Balancing topology structure and node attribute in evolutionary multi-objective community detection for attributed networks

Abstract The task of community detection in attributed networks is to find a good community partition in terms of both topology structure and node attribute. Despite that a number of algorithms have been suggested for community detection in attributed networks, most of them suffer from considerable performance deterioration when the community structure is not clear or the attributes of nodes in one community are not homogeneous. In this paper, we suggest a dual-population-based multi-objective evolutionary algorithm, called DP-MOEA, for balancing topology structure and node attribute in community detection of attributed networks. In DP-MOEA, one population takes charge of community detection according to the topology structure, whereas the other population is responsible for community detection based on the node attribute information. The two populations evolve independently by different genetic operations and interact with each other at every certain number of generations to utilize the good individuals obtained in the other population. Moreover, a node attribute similarity-based local search strategy and a community merging strategy are designed in the procedure of population interaction to enable the generation of high-quality individuals. Experimental results on synthetic and real-world attributed networks demonstrate the superiority of the proposed DP-MOEA over eight state-of-art evolutionary algorithms for community detection in attributed networks, especially when the community structure is unclear or the node attributes in one community are not homogeneous.