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A hybrid heuristic for overlapping community detection through the conductance minimization

Author

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  • Chagas, Guilherme Oliveira
  • Lorena, Luiz Antonio Nogueira
  • dos Santos, Rafael Duarte Coelho

Abstract

Community structures, which are sets of elements that share some relationship between themselves, can be found in several real-world networks. Many of these communities, also known as clusters, can share elements, i.e., they may overlap. Identifying such overlapping clusters is usually a harder task than finding non-overlapping ones and, therefore, it needs more sophisticated methods. In this work we proposed a hybrid heuristic for detecting overlapping clusters in networks. An overlapping clustering is generated through the solving of a mixed-integer linear program using, as input, a heterogeneous set of good-quality clusters. This set is produced by two state-of-the-art overlapping community detection algorithms. In addition, some local search methods for conductance minimization are used to improve the quality of the clustering generate by our hybrid heuristic. Test results in artificial and real-world graphs show that our approach is able to detect overlapping clusters with better overall conductance than methods in the state of the art.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:phsmap:v:592:y:2022:i:c:s0378437122000231
    DOI: 10.1016/j.physa.2022.126887
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    References listed on IDEAS

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    Cited by:

    1. Chen, Chunchun & Zhu, Wenjie & Peng, Bo, 2022. "Differentiated graph regularized non-negative matrix factorization for semi-supervised community detection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).

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