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<h1 class="title"><a class="reference external" href="http://www.osl.iu.edu/research/pbgl"><img align="middle" alt="Parallel BGL" class="align-middle" src="pbgl-logo.png" /></a> Sorted R-MAT generator</h1>

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<pre class="literal-block">
 template&lt;typename RandomGenerator, typename Graph,
          typename EdgePredicate = keep_all_edges&gt;
 class sorted_rmat_iterator
 {
 public:
   typedef std::input_iterator_tag iterator_category;
   typedef std::pair&lt;vertices_size_type, vertices_size_type&gt; value_type;
   typedef const value_type&amp; reference;
   typedef const value_type* pointer;
   typedef void difference_type;

   sorted_rmat_iterator();
   sorted_rmat_iterator(RandomGenerator&amp; gen, vertices_size_type n,
                        edges_size_type m, double a, double b, double c,
                        double d, bool permute_vertices = true);
   // Iterator operations
   reference operator*() const;
   pointer operator-&gt;() const;
   sorted_rmat_iterator&amp; operator++();
   sorted_rmat_iterator operator++(int);
   bool operator==(const sorted_rmat_iterator&amp; other) const;
   bool operator!=(const sorted_rmat_iterator&amp; other) const;
};
</pre>
<p>This class template implements a generator for R-MAT graphs <a class="citation-reference" href="#czf04" id="id1">[CZF04]</a>,
suitable for initializing an adjacency_list or other graph structure
with iterator-based initialization. An R-MAT graph has a scale-free
distribution w.r.t. vertex degree and is implemented using
Recursive-MATrix partitioning.  The output of this generator is sorted
for use with <a class="reference external" href="http://www.boost.org/libs/graph/doc/compressed_sparse_row.html">compressed sparse row graph</a>.</p>
<div class="section" id="where-defined">
<h1>Where Defined</h1>
<p>&lt;<tt class="docutils literal"><span class="pre">boost/graph/rmat_graph_generator.hpp</span></tt>&gt;</p>
</div>
<div class="section" id="constructors">
<h1>Constructors</h1>
<pre class="literal-block">
sorted_rmat_iterator();
</pre>
<p>Constructs a past-the-end iterator.</p>
<pre class="literal-block">
sorted_rmat_iterator(RandomGenerator&amp; gen, vertices_size_type n,
                     edges_size_type m, double a, double b, double c,
                     double d, bool permute_vertices = true,
                     EdgePredicate ep = keep_all_edges());
</pre>
<p>Constructs an R-MAT generator iterator that creates a graph with <tt class="docutils literal"><span class="pre">n</span></tt>
vertices and <tt class="docutils literal"><span class="pre">m</span></tt> edges.  <tt class="docutils literal"><span class="pre">a</span></tt>, <tt class="docutils literal"><span class="pre">b</span></tt>, <tt class="docutils literal"><span class="pre">c</span></tt>, and <tt class="docutils literal"><span class="pre">d</span></tt> represent
the probability that a generated edge is placed of each of the 4
quadrants of the partitioned adjacency matrix.  Probabilities are
drawn from the random number generator <tt class="docutils literal"><span class="pre">gen</span></tt>.  Vertex indices are
permuted to eliminate locality when <tt class="docutils literal"><span class="pre">permute_vertices</span></tt> is true.
<tt class="docutils literal"><span class="pre">ep</span></tt> allows the user to specify which edges are retained, this is
useful in the case where the user wishes to refrain from storing
remote edges locally during generation to reduce memory consumption.</p>
</div>
<div class="section" id="example">
<h1>Example</h1>
<pre class="literal-block">
#include &lt;boost/graph/compressed_sparse_row_graph.hpp&gt;
#include &lt;boost/graph/rmat_graph_generator.hpp&gt;
#include &lt;boost/random/linear_congruential.hpp&gt;

typedef boost::compressed_sparse_row_graph&lt;&gt; Graph;
typedef boost::sorted_rmat_iterator&lt;boost::minstd_rand, Graph&gt;
  RMATGen;

int main()
{
  boost::minstd_rand gen;
  // Create graph with 100 nodes and 400 edges
  Graph g(RMATGen(gen, 100, 400, 0.57, 0.19, 0.19, 0.05),
          RMATGen(), 100);
  return 0;
}
</pre>
</div>
<div class="section" id="bibliography">
<h1>Bibliography</h1>
<table class="docutils citation" frame="void" id="czf04" rules="none">
<colgroup><col class="label" /><col /></colgroup>
<tbody valign="top">
<tr><td class="label"><a class="fn-backref" href="#id1">[CZF04]</a></td><td>D Chakrabarti, Y Zhan, and C Faloutsos.  R-MAT: A Recursive
Model for Graph Mining. In Proceedings of 4th International Conference
on Data Mining, pages 442--446, 2004.</td></tr>
</tbody>
</table>
<hr class="docutils" />
<p>Copyright (C) 2009 The Trustees of Indiana University.</p>
<p>Authors: Nick Edmonds and Andrew Lumsdaine</p>
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Generated on: 2009-05-31 00:21 UTC.
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