Abstract
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 + n 2logn) 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 worst-case 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 biological networks, road networks and web crawls.
David A. Bader
Distinguished Professor, Associate Dean for Research, and Director of the Institute for Data Science
David A. Bader is a Distinguished Professor in the Department of Data Science and Associate Dean for Research in the Ying Wu College of Computing at New Jersey Institute of Technology.
