A Two-Stage Multi-Objective Evolutionary Algorithm for Community Detection in Complex Networks

Community detection is a crucial research direction in the analysis of complex networks and has been shown to be an NP-hard problem (a that at least as hard hardest problems nondeterministic polynomial time). Multi-objective evolutionary algorithms (MOEAs) have demonstrated promising performance community detection. Given distinct crossover operators are suitable for various stages algorithm evolution, we propose two-stage uses individual similarity parameter divide into two stages. We employ appropriate each stage achieve optimal performance. Additionally, repair operation applied boundary-independent nodes during second phase algorithm, resulting improved partitioning results. assessed effectiveness by measuring its on synthetic network four real-world datasets. Compared existing competing methods, our achieves better accuracy stability.