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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>
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<pre class="literal-block">
template<typename ProcessGroup, typename Distribution,
typename RandomGenerator, typename Graph>
class scalable_rmat_iterator
{
public:
typedef std::input_iterator_tag iterator_category;
typedef std::pair<vertices_size_type, vertices_size_type> value_type;
typedef const value_type& reference;
typedef const value_type* pointer;
typedef void difference_type;
scalable_rmat_iterator();
scalable_rmat_iterator(ProcessGroup pg, Distribution distrib,
RandomGenerator& 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->() const;
scalable_rmat_iterator& operator++();
scalable_rmat_iterator operator++(int);
bool operator==(const scalable_rmat_iterator& other) const;
bool operator!=(const scalable_rmat_iterator& 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><<tt class="docutils literal"><span class="pre">boost/graph/rmat_graph_generator.hpp</span></tt>></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& 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 <boost/graph/distributed/mpi_process_group.hpp>
#include <boost/graph/compressed_sparse_row_graph.hpp>
#include <boost/graph/rmat_graph_generator.hpp>
#include <boost/random/linear_congruential.hpp>
using boost::graph::distributed::mpi_process_group;
typedef compressed_sparse_row_graph<directedS, no_property, no_property, no_property,
distributedS<mpi_process_group> > Graph;
typedef boost::scalable_rmat_iterator<boost::minstd_rand, Graph> RMATGen;
int main()
{
boost::minstd_rand gen;
mpi_process_group pg;
int N = 100;
boost::parallel::variant_distribution<ProcessGroup> 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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