// 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