# Sorted R-MAT generator

     template<typename RandomGenerator, typename Graph,
              typename EdgePredicate = keep_all_edges>
     class sorted_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;

       sorted_rmat_iterator();
       sorted_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;
       sorted_rmat_iterator& operator++();
       sorted_rmat_iterator operator++(int);
       bool operator==(const sorted_rmat_iterator& other) const;
       bool operator!=(const sorted_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. The output of this generator is sorted for use with compressed sparse row graph.

# Where Defined

<`boost/graph/rmat_graph_generator.hpp`>

# Constructors

    sorted_rmat_iterator();

Constructs a past-the-end iterator.

    sorted_rmat_iterator(RandomGenerator& gen, vertices_size_type n,
                         edges_size_type m, double a, double b, double c,
                         double d, bool permute_vertices = true,
                         EdgePredicate ep = keep_all_edges());

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. `ep` allows the user to specify which edges are retained, this is useful in the case where the user wishes to refrain from storing remote edges locally during generation to reduce memory consumption.

# Example

    #include <boost/graph/compressed_sparse_row_graph.hpp>
    #include <boost/graph/rmat_graph_generator.hpp>
    #include <boost/random/linear_congruential.hpp>

    typedef boost::compressed_sparse_row_graph<> Graph;
    typedef boost::sorted_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;
    }

# 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 and Andrew Lumsdaine