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Complex Network Community Detection Algorithm Based on Genetic Algorithm

In: The 19th International Conference on Industrial Engineering and Engineering Management

Author

Listed:
  • Yun Li

    (National University of Defense Technology)

  • Gang Liu

    (National University of Defense Technology)

  • Song-yang Lao

    (National University of Defense Technology)

Abstract

For the problem of complex network community detection, propose a new algorithm based on genetic algorithm to solve it. This algorithm sets network modularity function as target function and fitness function, uses matrix encoding to describe individuals, and generates initial population using nodes similarity. The crossover operation is based on the quality of individuals’ genes, in this process, all nodes that weren’t partitioned into any communities make up a new one together, and the nodes that were partitioned into more than one community are placed into the community to which most of their neighbors belong. The mutation operation is non-uniform, which splits the mutation gene into two new genes or fuses it into the others randomly. The experiment proved that this algorithm could effectively detect communities in complex networks.

Suggested Citation

  • Yun Li & Gang Liu & Song-yang Lao, 2013. "Complex Network Community Detection Algorithm Based on Genetic Algorithm," Springer Books, in: Ershi Qi & Jiang Shen & Runliang Dou (ed.), The 19th International Conference on Industrial Engineering and Engineering Management, edition 127, chapter 0, pages 257-267, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-37270-4_25
    DOI: 10.1007/978-3-642-37270-4_25
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    Cited by:

    1. Laassem, Brahim & Idarrou, Ali & Boujlaleb, Loubna & Iggane, M’bark, 2022. "Label propagation algorithm for community detection based on Coulomb’s law," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    2. Cvetković, Jovana & Cvetković, Milan, 2019. "On community detection in complex networks based on different training algorithms: A case study on prediction of depression of internet addiction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1161-1170.
    3. Mehmet Ali Balcı & Larissa M. Batrancea & Ömer Akgüller & Anca Nichita, 2022. "Coarse Graining on Financial Correlation Networks," Mathematics, MDPI, vol. 10(12), pages 1-16, June.

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