Approximating Betweenness Centrality

  • David A. Bader
  • Shiva Kintali
  • Kamesh Madduri
  • Milena Mihail
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4863)


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.


Approximation Algorithm Road Network Betweenness Centrality Weighted Graph Transitive Closure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • David A. Bader
    • 1
  • Shiva Kintali
    • 1
  • Kamesh Madduri
    • 1
  • Milena Mihail
    • 1
  1. 1.College of Computing, Georgia Institute of Technology 

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