An enhanced particle swarm optimization based on Physarum model for community detection

Community detection, an effective tool to analyze and understand network data, has been paid more and more attention in recent years. One of the most popular methods of detecting community structure is to find the division with the maximal modularity. However, the modularity maximization is an NP-complete problem. In the field of swarm intelligence algorithm, particle swarm optimization (PSO) has been widely used to solve such NP-complete problem. Nevertheless, premature convergence and lower accuracy limit its performance in community detection. In order to overcome these shortcomings, this paper proposes a novel PSO called P-PSO for community detection through combining the computational ability of Physarum, a kind of slime. The proposed algorithm improves the efficiency of PSO by recognizing inter-community edges based on Physarum-inspired network model (PNM). Experiments in eight networks show that the proposed algorithm is effective and promising for community detection, compared with other algorithms.