A Markov chain-based overlapping community detection algorithm for complex networks

Most community detection algorithms for complex networks are focused on nonoverlapping communities. However, there are many overlapping communities in real-world complex networks. To solve the contradiction, this paper develops a novel overlapping community detection algorithm based on Markov chain. First, the input adjacency matrix was expanded to guide the information flow. Then, the inflation operation was implemented to enhance the weakening boundary of communities. After that, an adaptive threshold was introduced to reconstruct the matrix. The network corresponding to the reconstructed matrix displays the overlapping communities in the original network. The proposed algorithm was compared with several popular community detection algorithms on artificial and real-world networks. The results show that our algorithm achieved higher recognition accuracy and faster convergence than the contrastive algorithms. © 2019 International Information and Engineering Technology Association. All rights reserved.