Research on complex network community identification based on partially optimized genetic algorithm

Article type: Research Article

Authors: Chang, Hao

Affiliations: Department of Computer Engineering, Tai Yuan University, Taiyuan, Shanxi 030032, China | E-mail: chao2000_0_0@163.com

Correspondence: [*] Corresponding author: Department of Computer Engineering, Tai Yuan University, Taiyuan, Shanxi 030032, China. E-mail: chao2000_0_0@163.com.

Abstract: The research of complex networks has become a hot research field, and has been successfully applied in many fields such as social network analysis, protein network analysis, and link prediction. In this paper, the traditional genetic algorithm has strong randomness and weak search ability in the community identification method. A complex network community recognition algorithm based on local optimization genetic algorithm is proposed. The method uses label propagation strategy to produce a certain precision. The initial population; a combined one-way crossover strategy is proposed to avoid destroying the original excellent community structure in the cross process; the local heuristic mutation strategy is used to ensure the accuracy of the identification community, and the overall optimization ability of the algorithm is realized. Finally, the effectiveness of the algorithm is verified by experiments.

Keywords: Genetic algorithm, complex network, community identification

DOI: 10.3233/JCM-193649

Journal: Journal of Computational Methods in Sciences and Engineering, vol. 20, no. 1, pp. 81-89, 2020