| // Copyright 2004 The Trustees of Indiana University. |
| |
| // Distributed under 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) |
| |
| // Authors: Douglas Gregor |
| // Andrew Lumsdaine |
| #ifndef BOOST_GRAPH_BETWEENNESS_CENTRALITY_CLUSTERING_HPP |
| #define BOOST_GRAPH_BETWEENNESS_CENTRALITY_CLUSTERING_HPP |
| |
| #include <boost/graph/betweenness_centrality.hpp> |
| #include <boost/graph/graph_traits.hpp> |
| #include <boost/graph/graph_utility.hpp> |
| #include <boost/pending/indirect_cmp.hpp> |
| #include <algorithm> |
| #include <vector> |
| #include <boost/property_map/property_map.hpp> |
| |
| namespace boost { |
| |
| /** Threshold termination function for the betweenness centrality |
| * clustering algorithm. |
| */ |
| template<typename T> |
| struct bc_clustering_threshold |
| { |
| typedef T centrality_type; |
| |
| /// Terminate clustering when maximum absolute edge centrality is |
| /// below the given threshold. |
| explicit bc_clustering_threshold(T threshold) |
| : threshold(threshold), dividend(1.0) {} |
| |
| /** |
| * Terminate clustering when the maximum edge centrality is below |
| * the given threshold. |
| * |
| * @param threshold the threshold value |
| * |
| * @param g the graph on which the threshold will be calculated |
| * |
| * @param normalize when true, the threshold is compared against the |
| * normalized edge centrality based on the input graph; otherwise, |
| * the threshold is compared against the absolute edge centrality. |
| */ |
| template<typename Graph> |
| bc_clustering_threshold(T threshold, const Graph& g, bool normalize = true) |
| : threshold(threshold), dividend(1.0) |
| { |
| if (normalize) { |
| typename graph_traits<Graph>::vertices_size_type n = num_vertices(g); |
| dividend = T((n - 1) * (n - 2)) / T(2); |
| } |
| } |
| |
| /** Returns true when the given maximum edge centrality (potentially |
| * normalized) falls below the threshold. |
| */ |
| template<typename Graph, typename Edge> |
| bool operator()(T max_centrality, Edge, const Graph&) |
| { |
| return (max_centrality / dividend) < threshold; |
| } |
| |
| protected: |
| T threshold; |
| T dividend; |
| }; |
| |
| /** Graph clustering based on edge betweenness centrality. |
| * |
| * This algorithm implements graph clustering based on edge |
| * betweenness centrality. It is an iterative algorithm, where in each |
| * step it compute the edge betweenness centrality (via @ref |
| * brandes_betweenness_centrality) and removes the edge with the |
| * maximum betweenness centrality. The @p done function object |
| * determines when the algorithm terminates (the edge found when the |
| * algorithm terminates will not be removed). |
| * |
| * @param g The graph on which clustering will be performed. The type |
| * of this parameter (@c MutableGraph) must be a model of the |
| * VertexListGraph, IncidenceGraph, EdgeListGraph, and Mutable Graph |
| * concepts. |
| * |
| * @param done The function object that indicates termination of the |
| * algorithm. It must be a ternary function object thats accepts the |
| * maximum centrality, the descriptor of the edge that will be |
| * removed, and the graph @p g. |
| * |
| * @param edge_centrality (UTIL/OUT) The property map that will store |
| * the betweenness centrality for each edge. When the algorithm |
| * terminates, it will contain the edge centralities for the |
| * graph. The type of this property map must model the |
| * ReadWritePropertyMap concept. Defaults to an @c |
| * iterator_property_map whose value type is |
| * @c Done::centrality_type and using @c get(edge_index, g) for the |
| * index map. |
| * |
| * @param vertex_index (IN) The property map that maps vertices to |
| * indices in the range @c [0, num_vertices(g)). This type of this |
| * property map must model the ReadablePropertyMap concept and its |
| * value type must be an integral type. Defaults to |
| * @c get(vertex_index, g). |
| */ |
| template<typename MutableGraph, typename Done, typename EdgeCentralityMap, |
| typename VertexIndexMap> |
| void |
| betweenness_centrality_clustering(MutableGraph& g, Done done, |
| EdgeCentralityMap edge_centrality, |
| VertexIndexMap vertex_index) |
| { |
| typedef typename property_traits<EdgeCentralityMap>::value_type |
| centrality_type; |
| typedef typename graph_traits<MutableGraph>::edge_iterator edge_iterator; |
| typedef typename graph_traits<MutableGraph>::edge_descriptor edge_descriptor; |
| typedef typename graph_traits<MutableGraph>::vertices_size_type |
| vertices_size_type; |
| |
| if (has_no_edges(g)) return; |
| |
| // Function object that compares the centrality of edges |
| indirect_cmp<EdgeCentralityMap, std::less<centrality_type> > |
| cmp(edge_centrality); |
| |
| bool is_done; |
| do { |
| brandes_betweenness_centrality(g, |
| edge_centrality_map(edge_centrality) |
| .vertex_index_map(vertex_index)); |
| std::pair<edge_iterator, edge_iterator> edges_iters = edges(g); |
| edge_descriptor e = *max_element(edges_iters.first, edges_iters.second, cmp); |
| is_done = done(get(edge_centrality, e), e, g); |
| if (!is_done) remove_edge(e, g); |
| } while (!is_done && !has_no_edges(g)); |
| } |
| |
| /** |
| * \overload |
| */ |
| template<typename MutableGraph, typename Done, typename EdgeCentralityMap> |
| void |
| betweenness_centrality_clustering(MutableGraph& g, Done done, |
| EdgeCentralityMap edge_centrality) |
| { |
| betweenness_centrality_clustering(g, done, edge_centrality, |
| get(vertex_index, g)); |
| } |
| |
| /** |
| * \overload |
| */ |
| template<typename MutableGraph, typename Done> |
| void |
| betweenness_centrality_clustering(MutableGraph& g, Done done) |
| { |
| typedef typename Done::centrality_type centrality_type; |
| std::vector<centrality_type> edge_centrality(num_edges(g)); |
| betweenness_centrality_clustering(g, done, |
| make_iterator_property_map(edge_centrality.begin(), get(edge_index, g)), |
| get(vertex_index, g)); |
| } |
| |
| } // end namespace boost |
| |
| #endif // BOOST_GRAPH_BETWEENNESS_CENTRALITY_CLUSTERING_HPP |