Simple and Deep Graph Convolutional Networks
This addresses a key bottleneck in deep graph learning for researchers and practitioners, offering an incremental improvement over existing shallow GCN models.
The paper tackles the over-smoothing problem in graph convolutional networks (GCNs) by proposing GCNII with initial residual and identity mapping techniques, achieving state-of-the-art performance on semi- and full-supervised tasks.
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 .