Abstract
The community detection problem is modeled as multi-objective optimization problem, and a classic NSGA-II (nondominated sorting genetic algorithm) is adopted to optimize this problem, which overcomes the resolution problem in the process of modularity density optimization and the parameter adjustment in the process of general modularity density optimization. In this case, a set of Pareto solutions with different partitioning results can be obtained in one time, which can be chosen by the decision maker. Besides that, the crossover and mutation operators take the neighborhood information of the vertices of networks into consideration, which matches up with the property of real world complex networks. The graph based on coding scheme confirms the self-adjustment of the community numbers, rather than sets up in advance. All the experiment results indicate that NSGA-II based algorithm can detect the construction of community effectively.
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