...one of the most highly
regarded and expertly designed C++ library projects in the
world.
— Herb Sutter and Andrei
Alexandrescu, C++
Coding Standards
template<typename RandomGenerator, typename Graph> class 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; rmat_iterator(); rmat_iterator(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; rmat_iterator& operator++(); rmat_iterator operator++(int); bool operator==(const rmat_iterator& other) const; bool operator!=(const 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.
<boost/graph/rmat_graph_generator.hpp>
rmat_iterator();
Constructs a past-the-end iterator.
rmat_iterator(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. 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.
#include <boost/graph/adjacency_list.hpp> #include <boost/graph/rmat_graph_generator.hpp> #include <boost/random/linear_congruential.hpp> typedef boost::adjacency_list<> Graph; typedef boost::rmat_iterator<boost::minstd_rand, Graph> 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; }
[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 and Andrew Lumsdaine