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A new genetic algorithm for community detection using matrix representation method

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  • Chen, Kaiqi
  • Bi, Weihong

Abstract

Community structures contain important information of social networks. In most applications, mining community structures would be helpful for people to analyze networks. Typically, genetic algorithm is an effective approach to detect communities. At present, there are two kinds of genetic encoding methods: SGR (Tasgin et al., 2008) and LAR (Pizzuti, 2008) and both have some shortages, which always lead to premature convergence. Based on this, we proposed a new matrix encoding method for community detection, which contains total information of community clustering. We also designed crossover and mutation operator. According to the experiments, our method performed effectively.

Suggested Citation

  • Chen, Kaiqi & Bi, Weihong, 2019. "A new genetic algorithm for community detection using matrix representation method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    • Handle: RePEc:eee:phsmap:v:535:y:2019:i:c:s0378437119313020
    DOI: 10.1016/j.physa.2019.122259
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    References listed on IDEAS

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    1. Shang, Ronghua & Bai, Jing & Jiao, Licheng & Jin, Chao, 2013. "Community detection based on modularity and an improved genetic algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(5), pages 1215-1231.
    2. Capocci, A. & Servedio, V.D.P. & Caldarelli, G. & Colaiori, F., 2005. "Detecting communities in large networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 352(2), pages 669-676.
    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.
    4. Shang, Ronghua & Luo, Shuang & Zhang, Weitong & Stolkin, Rustam & Jiao, Licheng, 2016. "A multiobjective evolutionary algorithm to find community structures based on affinity propagation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 453(C), pages 203-227.
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    Cited by:

    1. Karel Ječmen & Denisa Mocková & Dušan Teichmann, 2024. "Solving Transport Infrastructure Investment Project Selection and Scheduling Using Genetic Algorithms," Mathematics, MDPI, vol. 12(19), pages 1-28, September.

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