Knowledge Network Node
Computer Engineering and Applications. Core
(Accepted Version) Online First Publishing Date: 2023-06-27 13:30:32

Graph Representation Learning Using Second-order Graph Convolutional AutoencodersChinese Full TextEnglish Full Text (MT)

YUAN Lining;JIANG Ping;MO Jiaying;LIU Zhao;

Abstract: Graph convolutional autoencoders emerged as powerful graph representation learning methods with promising performances on link prediction. However， existing methods typically rely on graph convolutional network to encode adjacency matrix and attribute matrix. This strategy ignores high-order features such as second-order information. In this paper， we propose a new SeVGAE model based on second-order graph convolutional autoencoders to tackle this problem. Firstly， graph convolutional network and... More
Keywords：

graph representation learning; second-order graph convolutional network; link prediction;

  • Series：

    (I) Electronic Technology ＆ Information Science

  • Subject：

    Automation Technology

  • Classification Code：

    TP18

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