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The existing graph neural networks (GNNs) based on the spectral graphconvolutional operator have been criticized for its performance degradation,which is especially common for the models with deep architectures. In thispaper, we further identify the suspended animation problem with the existingGNNs. Such a problem happens when the model depth reaches the suspendedanimation limit, and the model will not respond to the training data any moreand become not learnable. Analysis about the causes of the suspended animationproblem with existing GNNs will be provided in this paper, whereas severalother peripheral factors that will impact the problem will be reported as well.To resolve the problem, we introduce the GResNet (Graph Residual Network)framework in this paper, which creates extensively connected highways toinvolve nodes' raw features or intermediate representations throughout thegraph for all the model layers. Different from the other learning settings, theextensive connections in the graph data will render the existing simpleresidual learning methods fail to work. We prove the effectiveness of theintroduced new graph residual terms from the norm preservation perspective,which will help avoid dramatic changes to the node's representations betweensequential layers. Detailed studies about the GResNet framework for manyexisting GNNs, including GCN, GAT and LoopyNet, will be reported in the paperwith extensive empirical experiments on real-world benchmark datasets.

GResNet: Graph Residual Network for Reviving Deep GNNs from Suspended Animation