Community Detection in Multiplex Networks Based on Evolutionary Multitask Optimization and Evolutionary Clustering Ensemble

Community detection in multiplex networks is an emerging research topic in the field of network science. Existing methods usually ignore the similarities among component layers of a multiplex network when detecting its community structures, which decreases the detection efficiency. In this article, we decompose the community detection in multiplex networks into two problems and propose a novel algorithm that can detect both the specific community partition for each component layer (layer-level community structure) and the composite community structure shared by all layers. First, by specifying the modularity optimization on a network layer as an optimization task, we model the layer-level community detection as a multitask optimization (MTO) problem and employ an evolutionary MTO algorithm to solve it. In this way, the topology correlations among different layers can be utilized to facilitate the community detection. Second, we propose an evolutionary clustering ensemble method to find the composite community structure based on the layer-level community partitions and the multiplex network. The proposed method is tested on both synthetic and real-world benchmark networks and compared with classical and state-of-the-art algorithms. Experimental results show that the proposed algorithm has superior community detection performances on multiplex networks.

Automated summary

This article proposes a novel algorithm for community detection in multiplex networks. The algorithm decomposes the problem into two parts, detecting specific community partitions for each component layer and finding the composite community structure shared by all layers. Experimental results demonstrate that the algorithm outperforms classical and state-of-the-art algorithms in community detection on multiplex networks.