Multi-objective population-based incremental learning for community structure detection

Abstract

The community structure detection of complex networks has become a hot topic in the past several years. In this paper, a new discrete framework of population-based incremental learning for complex networks problem is proposed. Based on the proposed discrete framework, a novel multi-objective population-based incremental learning algorithm is proposed to solve community structure detection problem. The proposed algorithm combines population-based incremental learning with the multi-objective evolutionary algorithm based on decomposition, this makes the evolution get directionality and converge fast. In order to discourage premature convergence, a random perturbation operator is adopted. The proposed algorithm has two contradictory objective functions termed as negative ratio association and ratio cut, respectively. The community structure detection results are a set of tradeoff solutions by simultaneous optimizing these two contradictory objective functions. Each of these solutions corresponds to a network community structure at one hierarchical level. Experiments on both real-world and synthetic networks prove the effectiveness of the proposed algorithm.