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Refinement of the community detection performance by weighted relationship coupling

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Abstract

The complexity of many community detection algorithms is usually an exponential function with the scale which hard to uncover community structure with high speed. Inspired by the ideas of the famous modularity optimization, in this paper, we proposed a proper weighting scheme utilizing a novel k-strength relationship which naturally represents the coupling distance between two nodes. Community structure detection using a generalized weighted modularity measure is refined based on the weighted k-strength matrix. We apply our algorithm on both the famous benchmark network and the real networks. Theoretical analysis and experiments show that the weighted algorithm can uncover communities fast and accurately and can be easily extended to large-scale real networks.

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Acknowledgements

The authors are grateful for the detailed reviews and constructive comments of the reviewers, which have greatly improved the quality of this paper. The research was supported in part by MOE (Ministry of Education in China), Liberal Arts and Social Sciences Foundation Grant No. 12YJA870013, NSFC grants 71561025, 71401194, 91324203 and Ph.D. research foundation of Xinjiang University of Finance and Economics Grant No. 2015BS004.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Xinjiang University of Finance and Economics, Urumqi, Xinjiang, 830001, China

    DONG MIN & KAI YU

  2. School of Management Science and Engineering, Central University of Finance and Economics, Beijing, 100080, China

    HUI-JIA LI

Authors
  1. DONG MIN
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  2. KAI YU
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  3. HUI-JIA LI
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Correspondence to KAI YU.

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MIN, D., YU, K. & LI, HJ. Refinement of the community detection performance by weighted relationship coupling. Pramana - J Phys 88, 44 (2017). https://doi.org/10.1007/s12043-016-1343-2

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  • DOI: https://doi.org/10.1007/s12043-016-1343-2

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