--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/ossrv_pub/boost_apis/boost/graph/bc_clustering.hpp Tue Feb 02 02:01:42 2010 +0200
@@ -0,0 +1,164 @@
+// 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/pending/indirect_cmp.hpp>
+#include <algorithm>
+#include <vector>
+#include <boost/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 (edges(g).first == edges(g).second) 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));
+ edge_descriptor e = *max_element(edges(g).first, edges(g).second, cmp);
+ is_done = done(get(edge_centrality, e), e, g);
+ if (!is_done) remove_edge(e, g);
+ } while (!is_done && edges(g).first != edges(g).second);
+}
+
+/**
+ * \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