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<title>Function betweenness_centrality_clustering</title>
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<h1><img src="figs/python.gif" alt="(Python)"/><span class="refentrytitle">Function
betweenness_centrality_clustering</span></h1>
<p>boost::betweenness_centrality_clustering &mdash; Graph
clustering based on edge betweenness centrality.</p>
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<h2 xmlns:rev=
"http://www.cs.rpi.edu/~gregod/boost/tools/doc/revision" class=
"refsynopsisdiv-title">Synopsis</h2>
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"http://www.cs.rpi.edu/~gregod/boost/tools/doc/revision" class=
"refsynopsisdiv">
<pre class="synopsis">
<span class="bold"><b>template</b></span>&lt;<span class=
"bold"><b>typename</b></span> MutableGraph, <span class=
"bold"><b>typename</b></span> Done, <span class=
"bold"><b>typename</b></span> EdgeCentralityMap, 
         <span class=
"bold"><b>typename</b></span> VertexIndexMap&gt; 
  <span class="type"><span class=
"bold"><b>void</b></span></span> betweenness_centrality_clustering(MutableGraph &amp; g, Done done, 
                                         EdgeCentralityMap edge_centrality, 
                                         VertexIndexMap vertex_index);
<span class="bold"><b>template</b></span>&lt;<span class=
"bold"><b>typename</b></span> MutableGraph, <span class=
"bold"><b>typename</b></span> Done, <span class=
"bold"><b>typename</b></span> EdgeCentralityMap&gt; 
  <span class="type"><span class=
"bold"><b>void</b></span></span> betweenness_centrality_clustering(MutableGraph &amp; g, Done done, 
                                         EdgeCentralityMap edge_centrality);
<span class="bold"><b>template</b></span>&lt;<span class=
"bold"><b>typename</b></span> MutableGraph, <span class=
"bold"><b>typename</b></span> Done&gt; 
  <span class="type"><span class=
"bold"><b>void</b></span></span> betweenness_centrality_clustering(MutableGraph &amp; g, Done done);
</pre></div>
<div class="refsect1" lang="en"><a name="id822306" id=
"id822306"></a>
<h2>Description</h2>
<p>This algorithm implements graph clustering based on edge
betweenness centrality. It is an iterative algorithm, where in each
step it computes the edge betweenness centrality (via <a href=
"betweenness_centrality.html">brandes_betweenness_centrality</a>) and
removes the edge with the maximum betweenness centrality. The
<tt class="computeroutput">done</tt> function object determines
when the algorithm terminates (the edge found when the algorithm
terminates will not be removed).</p>

<h2>Parameters</h2>
IN: <tt>const Graph&amp; g</tt>
<blockquote>
  The graph object on which the algorithm will be applied.  The type
  <tt>Graph</tt> must be a model of <a
  href="VertexListGraph.html">Vertex List Graph</a> and <a
  href="IncidenceGraph.html">Incidence Graph</a>. When an edge
  centrality map is supplied, it must also model <a
  href="EdgeListGraph.html">Edge List Graph</a> and <a
  href="MutableGraph.html">MutableGraph</a>.<br>

<b>Python</b>: The parameter is named <tt>graph</tt>.
</blockquote>

IN: <tt>Done done</tt>
<blockquote>
The function object that indicates termination of the algorithm.
It must be a ternary function object thats accepts the maximum
centrality, the descriptor of the edge that will be removed, and
the graph <tt class="computeroutput">g</tt>.<br>
<b>Python</b>: Any callable Python object will suffice.
</blockquote>

OUT/UTIL: <tt>EdgeCentralityMap edge_centrality_map</tt>
<blockquote>
  This property map is used to accumulate the betweenness centrality
  of each edge, and is a secondary form of output for the
  algorithm. The type <tt>EdgeCentralityMap</tt> must be a model of <a
  href="../../property_map/doc/ReadWritePropertyMap.html">Read/Write
  Property Map</a>, with the graph's edge descriptor type as its key
  type. The value type of this property map should be the same as the
  value type of the <tt>CentralityMap</tt> property map.<br>

  <b>Default:</b> a <tt>dummy_property_map</tt>, which requires no
  work to compute and returns no answer.<br>
  <b>Python</b>: The color map must be a <tt>edge_double_map</tt> for
  the graph.<br>
  <b>Python default</b>: <tt>graph.get_edge_double_map("centrality")</tt>
</blockquote>

IN: <tt>VertexIndexMap vertex_index</tt> 
<blockquote>
  This maps each vertex to an integer in the range <tt>[0,
    num_vertices(g))</tt>. This is necessary for efficient updates of the
  heap data structure when an edge is relaxed.  The type
  <tt>VertexIndexMap</tt> must be a model of
  <a href="../../property_map/doc/ReadablePropertyMap.html">Readable Property Map</a>. The value type of the map must be an
  integer type. The vertex descriptor type of the graph needs to be
  usable as the key type of the map.<br>
  <b>Default:</b> <tt>get(vertex_index, g)</tt>.
    Note: if you use this default, make sure your graph has
    an internal <tt>vertex_index</tt> property. For example,
    <tt>adjacency_list</tt> with <tt>VertexList=listS</tt> does
    not have an internal <tt>vertex_index</tt> property.<br>
  <b>Python</b>: Unsupported parameter.
</blockquote>

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<h3>Where Defined</h3>
&lt;<a href=
"../../../boost/graph/bc_clustering.hpp">boost/graph/bc_clustering.hpp</a>&gt;
<hr>
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<tr valign="top">
<td nowrap>Copyright &copy; 2004</td>
<td><a href="http://www.boost.org/people/doug_gregor.html">Douglas Gregor</a>,
Indiana University (dgregor@cs.indiana.edu)<br>
<a href="https://homes.cs.washington.edu/~al75">Andrew Lumsdaine</a>, Indiana
University (<a href=
"mailto:lums@osl.iu.edu">lums@osl.iu.edu</a>)</td>
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