodes into blocks based on a
given partitioning of the node set. Each partition of nodes
(block) is represented as a single node in the reduced graph.</p>
<p>Edges between nodes in the block graph are added according to the
edges in the original graph. If the parameter multigraph is False
(the default) a single edge is added with a weight equal to the
sum of the edge weights between nodes in the original graph
The default is a weight of 1 if weights are not specified. If the
parameter multigraph is True then multiple edges are added each
with the edge data from the original graph.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters :</th><td class="field-body"><p class="first"><strong>G</strong> : graph</p>
<blockquote>
<div><p>A networkx Graph or DiGraph</p>
</div></blockquote>
<p><strong>partitions</strong> : list of lists, or list of sets</p>
<blockquote>
<div><p>The partition of the nodes. Must be non-overlapping.</p>
</div></blockquote>
<p><strong>multigraph</strong> : bool, optional</p>
<blockquote>
<div><p>If True return a MultiGraph with the edge data of the original
graph applied to each corresponding edge in the new graph.
If False return a Graph with the sum of the edge weights, or a
count of the edges if the original graph is unweighted.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns :</th><td class="field-body"><p class="first last"><strong>blockmodel</strong> : a Networkx graph object</p>
</td>
</tr>
</tbody>
</table>
<p class="rubric">References</p>
<table class="docutils citation" frame="void" id="r152" rules="none">
<colgroup><col class="label" /><col /></colgroup>
<tbody valign="top">
<tr><td class="label"><a class="fn-backref" href="#id1">[R152]</a></td><td>Patrick Doreian, Vladimir Batagelj, and Anuska Ferligoj
“Generalized Blockmodeling”,Cambridge University Press, 2004.</td></tr>
</tbody>
</table>
<p class="rubric">Examples</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="n">G</span><span class="o">=</span><span class="n">nx</span><span class="o">.</span><span class="n">path_graph</span><span class="p">(</span><span class="mi">6</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">partition</span><span class="o">=</span><span class="p">[[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">],[</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">],[</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">]]</span>
<span class="gp">>>> </span><span class="n">M</span><span class="o">=</span><span class="n">nx</span><span class="o">.</span><span class="n">blockmodel</span><span class="p">(</span><span class="n">G</span><span class="p">,</span><span class="n">partition</span><span class="p">)</span>
</pre></div>
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