ceph/src/boost/boost/graph/bc_clustering.hpp

   1 // Copyright 2004 The Trustees of Indiana University.
   2
   3 // Distributed under the Boost Software License, Version 1.0.
   4 // (See accompanying file LICENSE_1_0.txt or copy at
   5 // http://www.boost.org/LICENSE_1_0.txt)
   6
   7 //  Authors: Douglas Gregor
   8 //           Andrew Lumsdaine
   9 #ifndef BOOST_GRAPH_BETWEENNESS_CENTRALITY_CLUSTERING_HPP
  10 #define BOOST_GRAPH_BETWEENNESS_CENTRALITY_CLUSTERING_HPP
  11
  12 #include <boost/graph/betweenness_centrality.hpp>
  13 #include <boost/graph/graph_traits.hpp>
  14 #include <boost/graph/graph_utility.hpp>
  15 #include <boost/pending/indirect_cmp.hpp>
  16 #include <algorithm>
  17 #include <vector>
  18 #include <boost/property_map/property_map.hpp>
  19
  20 namespace boost {
  21
  22 /** Threshold termination function for the betweenness centrality
  23  * clustering algorithm.
  24  */
  25 template<typename T>
  26 struct bc_clustering_threshold
  27 {
  28   typedef T centrality_type;
  29
  30   /// Terminate clustering when maximum absolute edge centrality is
  31   /// below the given threshold.
  32   explicit bc_clustering_threshold(T threshold)
  33     : threshold(threshold), dividend(1.0) {}
  34
  35   /**
  36    * Terminate clustering when the maximum edge centrality is below
  37    * the given threshold.
  38    *
  39    * @param threshold the threshold value
  40    *
  41    * @param g the graph on which the threshold will be calculated
  42    *
  43    * @param normalize when true, the threshold is compared against the
  44    * normalized edge centrality based on the input graph; otherwise,
  45    * the threshold is compared against the absolute edge centrality.
  46    */
  47   template<typename Graph>
  48   bc_clustering_threshold(T threshold, const Graph& g, bool normalize = true)
  49     : threshold(threshold), dividend(1.0)
  50   {
  51     if (normalize) {
  52       typename graph_traits<Graph>::vertices_size_type n = num_vertices(g);
  53       dividend = T((n - 1) * (n - 2)) / T(2);
  54     }
  55   }
  56
  57   /** Returns true when the given maximum edge centrality (potentially
  58    * normalized) falls below the threshold.
  59    */
  60   template<typename Graph, typename Edge>
  61   bool operator()(T max_centrality, Edge, const Graph&)
  62   {
  63     return (max_centrality / dividend) < threshold;
  64   }
  65
  66  protected:
  67   T threshold;
  68   T dividend;
  69 };
  70
  71 /** Graph clustering based on edge betweenness centrality.
  72  *
  73  * This algorithm implements graph clustering based on edge
  74  * betweenness centrality. It is an iterative algorithm, where in each
  75  * step it compute the edge betweenness centrality (via @ref
  76  * brandes_betweenness_centrality) and removes the edge with the
  77  * maximum betweenness centrality. The @p done function object
  78  * determines when the algorithm terminates (the edge found when the
  79  * algorithm terminates will not be removed).
  80  *
  81  * @param g The graph on which clustering will be performed. The type
  82  * of this parameter (@c MutableGraph) must be a model of the
  83  * VertexListGraph, IncidenceGraph, EdgeListGraph, and Mutable Graph
  84  * concepts.
  85  *
  86  * @param done The function object that indicates termination of the
  87  * algorithm. It must be a ternary function object thats accepts the
  88  * maximum centrality, the descriptor of the edge that will be
  89  * removed, and the graph @p g.
  90  *
  91  * @param edge_centrality (UTIL/OUT) The property map that will store
  92  * the betweenness centrality for each edge. When the algorithm
  93  * terminates, it will contain the edge centralities for the
  94  * graph. The type of this property map must model the
  95  * ReadWritePropertyMap concept. Defaults to an @c
  96  * iterator_property_map whose value type is
  97  * @c Done::centrality_type and using @c get(edge_index, g) for the
  98  * index map.
  99  *
 100  * @param vertex_index (IN) The property map that maps vertices to
 101  * indices in the range @c [0, num_vertices(g)). This type of this
 102  * property map must model the ReadablePropertyMap concept and its
 103  * value type must be an integral type. Defaults to
 104  * @c get(vertex_index, g).
 105  */
 106 template<typename MutableGraph, typename Done, typename EdgeCentralityMap,
 107          typename VertexIndexMap>
 108 void
 109 betweenness_centrality_clustering(MutableGraph& g, Done done,
 110                                   EdgeCentralityMap edge_centrality,
 111                                   VertexIndexMap vertex_index)
 112 {
 113   typedef typename property_traits<EdgeCentralityMap>::value_type
 114     centrality_type;
 115   typedef typename graph_traits<MutableGraph>::edge_iterator edge_iterator;
 116   typedef typename graph_traits<MutableGraph>::edge_descriptor edge_descriptor;
 117
 118   if (has_no_edges(g)) return;
 119
 120   // Function object that compares the centrality of edges
 121   indirect_cmp<EdgeCentralityMap, std::less<centrality_type> >
 122     cmp(edge_centrality);
 123
 124   bool is_done;
 125   do {
 126     brandes_betweenness_centrality(g,
 127                                    edge_centrality_map(edge_centrality)
 128                                    .vertex_index_map(vertex_index));
 129     std::pair<edge_iterator, edge_iterator> edges_iters = edges(g);
 130     edge_descriptor e = *max_element(edges_iters.first, edges_iters.second, cmp);
 131     is_done = done(get(edge_centrality, e), e, g);
 132     if (!is_done) remove_edge(e, g);
 133   } while (!is_done && !has_no_edges(g));
 134 }
 135
 136 /**
 137  * \overload
 138  */
 139 template<typename MutableGraph, typename Done, typename EdgeCentralityMap>
 140 void
 141 betweenness_centrality_clustering(MutableGraph& g, Done done,
 142                                   EdgeCentralityMap edge_centrality)
 143 {
 144   betweenness_centrality_clustering(g, done, edge_centrality,
 145                                     get(vertex_index, g));
 146 }
 147
 148 /**
 149  * \overload
 150  */
 151 template<typename MutableGraph, typename Done>
 152 void
 153 betweenness_centrality_clustering(MutableGraph& g, Done done)
 154 {
 155   typedef typename Done::centrality_type centrality_type;
 156   std::vector<centrality_type> edge_centrality(num_edges(g));
 157   betweenness_centrality_clustering(g, done,
 158     make_iterator_property_map(edge_centrality.begin(), get(edge_index, g)),
 159     get(vertex_index, g));
 160 }
 161
 162 } // end namespace boost
 163
 164 #endif // BOOST_GRAPH_BETWEENNESS_CENTRALITY_CLUSTERING_HPP