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Multi-Objective Evolutionary Algorithms to Find Community Structures in Large Networks

Author

Listed:
  • Manuel Guerrero

    (CeiA3, Department of Informatics, University of Almería, Carretera de Sacramento s/n, 04120 Almería, Spain)

  • Consolación Gil

    (CeiA3, Department of Informatics, University of Almería, Carretera de Sacramento s/n, 04120 Almería, Spain)

  • Francisco G. Montoya

    (CeiA3, Department of Engineering, University of Almería, Carretera de Sacramento s/n, 04120 Almería, Spain)

  • Alfredo Alcayde

    (CeiA3, Department of Engineering, University of Almería, Carretera de Sacramento s/n, 04120 Almería, Spain)

  • Raúl Baños

    (CeiA3, Department of Engineering, University of Almería, Carretera de Sacramento s/n, 04120 Almería, Spain)

Abstract

Real-world complex systems are often modeled by networks such that the elements are represented by vertices and their interactions are represented by edges. An important characteristic of these networks is that they contain clusters of vertices densely linked amongst themselves and more sparsely connected to nodes outside the cluster. Community detection in networks has become an emerging area of investigation in recent years, but most papers aim to solve single-objective formulations, often focused on optimizing structural metrics, including the modularity measure. However, several studies have highlighted that considering modularity as a unique objective often involves resolution limit and imbalance inconveniences. This paper opens a new avenue of research in the study of multi-objective variants of the classical community detection problem by applying multi-objective evolutionary algorithms that simultaneously optimize different objectives. In particular, they analyzed two multi-objective variants involving not only modularity but also the conductance metric and the imbalance in the number of nodes of the communities. With this aim, a new Pareto-based multi-objective evolutionary algorithm is presented that includes advanced initialization strategies and search operators. The results obtained when solving large-scale networks representing real-life power systems show the good performance of these methods and demonstrate that it is possible to obtain a balanced number of nodes in the clusters formed while also having high modularity and conductance values.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:11:p:2048-:d:446364
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    References listed on IDEAS

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    Cited by:

    1. Weihua Qian & Hang Xu & Houjin Chen & Lvqing Yang & Yuanguo Lin & Rui Xu & Mulan Yang & Minghong Liao, 2024. "A Synergistic MOEA Algorithm with GANs for Complex Data Analysis," Mathematics, MDPI, vol. 12(2), pages 1-30, January.
    2. Bo Zhang & Yifei Mi & Lele Zhang & Yuping Zhang & Maozhen Li & Qianqian Zhai & Meizi Li, 2022. "Dynamic Community Detection Method of a Social Network Based on Node Embedding Representation," Mathematics, MDPI, vol. 10(24), pages 1-22, December.

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