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Inverse modelling-based multi-objective evolutionary algorithm with decomposition for community detection in complex networks

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  • Zou, Feng
  • Chen, Debao
  • Huang, De-Shuang
  • Lu, Renquan
  • Wang, Xude

Abstract

Community structure is an important topological property of complex networks representing real-world systems, and it is believed to be a highly important tool for understanding how complex networks are organized and function. Generally, community detection can be considered to be a single-objective or multi-objective optimization problem, and a great number of population-based optimization algorithms have been explored to address this problem in the past several decades. In this study, we present a novel discrete inverse modelling-based multi-objective evolutionary algorithm with decomposition (DIM-MOEA/D) for community detection in complex networks. First, the population is initialized by a problem-specific method based on label propagation. Next, inverse models based on the network topology are constructed to generate offspring by sampling the objective space, and the problem-specific mutation is introduced to maintain the diversity of the population and avoid being trapped in the local optima. Next, the decomposition-based selection is introduced as the updating rule of individuals. Finally, several real-world networks are considered to evaluate the performance of the proposed algorithm. The experimental results demonstrate that compared with the state-of-the-art approaches, DIM-MOEA/D is an effective and promising method for solving community detection in complex networks.

Suggested Citation

  • Zou, Feng & Chen, Debao & Huang, De-Shuang & Lu, Renquan & Wang, Xude, 2019. "Inverse modelling-based multi-objective evolutionary algorithm with decomposition for community detection in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 662-674.
  • Handle: RePEc:eee:phsmap:v:513:y:2019:i:c:p:662-674
    DOI: 10.1016/j.physa.2018.08.077
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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. Zhou, Xu & Liu, Yanheng & Li, Bin & Sun, Geng, 2015. "Multiobjective biogeography based optimization algorithm with decomposition for community detection in dynamic networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 430-442.
    3. Li, Zhangtao & Liu, Jing, 2016. "A multi-agent genetic algorithm for community detection in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 449(C), pages 336-347.
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    Cited by:

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    2. 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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