Certifiably Robust Graph Contrastive Learning
Minhua Lin Teng Xiao Enyan Dai Xiang Zhang Suhang Wang
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.