  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 revealed by recent
studies. In this work, we propose a novel and robust GNN encoder, Low-Rank
Graph Contrastive Learning (LR-GCL). Our method performs transductive node
classification in two steps. First, a low-rank GCL encoder named LR-GCL is
trained by prototypical contrastive learning with low-rank regularization.
Next, using the features produced by LR-GCL, a linear transductive
classification algorithm is used to classify the unlabeled nodes in the graph.
Our LR-GCL is inspired by the low frequency property of the graph data and its
labels, and it is also theoretically motivated by our sharp generalization
bound for transductive learning. To the best of our knowledge, our theoretical
result is among the first to theoretically demonstrate the advantage of
low-rank learning in graph contrastive learning supported by strong empirical
performance. Extensive experiments on public benchmarks demonstrate the
superior performance of LR-GCL and the robustness of the learned node
representations. The code of LR-GCL is available at
\url{https://anonymous.4open.science/r/Low-Rank_Graph_Contrastive_Learning-64A6/}.
