An improved multiobjective evolutionary approach for community detection in multilayer networks

The detection of shared community structure in multilayer network is an interesting and important issue that has attracted many researches. Traditional methods for community detection of single layer networks are not suitable for that of multilayer networks. In a previous work, the authors modeled the community discovery problem in multilayer network as a multiobjective one and devised a genetic algorithm to carry out it. In this paper, based on their model, we propose an improved multiobjective evolutionary approach MOEA-MultiNet for community detection in multilayer networks. The proposed MOEA-MultiNet is based on the framework of NSGA-II which employs the string-based representation scheme and synthesizes the genetic operation and local search to perform individual refinement. Experimental results on two real-world networks both demonstrate the ability and efficiency of the proposed MOEA-MultiNet in detecting community structure in multilayer networks.