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.

Authors: Nick Edmonds, Brian Barrett, and Andrew Lumsdaine