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DeeperGCN: All You Need to Train Deeper GCNs

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arxiv 2006.07739 v1 pith:VZ2A2LBZ submitted 2020-06-13 cs.LG cs.CVstat.ML

Guohao Li , Chenxin Xiong , Ali Thabet , Bernard Ghanem This is my paper
classification cs.LGcs.CVstat.ML
keywords gcnsgraphdeepergcnaggregationconvolutionaldeepdeepergraphs
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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