A multi-objective genetic algorithm based on neighborhood coevolution for community detection

Community detection has attracted growing interest, with multi-objective evolutionary algorithms proving to be highly competitive in this area. In this paper, a community detection method based on a multi-objective neighborhood coevolution genetic algorithm, NCMOGA, is proposed. To improve the computational efficiency in large-scale networks, NCMOGA introduces a network processing strategy to simplify the network before and during evolution. A neighborhood coevolution strategy is proposed, in which the corresponding subpopulation is formed according to the neighborhood of each individual. A series of operations such as crossover, mutation and update are performed in the subpopulation, emphasizing the synergy between individuals and their neighbors. Mating selection and crossover operations are performed based on the center selection idea of density peak clustering, and the most important nodes are selected to generate offspring. The effectiveness of NCMOGA is verified on synthetic networks and real-world networks. In addition, the results in guiding the classification of disease and healthy samples demonstrate the high quality of the modules detected by NCMOGA.