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Overlapping community detection using weighted consensus clustering

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Abstract

Many overlapping community detection algorithms have been proposed. Most of them are unstable and behave non-deterministically. In this paper, we use weighted consensus clustering for combining multiple base covers obtained by classic non-deterministic algorithms to improve the quality of the results. We first evaluate a reliability measure for each community in all base covers and assign a proportional weight to each one. Then we redefine the consensus matrix that takes into account not only the common membership of nodes, but also the reliability of the communities. Experimental results on both artificial and real-world networks show that our algorithm can find overlapping communities accurately.

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Acknowledgements

The authors would like to acknowledge the reviewers for their useful comments and advice. This work was financially supported by self-determined research funds of CCNU from the colleges basic research and operation of MOE (CCNUI5A05044) and the National Natural Science Foundation of Hubei province under Grant No. 2013CFB210.

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Authors and Affiliations

  1. College of Physical Science and Technology, Central China Normal University, Wuhan, 430079, China

    LINTAO YANG, ZETAI YU, JING QIAN & SHOUYIN LIU

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  1. LINTAO YANG
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  2. ZETAI YU
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  3. JING QIAN
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  4. SHOUYIN LIU
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Correspondence to LINTAO YANG.

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YANG, L., YU, Z., QIAN, J. et al. Overlapping community detection using weighted consensus clustering. Pramana - J Phys 87, 58 (2016). https://doi.org/10.1007/s12043-016-1270-2

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  • DOI: https://doi.org/10.1007/s12043-016-1270-2

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