Journal of Computer Science and Technology ›› 2020, Vol. 35 ›› Issue (2): 320-337.doi: 10.1007/s11390-020-9957-8

• Special Section on Learning and Mining in Dynamic Environments • Previous Articles     Next Articles

Finding Communities by Decomposing and Embedding Heterogeneous Information Network

Yue Kou, Member, CCF, De-Rong Shen, Senior Member, CCF, Dong Li*, Tie-Zheng Nie, Member, CCF, Ge Yu, Senior Member, CCF, Member, ACM, IEEE        

  1. School of Computer Science and Engineering, Northeastern University, Shenyang 110004, China
  • Received:2019-08-20 Revised:2020-01-03 Online:2020-03-05 Published:2020-03-18
  • Contact: Dong Li
  • About author:Yue Kou is an associate professor at School of Computer Science and Engineering, Northeastern University, Shenyang. She received her Ph.D. degree in computer software and theory from Northeastern University, Shenyang, in 2009. She is a member of CCF. Her main research interests include Web data management and social networks analysis.
  • Supported by:
    The work was supported by the National Key Research and Development Program of China under Grant No. 2018YFB1003404 and the National Natural Science Foundation of China under Grant Nos. 61672142, U1435216 and 61602103.

Community discovery is an important task in social network analysis. However, most existing methods for community discovery rely on the topological structure alone. These methods ignore the rich information available in the content data. In order to solve this issue, in this paper, we present a community discovery method based on heterogeneous information network decomposition and embedding. Unlike traditional methods, our method takes into account topology, node content and edge content, which can supply abundant evidence for community discovery. First, an embedding-based similarity evaluation method is proposed, which decomposes the heterogeneous information network into several subnetworks, and extracts their potential deep representation to evaluate the similarities between nodes. Second, a bottom-up community discovery algorithm is proposed. Via leader nodes selection, initial community generation, and community expansion, communities can be found more efficiently. Third, some incremental maintenance strategies for the changes of networks are proposed. We conduct experimental studies based on three real-world social networks. Experiments demonstrate the effectiveness and the efficiency of our proposed method. Compared with the traditional methods, our method improves normalized mutual information (NMI) and the modularity by an average of 12% and 37% respectively.

Key words: community discovery; heterogeneous information network; decomposition; embedding; incremental maintenance;

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