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Fast Community Detection

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Database and Expert Systems Applications (DEXA 2013)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8055))

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Abstract

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.

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Author information

Authors and Affiliations

  1. School of Computing, National University of Singapore, Singapore

    Yi Song & Stéphane Bressan

Authors
  1. Yi Song
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  2. Stéphane Bressan
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Editor information

Editors and Affiliations

  1. Instituto Tecnológico de Informática, Valencia, Spain

    Hendrik Decker

  2. Faculty of Electrical Engineering, Department of Cybernetics, Czech Technical University in Prague, 166 27, Prague 6, Czech Republic

    Lenka Lhotská

  3. Department of Computer Science, The University of Auckland, 1010, Auckland, New Zealand

    Sebastian Link

  4. Department of Information Technologies, University of Economics, Winston Churchill Square 4, 130 67, Prague 3, Czech Republic

    Josef Basl

  5. Institute of Software Technology, Vienna University of Technology, Favoritenstraße 9-11 / 188, 1040, Vienna, Austria

    A Min Tjoa

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Song, Y., Bressan, S. (2013). Fast Community Detection. In: Decker, H., Lhotská, L., Link, S., Basl, J., Tjoa, A.M. (eds) Database and Expert Systems Applications. DEXA 2013. Lecture Notes in Computer Science, vol 8055. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40285-2_35

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  • DOI: https://doi.org/10.1007/978-3-642-40285-2_35

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40284-5

  • Online ISBN: 978-3-642-40285-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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