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Graph attention network via node similarity for link prediction

Author

Listed:
  • Kai Yang

    (Yangzhou University)

  • Yuan Liu

    (Yangzhou University)

  • Zijuan Zhao

    (University of Shanghai for Science and Technology)

  • Xingxing Zhou

    (Yangzhou University)

  • Peijin Ding

    (Yangzhou University)

Abstract

Link prediction is a classic complex network analytical problem to predict the possible links according to the known network structure information. Considering similar nodes should present closer embedding vectors with network representation learning, in this paper, we propose a Graph ATtention network method based on node Similarity (SiGAT) for link prediction. Specifically, we calculate similar node set for each node in the network by traditional method. The similar nodes and first-order neighbors are assigned an optimal weight through the graph attention network mechanism. Then, we obtain the embedding vectors of nodes with aggregating the information of the similar nodes and first-order neighbor nodes. By incorporating similar nodes, the node embeddings preserve more structure information of the network in low-dimensional embedding space. Finally, the SiGAT represents the links between pairs of nodes with concatenating the node embedding vectors and then trains a classifier to predict novel potential network links. The results of experiments on five real datasets and large-scale artificial datasets, which are the Yeast dataset, Cora dataset, BIO-CE-HT dataset, Human proteins (Vidal) dataset, Human proteins (Stelzl) dataset, and LFR benchmark datasets, show that the SiGAT outperforms the existing popular approaches.

Suggested Citation

  • Kai Yang & Yuan Liu & Zijuan Zhao & Xingxing Zhou & Peijin Ding, 2023. "Graph attention network via node similarity for link prediction," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 96(3), pages 1-10, March.
    • Handle: RePEc:spr:eurphb:v:96:y:2023:i:3:d:10.1140_epjb_s10051-023-00495-1
    DOI: 10.1140/epjb/s10051-023-00495-1
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    References listed on IDEAS

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