Oversmoothing has been assumed to be the major cause of performance drop in
deep graph convolutional networks (GCNs). In this paper, we propose a new view
that deep GCNs can actually learn to anti-oversmooth during training. This work
interprets a standard GCN architecture as layerwise integration of a
Multi-layer Perceptron (MLP) and graph regularization. We analyze and conclude
that before training, the final representation of a deep GCN does over-smooth,
however, it learns anti-oversmoothing during training. Based on the conclusion,
the paper further designs a cheap but effective trick to improve GCN training.
We verify our conclusions and evaluate the trick on three citation networks and
further provide insights on neighborhood aggregation in GCNs.

本论文提出了一种新的观点，即深度图卷积网络在训练过程中可以学习抗去平滑化的能力，并设计了一种简单却有效的技巧来改善 GCN 训练，同时在三个引用网络上验证了结论并提供了 GCN 邻域聚合方面的见解。

深度图卷积网络中过度平滑化的重新审视

Revisiting Over-smoothing in Deep GCNs

Recently, many researchers have been focusing on the definition of neural
networks for graphs. The basic component for many of these approaches remains
the graph convolution idea proposed almost a decade ago. In this paper, we
extend this basic component, following an intuition derived from the well-known
convolutional filters over multi-dimensional tensors. In particular, we derive
a simple, efficient and effective way to introduce a hyper-parameter on graph
convolutions that influences the filter size, i.e. its receptive field over the
considered graph. We show with experimental results on real-world graph
datasets that the proposed graph convolutional filter improves the predictive
performance of Deep Graph Convolutional Networks.

本文介绍一种基于图卷积的滤波器方法，通过引入一个超参数为基本组件注入矩阵滤波器，用于提高深度图卷积神经网络预测性能。