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A novel method for overlapping community detection using Multi-objective optimization

Author

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  • Ebrahimi, Morteza
  • Shahmoradi, Mohammad Reza
  • Heshmati, Zainabolhoda
  • Salehi, Mostafa

Abstract

The problem of community detection as one of the most important applications of network science can be addressed effectively by multi-objective optimization. In this paper, we aim to present a novel efficient method based on this approach. Also, in this study the idea of using all Pareto fronts to detect overlapping communities is introduced.

Suggested Citation

  • Ebrahimi, Morteza & Shahmoradi, Mohammad Reza & Heshmati, Zainabolhoda & Salehi, Mostafa, 2018. "A novel method for overlapping community detection using Multi-objective optimization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 825-835.
    • Handle: RePEc:eee:phsmap:v:505:y:2018:i:c:p:825-835
    DOI: 10.1016/j.physa.2018.03.033
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    References listed on IDEAS

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    1. Gong, Maoguo & Ma, Lijia & Zhang, Qingfu & Jiao, Licheng, 2012. "Community detection in networks by using multiobjective evolutionary algorithm with decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(15), pages 4050-4060.
    2. M. Gligor & M. Ausloos, 2008. "Clusters in weighted macroeconomic networks: the EU case. Introducing the overlapping index of GDP/capita fluctuation correlations," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 63(4), pages 533-539, June.
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    Cited by:

    1. Liu, Saisai & Xia, Zhengyou, 2020. "A two-stage BFS local community detection algorithm based on node transfer similarity and Local Clustering Coefficient," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).

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