ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2020, Vol. 57 ›› Issue (8): 1663-1673.doi: 10.7544/issn1000-1239.2020.20200202

Special Issue: 2020数据挖掘与知识发现专题

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Conditional Variational Time-Series Graph Auto-Encoder

Chen Kejia1,2, Lu Hao1, Zhang Jiajun1   

  1. 1(School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023);2(Jiangsu Key Laboratory of Big Data Security & Intelligent Processing(Nanjing University of Posts and Telecommunications), Nanjing 210023)
  • Online:2020-08-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (61772284).

Abstract: Network representation learning (also called graph embedding) is the basis for graph tasks such as link prediction, node classification, community discovery, and graph visualization. Most of the existing graph embedding algorithms are mainly developed for static graphs, which is difficult to capture the dynamic characteristics of the real-world networks that evolve over time. At present, research on dynamic network representation learning is still inadequate. This paper proposes a conditional variational time-series graph auto-encoder (TS-CVGAE), which can simultaneously learn the local structure and evolution pattern of a dynamic network. The model improves the traditional graph convolution to obtain time-series graph convolution and uses it to encode the network in the framework of conditional variational auto-encoder. After training, the middle layer of TS-CVGAE is the final network embedding. Experimental results show that the method performs better in link prediction task than the related static and dynamic network representation learning methods with all four real dynamic network datasets.

Key words: network representation learning, conditional variational auto-encoder, dynamic network, graph convolution, link prediction

CLC Number: