In this paper, we will investigate the causes of the GNNs' "suspended animation problem", and analyze if such a problem also exists in all other GNN models or not. GNNs are very different from the traditional deep learning models, and the existing solutions to resolve such problems, e.g., residual terms used in ResNet for CNN, cannot work well for GNNs actually. In this paper, several different novel graph residual terms will be studied for GNNs specially. Equipped with the new graph residual blocks, we will further introduce a new graph neural network architecture, namely graph residual neural network (GRESNET), to resolve the observed problem. Instead of merely stacking the spectral graph convolution layers on each other, GRESNET creates a high-way to allow the raw features of the nodes to be fed into the graph convolution operators in each layer of the model. We will study the effectiveness of the GRESNET architecture and those different graph residuals for several existing vanilla GNNs. In addition, theoretic analyses on GRESNET will be provided in this paper as well to demonstrate its effectiveness from the norm-preservation perspective.

本文发现目前基于谱图卷积算子的图神经网络（GNNs）存在悬挂动画问题，当模型深度达到悬挂动画极限时，模型将无法响应训练数据且无法学习。为了解决这一问题，我们引入了GResNet框架，该框架通过创建广泛连接的高速公路来涉及所有模型层中节点的原始特征或中间表示。我们对多种现有GNNs的GResNet框架进行了详细研究，并在真实世界基准数据集上进行了广泛的实证实验，证明了新引入的图残差项的有效性和规范保持优势。

GResNet: 用于唤醒深度暂停的图形残差网络(GNNs)