We propose a unified framework for adaptive connection sampling in graph neural networks (GNNs) that generalizes existing stochastic regularization methods for training GNNs. The proposed framework not only alleviates over-smoothing and over-fitting tendencies of deep GNNs, but also enables learning with uncertainty in graph analytic tasks with GNNs. Instead of using fixed sampling rates or hand-tuning them as model hyperparameters in existing stochastic regularization methods, our adaptive connection sampling can be trained jointly with GNN model parameters in both global and local fashions. GNN training with adaptive connection sampling is shown to be mathematically equivalent to an efficient approximation of training Bayesian GNNs. Experimental results with ablation studies on benchmark datasets validate that adaptively learning the sampling rate given graph training data is the key to boost the performance of GNNs in semi-supervised node classification, less prone to over-smoothing and over-fitting with more robust prediction.

我们提出了一个统一的自适应连接采样框架，该框架可以泛化现有的用于训练GNN的随机正则化方法，并消除深度GNN的过度平滑和过拟合趋势，并允许在GNN图分析任务中具有不确定性的学习。将自适应连接采样与GNN模型参数以全局和局部方式联合训练，证明了使用自适应连接采样自适应性地学习给定图形训练数据的采样率是提高GNN性能的关键。

具有自适应连接采样的贝叶斯图神经网络