We propose an algorithm for the detection of communities in networks. The algorithm exploits degree and clustering coefficient of vertices as these metrics characterize dense connections, which, we hypothesize, are indicative of communities. Each vertex, independently, seeks the community to which it belongs by visiting its neighbour vertices and choosing its peers on the basis of their degrees and clustering coefficients. The algorithm is intrinsically data parallel. We devise a version for Graphics Processing Unit (GPU). We empirically evaluate the performance of our method. We measure and compare its efficiency and effectiveness to several state of the art community detection algorithms. Effectiveness is quantified by five metrics, namely, modularity, conductance, internal density, cut ratio and weighted community clustering. Efficiency is measured by the running time. Clearly the opportunity to parallelize our algorithm yields an efficient solution to the community detection problem.


Graphic Processing Unit Community Detection Common Neighbour Graph Cluster Community Detection Algorithm 
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 2013

Authors and Affiliations

  • Yi Song
    • 1
  • Stéphane Bressan
    • 1
  1. 1.School of ComputingNational University of SingaporeSingapore

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