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/}.

我们提出了一种新颖而稳健的图神经网络编码器，低秩图对比学习（LR-GCL），并通过低秩正则化方法进行原型对比学习训练，然后使用LR-GCL生成的特征进行线性传导分类算法来对图中的未标记节点进行分类。我们的方法受到图数据和标签的低频属性以及我们对传导学习的尖锐一般化界限的理论动机，是在图对比学习中低秩学习优势在理论上得到证明的第一批理论结果之一。广泛的公共基准实验表明了LR-GCL的卓越性能和所学节点表示的鲁棒性。

节点分类的低秩图对比学习