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.. Copyright (C) 2004-2009 The Trustees of Indiana University.
Use, modification and distribution is subject to the Boost Software
License, Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at
http://www.boost.org/LICENSE_1_0.txt)
===================================
|Logo| Scalable R-MAT generator
===================================
::
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;
};
This class template implements a generator for R-MAT graphs [CZF04]_,
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.
Where Defined
-------------
<``boost/graph/rmat_graph_generator.hpp``>
Constructors
------------
::
scalable_rmat_iterator();
Constructs a past-the-end 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);
Constructs an R-MAT generator iterator that creates a graph with ``n``
vertices and ``m`` edges. Inside the ``scalable_rmat_iterator``
processes communicate using ``pg`` to generate their local edges as
defined by ``distrib``. ``a``, ``b``, ``c``, and ``d`` 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 ``gen``. Vertex indices are permuted to
eliminate locality when ``permute_vertices`` is true.
Example
-------
::
#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;
}
Bibliography
------------
.. [CZF04] 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.
-----------------------------------------------------------------------------
Copyright (C) 2009 The Trustees of Indiana University.
Authors: Nick Edmonds, Brian Barrett, and Andrew Lumsdaine
.. |Logo| image:: pbgl-logo.png
:align: middle
:alt: Parallel BGL
:target: http://www.osl.iu.edu/research/pbgl
.. _Sequential connected components: http://www.boost.org/libs/graph/doc/connected_components.html
|
