DeGNN: Characterizing and Improving Graph Neural Networks with Graph Decomposition

10 Oct 2019Xupeng MiaoNezihe Merve GürelWentao ZhangZhichao HanBo LiWei MinXi RaoHansheng RenYinan ShanYingxia ShaoYujie WangFan WuHui XueYaming YangZitao ZhangYang ZhaoShuai ZhangYujing WangBin CuiCe Zhang

Despite the wide application of Graph Convolutional Network (GCN), one major limitation is that it does not benefit from the increasing depth and suffers from the oversmoothing problem. In this work, we first characterize this phenomenon from the information-theoretic perspective and show that under certain conditions, the mutual information between the output after $l$ layers and the input of GCN converges to 0 exponentially with respect to $l$... (read more)

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