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Imbalance problem in community detection

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  • Sun, Peng Gang

Abstract

Community detection gives us a simple way to understand complex networks’ structures. However, there is an imbalance problem in community detection. This paper first introduces the imbalance problem and then proposes a new measure to alleviate the imbalance problem. In addition, we study two variants of the measure and further analyze the resolution scale of community detection. Finally, we compare our approach with some state of the art methods on random networks as well as real-world networks for community detection. Both the theoretical analysis and the experimental results show that our approach achieves better performance for community detection. We also find that our approach tends to separate densely connected subgroups preferentially.

Suggested Citation

  • Sun, Peng Gang, 2016. "Imbalance problem in community detection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 364-376.
    • Handle: RePEc:eee:phsmap:v:457:y:2016:i:c:p:364-376
    DOI: 10.1016/j.physa.2016.03.085
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    Cited by:

    1. 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.
    2. Sun, Peng Gang & Sun, Xiya, 2017. "Complete graph model for community detection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 88-97.

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    Keywords

    Community detection; Imbalance problem;

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