Where Defined

<boost/graph/distributed/betweenness_centrality.hpp>

also accessible from

<boost/graph/betweenness_centrality.hpp>

Parameters

IN: const Graph& g

The graph type must be a model of Distributed Graph. The graph type must also model the Incidence Graph concept. 0-weighted edges in g will result in undefined behavior.

IN: CentralityMap centrality

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.

IN: EdgeCentralityMap edge_centrality_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.

IN: IncomingMap incoming

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.

Default: An iterator_property_map created from a

std::vector of std::vector of the graph's vertex descriptor type.

IN: DistanceMap distance

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.

Default: An iterator_property_map created from a

std::vector of the value type of the CentralityMap.

IN: DependencyMap dependency

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.

Default: An iterator_property_map created from a

std::vector of the value type of the CentralityMap.

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.

IN: VertexIndexMap vertex_index

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)

IN: WeightMap weight_map

A model of Readable Property Map whose key type is the edge descriptor type of the graph g. If not supplied the betweenness centrality calculation will be unweighted.

IN: Buffer sources

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.

Complexity

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).

Algorithm Description

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.

Bibliography

Copyright (C) 2009 The Trustees of Indiana University.

Authors: Nick Edmonds and Andrew Lumsdaine