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Convolution Based Graph Representation Learning from the Perspective of High Order Node Similarities

Author

Listed:
  • Xing Li

    (School of Mathematical Sciences, Beihang University, Beijing 100191, China
    Key Laboratory of Mathematics Informatics Behavioral Semantics, Ministry of Education, Beijing 100191, China)

  • Qingsong Li

    (Key Laboratory of Mathematics Informatics Behavioral Semantics, Ministry of Education, Beijing 100191, China
    Institute of Artificial Intelligence, Beihang University, Beijing 100191, China
    Zhongguancun Laboratory, Beijing 100094, China)

  • Wei Wei

    (School of Mathematical Sciences, Beihang University, Beijing 100191, China
    Key Laboratory of Mathematics Informatics Behavioral Semantics, Ministry of Education, Beijing 100191, China
    Institute of Artificial Intelligence, Beihang University, Beijing 100191, China
    Zhongguancun Laboratory, Beijing 100094, China)

  • Zhiming Zheng

    (School of Mathematical Sciences, Beihang University, Beijing 100191, China
    Key Laboratory of Mathematics Informatics Behavioral Semantics, Ministry of Education, Beijing 100191, China
    Institute of Artificial Intelligence, Beihang University, Beijing 100191, China
    Zhongguancun Laboratory, Beijing 100094, China)

Abstract

Nowadays, graph representation learning methods, in particular graph neural network methods, have attracted great attention and performed well in many downstream tasks. However, most graph neural network methods have a single perspective since they start from the edges (or adjacency matrix) of graphs, ignoring the mesoscopic structure (high-order local structure). In this paper, we introduce HS-GCN (High-order Node Similarity Graph Convolutional Network), which can mine the potential structural features of graphs from different perspectives by combining multiple high-order node similarity methods. We analyze HS-GCN theoretically and show that it is a generalization of the convolution-based graph neural network methods from different normalization perspectives. A series of experiments have shown that by combining high-order node similarities, our method can capture and utilize the high-order structural information of the graph more effectively, resulting in better results.

Suggested Citation

  • Xing Li & Qingsong Li & Wei Wei & Zhiming Zheng, 2022. "Convolution Based Graph Representation Learning from the Perspective of High Order Node Similarities," Mathematics, MDPI, vol. 10(23), pages 1-13, December.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:23:p:4586-:d:992832
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    References listed on IDEAS

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