Approximating Betweenness Centrality

10 years 5 months ago
Approximating Betweenness Centrality
Betweenness is a centrality measure based on shortest paths, widely used in complex network analysis. It is computationally-expensive to exactly determine betweenness; currently the fastest-known algorithm by Brandes requires O(nm) time for unweighted graphs and O(nm + n2 log n) time for weighted graphs, where n is the number of vertices and m is the number of edges in the network. These are also the worstcase time bounds for computing the betweenness score of a single vertex. In this paper, we present a novel approximation algorithm for computing betweenness centrality of a given vertex, for both weighted and unweighted graphs. Our approximation algorithm is based on an adaptive sampling technique that significantly reduces the number of single-source shortest path computations for vertices with high centrality. We conduct an extensive experimental study on real-world graph instances, and observe that our random sampling algorithm gives very good betweenness approximations for biolog...
David A. Bader, Shiva Kintali, Kamesh Madduri, Mil
Added 09 Jun 2010
Updated 09 Jun 2010
Type Conference
Year 2007
Where WAW
Authors David A. Bader, Shiva Kintali, Kamesh Madduri, Milena Mihail
Comments (0)