Corpus ID: 220363476

Simple and Deep Graph Convolutional Networks

@article{Chen2020SimpleAD,
  title={Simple and Deep Graph Convolutional Networks},
  author={M. Chen and Zhewei Wei and Zengfeng Huang and B. Ding and Y. Li},
  journal={ArXiv},
  year={2020},
  volume={abs/2007.02133}
}
  • M. Chen, Zhewei Wei, +2 authors Y. Li
  • Published 2020
  • Computer Science, Mathematics
  • ArXiv
  • Graph convolutional networks (GCNs) are a powerful deep learning approach for graph-structured data. Recently, GCNs and subsequent variants have shown superior performance in various application areas on real-world datasets. Despite their success, most of the current GCN models are shallow, due to the over-smoothing problem. In this paper, we study the problem of designing and analyzing deep graph convolutional networks. We propose the GCNII, an extension of the vanilla GCN model with two… CONTINUE READING

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