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<title>Parallel BGL Scalable R-MAT generator</title>
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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> Scalable R-MAT generator</h1>

<!-- Copyright (C) 2004-2009 The Trustees of Indiana University.
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<pre class="literal-block">
 template&lt;typename ProcessGroup, typename Distribution,
          typename RandomGenerator, typename Graph&gt;
 class scalable_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;

   scalable_rmat_iterator();
   scalable_rmat_iterator(ProcessGroup pg, Distribution distrib,
                          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;
   scalable_rmat_iterator&amp; operator++();
   scalable_rmat_iterator operator++(int);
   bool operator==(const scalable_rmat_iterator&amp; other) const;
   bool operator!=(const scalable_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.</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">
scalable_rmat_iterator();
</pre>
<p>Constructs a past-the-end iterator.</p>
<pre class="literal-block">
scalable_rmat_iterator(ProcessGroup pg, Distribution distrib,
                       RandomGenerator&amp; gen, vertices_size_type n,
                       edges_size_type m, double a, double b, double c,
                       double d, bool permute_vertices = true);
</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.  Inside the <tt class="docutils literal"><span class="pre">scalable_rmat_iterator</span></tt>
processes communicate using <tt class="docutils literal"><span class="pre">pg</span></tt> to generate their local edges as
defined by <tt class="docutils literal"><span class="pre">distrib</span></tt>.  <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.</p>
</div>
<div class="section" id="example">
<h1>Example</h1>
<pre class="literal-block">
#include &lt;boost/graph/distributed/mpi_process_group.hpp&gt;
#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;

using boost::graph::distributed::mpi_process_group;

typedef compressed_sparse_row_graph&lt;directedS, no_property, no_property, no_property,
                                    distributedS&lt;mpi_process_group&gt; &gt; Graph;
typedef boost::scalable_rmat_iterator&lt;boost::minstd_rand, Graph&gt; RMATGen;

int main()
{
  boost::minstd_rand gen;
  mpi_process_group pg;

  int N = 100;

  boost::parallel::variant_distribution&lt;ProcessGroup&gt; distrib
    = boost::parallel::block(pg, N);

  // Create graph with 100 nodes and 400 edges
  Graph g(RMATGen(pg, distrib, gen, N, 400, 0.57, 0.19, 0.19, 0.05),
          RMATGen(), N, pg, distrib);
  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, Brian Barrett, and Andrew Lumsdaine</p>
</div>
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Generated on: 2009-05-31 00:21 UTC.
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