Edit: /usr/include/boost/graph/bc_clustering.hpp (5937B)
// 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
#include
#include
#include
#include
#include
#include
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;
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