  Graph Convolutional Networks (GCNs) have been drawing significant attention
with the power of representation learning on graphs. Unlike Convolutional
Neural Networks (CNNs), which are able to take advantage of stacking very deep
layers, GCNs suffer from vanishing gradient, over-smoothing and over-fitting
issues when going deeper. These challenges limit the representation power of
GCNs on large-scale graphs. This paper proposes DeeperGCN that is capable of
successfully and reliably training very deep GCNs. We define differentiable
generalized aggregation functions to unify different message aggregation
operations (e.g. mean, max). We also propose a novel normalization layer namely
MsgNorm and a pre-activation version of residual connections for GCNs.
Extensive experiments on Open Graph Benchmark (OGB) show DeeperGCN
significantly boosts performance over the state-of-the-art on the large scale
graph learning tasks of node property prediction and graph property prediction.
Please visit https://www.deepgcns.org for more information.
