Graph Contrastive Learning (GCL) has emerged as a popular unsupervised graph
representation learning method. However, it has been shown that GCL is
vulnerable to adversarial attacks on both the graph structure and node
attributes. Although empirical approaches have been proposed to enhance the
robustness of GCL, the certifiable robustness of GCL is still remain
unexplored. In this paper, we develop the first certifiably robust framework in
GCL. Specifically, we first propose a unified criteria to evaluate and certify
the robustness of GCL. We then introduce a novel technique, RES (Randomized
Edgedrop Smoothing), to ensure certifiable robustness for any GCL model, and
this certified robustness can be provably preserved in downstream tasks.
Furthermore, an effective training method is proposed for robust GCL. Extensive
experiments on real-world datasets demonstrate the effectiveness of our
proposed method in providing effective certifiable robustness and enhancing the
robustness of any GCL model. The source code of RES is available at
this https URL

在本文中，我们开发了第一个能够确定性地提高图对比学习（Graph Contrastive Learning，GCL）鲁棒性的框架。我们提出了一个统一的评估和认证 GCL 鲁棒性的标准，并引入了一种新的技术 RES（Randomized Edgedrop Smoothing）来确保任何 GCL 模型的鲁棒性，并能够在下游任务中被可靠地保持。此外，我们还提出了一种有效的培训方法来提高 GCL 的鲁棒性。在真实世界的数据集上进行的大量实验证明了我们提出的方法在提供有效的可证鲁棒性和提高任何 GCL 模型的鲁棒性方面的有效性。RES 的源代码可在此 https URL 获得。