Graph Neural Networks (GNNs) have been widely used to learn node representations and with outstanding performance on various tasks such as node classification. However, noise, which inevitably exists in real-world graph data, would considerably degrade the performance of GNNs as the noise is easily propagated via the graph structure. In this work, we propose a novel and robust method, Bayesian Robust Graph Contrastive Learning (BRGCL), which trains a GNN encoder to learn robust node representations. The BRGCL encoder is a completely unsupervised encoder. Two steps are iteratively executed at each epoch of training the BRGCL encoder: (1) estimating confident nodes and computing robust cluster prototypes of node representations through a novel Bayesian nonparametric method; (2) prototypical contrastive learning between the node representations and the robust cluster prototypes. Experiments on public and large-scale benchmarks demonstrate the superior performance of BRGCL and the robustness of the learned node representations. The code of BRGCL is available at \url{https://github.com/BRGCL-code/BRGCL-code}.
翻译:神经网络(GNNs)被广泛用于学习节点表达方式,在节点分类等各种任务上表现突出,但是,噪音(在现实世界图形数据中不可避免地存在)会大大降低GNN的性能,因为噪音很容易通过图形结构传播。在这项工作中,我们提出了一个创新而有力的方法,即Bayesian Robust 图表对比学习(BRGCL),它训练GN 编码器学习稳健的节点表达方式。BRGCL 编码器是一个完全不受监督的编码器。在BRGCL 编码器培训的每一个阶段,都会反复执行两个步骤:(1) 通过一种新型的Bayesian非参数方法,估计自信的节点和计算结点表示的稳健的集群原型;(2) 节点表达方式和强健健的集原型之间的准的对比学习。对公共基准和大型基准的实验显示了BRGCL的优异性表现和所学的节点表达方式。BRGCL的代码可以在urlas/gihubbub.BRGC/BRGC/BRGC/codecodecodegy。