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Deepwalk-aware graph convolutional networks

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Abstract

Graph convolutional networks (GCNs) provide a promising way to extract the useful information from graph-structured data. Most of the existing GCNs methods usually focus on local neighborhood information based on specific convolution operations, and ignore the global structure of the input data. To extract the latent representation for the graph-structured data more effectively, we introduce a deepwalk strategy into GCNs to efficiently explore the global graph information. This strategy can complement the local neighborhood information of a graph, resulting in the more robust representation for the graph data. The fusion of the local neighboring and global structured information of a graph can further facilitate deep feature learning at the output layer of GCNs for node classification. Experimental results show that the proposed model has achieved state-of-the-art results on three benchmark datasets including Cora, Citeseer, and Pubmed citation networks.

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Acknowledgements

The work was supported by National Key Research and Development Plan Project (Grant Nos. 2018YFC0-830105, 2018YFC0830100), in part by National Science Fund for Distinguished Young Scholars (Grant No. 62025603), in part by National Natural Science Foundation of China (Grant Nos. U1705262, 62072386, 62072387, 62076016, 61772443).

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Authors and Affiliations

  1. Media Analytics and Computing Lab, Department of Computer Science and Technology, School of Informatics, Xiamen University, Xiamen, 361005, China

    Taisong Jin & Liujuan Cao

  2. Media Analytics and Computing Lab, Department of Artificial Intelligence, School of Informatics, Xiamen University, Xiamen, 361005, China

    Huaqiang Dai & Rongrong Ji

  3. School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100083, China

    Baochang Zhang

  4. Tencent Youtu Lab, Shanghai, 200233, China

    Feiyue Huang

  5. School of Software, Tsinghua University, Beijing, 100084, China

    Yue Gao

Authors
  1. Taisong Jin
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  2. Huaqiang Dai
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  3. Liujuan Cao
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  4. Baochang Zhang
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  5. Feiyue Huang
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  6. Yue Gao
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  7. Rongrong Ji
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Correspondence to Liujuan Cao.

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Jin, T., Dai, H., Cao, L. et al. Deepwalk-aware graph convolutional networks. Sci. China Inf. Sci. 65, 152104 (2022). https://doi.org/10.1007/s11432-020-3318-5

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  • DOI: https://doi.org/10.1007/s11432-020-3318-5

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