Community detection can be used to help mine the potential information in social networks, and uncovering community structures in social networks can be regarded as clustering optimization problems. In this paper, an overlapping community detection algorithm based on biogeography optimization is proposed. Firstly, the algorithm takes the method of label propagation based on local max degree and neighborhood overlap for initial network partitioning. The preliminary partition result used to construct initial population by cloning and mutating to accelerate the algorithm’s convergence. Next, to make biogeography optimization algorithm suitable for community detection, we design problem-specific migration rules and mutation operators based on a novel affinity degree to improve the effectiveness of the algorithm. Experiments on benchmark test data, including two synthetic networks and four real-world networks, show that the proposed algorithm can achieve results with better accuracy and stability than the compared evolutionary algorithms.
Community detection Local maximum degree node Neighborhood overlap Label propagation Affinity degree
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This work was supported by the Natural Science Foundation of Chongqing Education Commission (No. KJ1601214), and the Research Innovation Platform of Yangtze Normal University (No. 2015XJPT02).
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