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Differential Betweenness in Complex Networks Clustering

  • Alberto Ochoa
  • Leticia Arco
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5197)

Abstract

We propose a novel metric for measuring the degree of edge centrality in complex networks clustering, a task commonly called community detection in the analysis of social, biological and information networks. The metric, which has been called differential betweenness, has some unexpected and interesting properties that might help us to create better clustering algorithms. We compare our measure with the shortest path edge betweenness of Girvan and Newman and found that it can be more accurate and robust without requiring the costly recalculation step the other measure needs.

Keywords

graph clustering betweenness centrality complex networks 

References

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Alberto Ochoa
    • 1
  • Leticia Arco
    • 2
  1. 1.Institute of CyberneticsMathematics and PhysicsCuba
  2. 2.Central University of Las VillasCuba

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