  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}.
