A New Active Learning Semi-supervised Community Detection Algorithm in Complex Networks

Community detection is an important method for analyzing community structure of complex networks. Semi-supervised community detection algorithms use prior information to improve the performance of community detection. But how to obtain these prior information is a challenging problem. In this paper, we present a new active learning semi-supervised community detection algorithm based on node relevance and similarities measures. Firstly, it calculates similarity for each neighbored nodes. Then it using our combine rule and node relevance measure to select informative nodes in active learning step. Secondly, it expands community using these selected nodes with our hierarchical expanding rule in semi-supervised strategy. Finally, the algorithm is demonstrated with four real-world networks and a artificial network benchmark. Extensive experiments show that it effectively improves community detection results and has lower computational complexity compared with some community detection algorithms.

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