Graph convolutional networks have been successfully applied in various graph-based tasks. In a typical graph convolutional layer, node features are updated by aggregating neighborhood information. Repeatedly applying graph convolutions can cause the oversmoothing issue, i.e., node features at deep layers converge to similar values. Previous studies have suggested that oversmoothing is one of the major issues that restrict the performance of graph convolutional networks. In this paper, we propose a stochastic regularization method to tackle the oversmoothing problem. In the proposed method, we stochastically scale features and gradients (SSFG) by a factor sampled from a probability distribution in the training procedure. By explicitly applying a scaling factor to break feature convergence, the oversmoothing issue is alleviated. We show that applying stochastic scaling at the gradient level is complementary to that applied at the feature level to improve the overall performance. Our method does not increase the number of trainable parameters. When used together with ReLU, our SSFG can be seen as a stochastic ReLU activation function. We experimentally validate our SSFG regularization method on three commonly used types of graph networks. Extensive experimental results on seven benchmark datasets for four graph-based tasks demonstrate that our SSFG regularization is effective in improving the overall performance of the baseline graph networks.

翻译：在典型的图形革命层中,通过汇总周边信息来更新节点特征。反复应用图形演变可以导致过度悬浮问题,即深层的节点特征与相似值相融合。先前的研究表明,过度悬浮是限制图形革命网络性能的主要问题之一。在本文中,我们提出一种处理过度移动问题的随机正规化方法。在拟议方法中,我们用从培训过程中概率分布抽样的系数来对比例尺特征进行更新。通过明确应用一个缩放系数来打破特征趋同,过度悬浮问题得到缓解。我们表明,在梯度水平上应用悬浮测量度是用来改进图形总体性能的重大问题之一。我们的方法并不增加可训练参数的数量。在与RELU一起使用时,我们SSFG的SFStocast特征和梯度(SSSSSSSSSSSSSSD) 三种实验性分析模型,我们用SSSSSSSF模型来验证我们四种实验性基准的SSDSB模型。