Papers › Simple and Deep Graph Convolutional Networks

Simple and Deep Graph Convolutional Networks

4 Jul 2020ICML 2020 1arXiv:2007.02133archive 2025-07-28

Ming Chen, Zhewei Wei, Zengfeng Huang, Bolin Ding, Yaliang Li

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 {\em 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 simple yet effective techniques: {\em Initial residual} and {\em Identity mapping}. We provide theoretical and empirical evidence that the two techniques effectively relieves the problem of over-smoothing. Our experiments show that the deep GCNII model outperforms the state-of-the-art methods on various semi- and full-supervised tasks. Code is available at https://github.com/chennnM/GCNII .

Code

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Tasks

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Methods

Introduced by this paper: GCNII

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