  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 .
