Multiobjective Particle Swarm Optimization Based on Network Embedding for Complex Network Community Detection

Abstract

Community detection in complex networks is significant to social network analysis. Most of the algorithms take advantage of single-objective optimization methods, which may not be effective for complex networks. Compared with single-objective algorithms, multiobjective evolutionary algorithms can avoid local optimization. However, multiobjective evolutionary algorithms often encounter problems of excessive search space and low efficiency. To solve these issues, this study introduces network embedding into the multiobjective particle swarm algorithm and maps nodes into a low-latitude space, thereby effectively reducing the search space while increasing search efficiency via a consensus propagation strategy. Experimental results demonstrate that a novel effective algorithm based on multiobjective particle swarm optimization (NE-PSO) performs effectively and has competitive performance in comparison with state-of-the-art approaches on synthetic and real-world networks, especially the large-scale ones.