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The community detection problem in social networks has been broadly explored from different aspects. In this problem, it is attempted to divide a network into a group-set of similar nodes (communities), in which nodes inside a community structure reflect more similar functional characteristics or than that of other nodes outside the community. However, a typical characteristic of some real networks is that the presence of overlapping communities, where nodes can be a member of quite one community at an equivalent time. In this paper, an algorithm for overlapping community detection is proposed using cellular learning automata (CLA-OCD). In the CLA-OCD, a group of learning automata collaborates to find overlapping communities. The performance of the CLA-OCD is evaluated by several experiments, which is the design of both synthetic and real networks. Through experiments, the CLA-OCD achieves significant improvement in performance measures, including modularity, F-score, and normalized mutual information (NMI). |
