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 https://github.com/ventr1c/RES-GCL.

翻译：图对比学习（GCL）已成为一种流行的无监督图表示学习方法。然而，研究表明GCL在图结构和节点属性上均易受对抗攻击。尽管已有经验性方法被提出以增强GCL的鲁棒性，但GCL的可认证鲁棒性仍未得到探索。本文首次构建了GCL中可认证鲁棒性框架。具体而言，我们首先提出统一准则来评估和认证GCL的鲁棒性。随后，我们引入一种新技术——随机边丢弃平滑（RES），以确保任意GCL模型的可认证鲁棒性，并且该认证鲁棒性可在下游任务中得到可证明的保持。此外，我们提出了一种针对鲁棒GCL的有效训练方法。在真实世界数据集上的大量实验表明，我们提出的方法在提供有效可认证鲁棒性及增强任意GCL模型的鲁棒性方面具有显著效果。RES源代码已公开于https://github.com/ventr1c/RES-GCL。