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A Community Detection Method Based on Variational Graph Auto-Encoders

LIU Peng, GUI Liang, LIU Huiyu   

  1. School of Management and Economics, Jiangsu University of Science and Technology, Zhenjiang 212003
  • Received:2022-01-06 Revised:2022-03-21 Online:2022-06-25 Published:2022-07-29

LIU Peng, GUI Liang, LIU Huiyu. A Community Detection Method Based on Variational Graph Auto-Encoders[J]. Journal of Systems Science and Mathematical Sciences, 2022, 42(6): 1402-1410.

Community detection is an important issue in the field of complex network research. However, the traditional methods that achieve community detection through the density of edges can not include the non-structural attributes of nodes. In this paper, we propose a method of community detection based on variational graph auto-encoders (VGAE) incorporating node attributes, namely VGAE-INA, and then test the method by real network data in two different fields. Through the experiment, we find that the modularity obtained by the proposed method is not significantly different from the traditional methods (such as the Louvain method) and the methods based on deep learning (such as the node2vec method), but the node similarity in the community is much higher than these methods. This result indicates that through unsupervised iterative learning, VGAE-INA can effectively detect the network community under the condition of considering both connections and attributes of nodes. At the same time, our method also lays the groundwork for the performance improvement of practical applications based on community detection such as personalized recommendations and opinion mining in crowds.

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