Research on network Community Detection (CD) has predominantly focused on identifying communities of densely connected nodes in undirected networks. Community structure is an integral part of a social network. Detecting such communities plays a vital role in a wide range of applications, including but not limited to cluster analysis, recommendation systems, and understanding the behavior of complex systems. Researchers have derived many algorithms from discovering the community structures of networks. Finding communities is a challenging task, and there is no single algorithm that produces the best results for all networks. Therefore, despite many elegant solutions, learning communities remain active research areas.CD is a challenging optimization problem that consists in searching for communities that belong to a network or graph under the assumption that the nodes of the same community share properties that enable the detection of new characteristics or functional relationships in the network. Many methods have been proposed to address this problem in many research fields, such as power systems, biology, sociology, and physics. Many of those optimization methods use modularity to identify the optimal network subdivision. This paper proposes a new CD approach based on Symbiotic Organisms Search (SOS) and Lévy Flight (LF). The LF distribution is used to prevent the stagnation of solutions in local minima. Extensive experiments compare the SOS-LF with other state-of-the-art algorithms on real-world social networks. Experimental results show that the SOS-LF is effective and stable.
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