Research on complex network community discovery based on adaptive cross mutation operator

Abstract

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Community discovery in complex networks has become a core issue in multidisciplinary cross-disciplinary research and has been successfully applied in many areas, such as social network analysis, protein network analysis, and link prediction. This paper proposes a genetic algorithm based on adaptive cross mutation operator for complex network community mining. The population is generated by establishing fitness value calibration and adaptive cross mutation operator, and a good individual is selected from it. An improved adaptive cross mutation operator is proposed to ensure the convergence of genetic algorithm and accelerate the generation of optimal solution while maintaining the diversity of population. Finally, experiments were carried out in multiple real networks to verify the stability and efficiency of the algorithm.