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Overlapping community detection based on conductance optimization in large-scale networks

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  • Gao, Yang
  • Zhang, Hongli
  • Zhang, Yue

Abstract

Community structure reveals useful information in domains of sociology, biology, physics and computer science. In this work, an overlapping community detection algorithm for large-scale networks based on local expansion is proposed, in which we present a novel seeding method. And we optimize conductance of communities by: (1) modifying inaccurate community affiliations by node movements; (2) combining densely overlapping communities with a novel combining function; (3) finding communities for the outliers with our proposed theorem. Experimental results in synthetic networks show that the optimization largely enhance the community accuracy. Experimental results in large real-world networks show that our approach is superior to the others in the state of the art.

Suggested Citation

  • Gao, Yang & Zhang, Hongli & Zhang, Yue, 2019. "Overlapping community detection based on conductance optimization in large-scale networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 522(C), pages 69-79.
    • Handle: RePEc:eee:phsmap:v:522:y:2019:i:c:p:69-79
    DOI: 10.1016/j.physa.2019.01.142
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    References listed on IDEAS

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    Cited by:

    1. Chagas, Guilherme Oliveira & Lorena, Luiz Antonio Nogueira & dos Santos, Rafael Duarte Coelho, 2022. "A hybrid heuristic for overlapping community detection through the conductance minimization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 592(C).
    2. Sahar Bakhtar & Hovhannes A. Harutyunyan, 2022. "A new metric to compare local community detection algorithms in social networks using geodesic distance," Journal of Combinatorial Optimization, Springer, vol. 44(4), pages 2809-2831, November.
    3. Manuel Guerrero & Consolación Gil & Francisco G. Montoya & Alfredo Alcayde & Raúl Baños, 2020. "Multi-Objective Evolutionary Algorithms to Find Community Structures in Large Networks," Mathematics, MDPI, vol. 8(11), pages 1-18, November.

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