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// named parameter versions template<typename Graph, typename Param, typename Tag, typename Rest> void brandes_betweenness_centrality(const Graph& g, const bgl_named_params<Param,Tag,Rest>& params); template<typename Graph, typename CentralityMap> void brandes_betweenness_centrality(const Graph& g, CentralityMap centrality); template<typename Graph, typename CentralityMap, typename EdgeCentralityMap> void brandes_betweenness_centrality(const Graph& g, CentralityMap centrality, EdgeCentralityMap edge_centrality_map); // non-named parameter versions template<typename Graph, typename CentralityMap, typename EdgeCentralityMap, typename IncomingMap, typename DistanceMap, typename DependencyMap, typename PathCountMap, typename VertexIndexMap, typename Buffer> void brandes_betweenness_centrality(const Graph& g, CentralityMap centrality, EdgeCentralityMap edge_centrality_map, IncomingMap incoming, DistanceMap distance, DependencyMap dependency, PathCountMap path_count, VertexIndexMap vertex_index, Buffer sources, typename property_traits<DistanceMap>::value_type delta); template<typename Graph, typename CentralityMap, typename EdgeCentralityMap, typename IncomingMap, typename DistanceMap, typename DependencyMap, typename PathCountMap, typename VertexIndexMap, typename WeightMap, typename Buffer> void brandes_betweenness_centrality(const Graph& g, CentralityMap centrality, EdgeCentralityMap edge_centrality_map, IncomingMap incoming, DistanceMap distance, DependencyMap dependency, PathCountMap path_count, VertexIndexMap vertex_index, Buffer sources, typename property_traits<WeightMap>::value_type delta, WeightMap weight_map); // helper functions template<typename Graph, typename CentralityMap> typename property_traits<CentralityMap>::value_type central_point_dominance(const Graph& g, CentralityMap centrality);
The brandes_betweenness_centrality() function computes the betweenness centrality of the vertices and edges in a graph. The method of calculating betweenness centrality in O(V) space is due to Brandes [Brandes01]. The algorithm itself is a modification of Brandes algorithm by Edmonds [Edmonds09].
<boost/graph/distributed/betweenness_centrality.hpp>
also accessible from
<boost/graph/betweenness_centrality.hpp>
A centrality map may be supplied to the algorithm, if not supplied a dummy_property_map will be used and no vertex centrality information will be recorded. The CentralityMap type must be a Distributed Property Map. The key type must be the graph's vertex descriptor type.
Default: A dummy_property_map.
An edge centrality map may be supplied to the algorithm, if not supplied a dummy_property_map will be used and no edge centrality information will be recorded. The EdgeCentralityMap type must be a Distributed Property Map. The key type must be the graph's vertex descriptor type.
Default: A dummy_property_map.
The incoming map contains the incoming edges to a vertex that are part of shortest paths to that vertex. The IncomingMap type must be a Distributed Property Map. Its key type and value type must both be the graph's vertex descriptor type.
The distance map records the distance to vertices during the shortest paths portion of the algorithm. The DistanceMap type must be a Distributed Property Map. Its key type must be the graph's vertex descriptor type.
The dependency map records the dependency of each vertex during the centrality calculation portion of the algorithm. The DependencyMap type must be a Distributed Property Map. Its key type must be the graph's vertex descriptor type.
IN: PathCountMap path_count
The path count map records the number of shortest paths each vertex is on during the centrality calculation portion of the algorithm. The PathCountMap type must be a Distributed Property Map. Its key type must be the graph's vertex descriptor type.
- Default: An iterator_property_map created from a
- std::vector of the graph's degree size type.
A model of Readable Property Map whose key type is the vertex descriptor type of the graph g and whose value type is an integral type. The property map should map from vertices to their (local) indices in the range [0, num_vertices(g)).
Default: get(vertex_index, g)
A model of Buffer containing the starting vertices for the algorithm. If sources is empty a complete betweenness centrality calculation using all vertices in g will be performed. The value type of the Buffer must be the graph's vertex descriptor type.
Default: An empty boost::queue of int.
Computing the shortest paths, counting them, and computing the contribution to the centrality map is O(V log V). Calculating exact betweenness centrality requires counting the shortest paths from all vertices in g, thus exact betweenness centrality is O(V^2 log V).
For the vertices in sources (or all vertices in g when sources is empty) the algorithm first calls a customized implementation of delta_stepping_shortest_paths which maintains a shortest path tree using an IncomingMap. The IncomingMap contains the source of all incoming edges on shortest paths.
The IncomingMap defines the shortest path DAG at the target of the edges in the shortest paths tree. In the bidirectional case edge flags could be used to translate the shortest paths information to the source of the edges. Setting edge flags during the shortest path computation rather than using an IncomingMap would result in adding an O(V) factor to the inner loop of the shortest paths computation to account for having to clear edge flags when a new shortest path is found. This would increase the complexity of the algorithm. Asymptotically, the current implementation is better, however using edge flags in the bidirectional case would reduce the number of supersteps required by the depth of the shortest paths DAG for each vertex. Currently an outgoing map is explicitly constructed by simply reversing the edges in the incoming map. Once the outgoing map is constructed it is traversed in dependency order from the source of the shortest paths calculation in order to compute path counts. Once path counts are computed the shortest paths DAG is again traversed in dependency order from the source to calculate the dependency and centrality of each vertex.
The algorithm is complete when the centrality has been computed from all vertices in g.
[Brandes01] | Ulrik Brandes. A Faster Algorithm for Betweenness Centrality. In the Journal of Mathematical Sociology, volume 25 number 2, pages 163--177, 2001. |
[Edmonds09] | Nick Edmonds, Torsten Hoefler, and Andrew Lumsdaine. A Space-Efficient Parallel Algorithm for Computing Betweenness Centrality in Sparse Networks. Indiana University tech report. 2009. |
Copyright (C) 2009 The Trustees of Indiana University.
Authors: Nick Edmonds and Andrew Lumsdaine