Abstract
Community detection in complex network has been an active research area in data mining and machine learning. This paper proposed a community detection method based on multi-objective evolutionary algorithm, named CDMOEA, which tries to find the Pareto front by maximize two objectives, community score and community fitness. Fast and Elitist Multi-objective Genetic Algorithm is used to attained a set of optimal solutions, and then use Modularity function to choose the best one from them. The locus based adjacency representation is used to realize genetic representation, which ensures the effective connections of the nodes in the network during the process of population Initialization and other genetic operator. Uniform crossover is introduced to ensure population’s diversity. We compared it with some popular community detection algorithms in computer generated network and real world networks. Experiment results show that it is more efficient in community detection.
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