AFSMA: An Enhanced Artificial Fish Swarm Algorithm Based on Mutuality for Community Detection

Community structure is an important property of complex networks. Detecting these communities is of great significance to a wide range of applications. Community detection is an NP-hard problem having received great attentions in recent years. Modularity Q is by far the most common and well-known fitness function for measuring the quality of network division. Many optimization algorithms have been developed for community detection. In this paper, we propose a new modularity optimization method based on the artificial fish swarm algorithm, namely AFSMA. In the algorithm, mutuality is defined to represent the distance and relationship between nodes. A series of artificial fish swarm's behaviors are used to simulate the change of nodes' community labels. Experimental results on datasets of multiple real-world networks have shown the viability and effectiveness of the proposed algorithm.

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