mlsanityData to 7 Oct 2026

Certifiably Robust Graph Contrastive Learning

arXiv 2310.03312 ↗First posted 5 Oct 2023cs.CR cs.LGcited by 5

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 53 of its 65 references that are in the map. First posted on arXiv 5 Oct 2023.

Builds on

53 of 65 references.

References · 53

  1. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  3. 2018Representation Learning with Contrastive Predictive Codingcited by 7,662
  4. 2017Inductive Representation Learning on Large Graphscited by 6,284
  5. 2020Bootstrap your own latent: A new approach to self-supervised Learningcited by 4,712
  6. 2018How Powerful are Graph Neural Networks?cited by 4,355
  7. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,148
  8. 2016node2vec: Scalable Feature Learning for Networkscited by 1,913
  9. 2016Variational Graph Auto-Encoderscited by 1,373
  10. 2019Certified Adversarial Robustness via Randomized Smoothingcited by 1,324
  11. 2018Learning deep representations by mutual information estimation and maximizationcited by 1,265
  12. 2020Graph Contrastive Learning with Augmentationscited by 1,203
38 more
  1. 2018Deep Graph Infomaxcited by 1,101
  2. 2017MoleculeNet: A Benchmark for Molecular Machine Learningcited by 1,037
  3. 2018Pitfalls of Graph Neural Network Evaluationcited by 994
  4. 2020TUDataset: A collection of benchmark datasets for learning with graphscited by 718
  5. 2020Contrastive Multi-View Representation Learning on Graphscited by 651
  6. 2020Deep Graph Contrastive Representation Learningcited by 535
  7. 2019A Theoretical Analysis of Contrastive Unsupervised Representation Learningcited by 530
  8. 2020Graph Contrastive Learning with Adaptive Augmentationcited by 519
  9. 2018Certified Robustness to Adversarial Examples with Differential Privacycited by 462
  10. 2019InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximizationcited by 441
  11. 2018Adversarial Attacks on Neural Networks for Graph Datacited by 395
  12. 2018Adversarial Attack on Graph Structured Datacited by 300
  13. 2021Adversarial Graph Augmentation to Improve Graph Contrastive Learningcited by 200
  14. 2019Topology Attack and Defense for Graph Neural Networks: An Optimization Perspectivecited by 196
  15. 2021Large-Scale Representation Learning on Graphs via Bootstrappingcited by 184
  16. 2020A Semi-supervised Graph Attentive Network for Financial Fraud Detectioncited by 150
  17. 2022A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainabilitycited by 118
  18. 2021An Empirical Study of Graph Contrastive Learningcited by 113
  19. 2021Robustness of Graph Neural Networks at Scalecited by 100
  20. 2021Towards Self-Explainable Graph Neural Networkcited by 75
  21. 2021TDGIA:Effective Injection Attacks on Graph Neural Networkscited by 67
  22. 2022Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labelscited by 63
  23. 2019Certifiable Robustness to Graph Perturbationscited by 62
  24. 2022Understanding and Improving Graph Injection Attack by Promoting Unnoticeabilitycited by 62
  25. 2019Certifiable Robustness and Robust Training for Graph Convolutional Networkscited by 59
  26. 2020Certified Robustness of Graph Neural Networks against Adversarial Structural Perturbationcited by 57
  27. 2022Let Invariant Rationale Discovery Inspire Graph Contrastive Learningcited by 57
  28. 2020Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and Morecited by 56
  29. 2021Investigating the Role of Negatives in Contrastive Representation Learningcited by 54
  30. 2023Unnoticeable Backdoor Attacks on Graph Neural Networkscited by 45
  31. 2022Training with More Confidence: Mitigating Injected and Natural Backdoors During Trainingcited by 38
  32. 2022Adversarial Graph Contrastive Learning with Information Regularizationcited by 38
  33. 2022Rethinking the Reverse-engineering of Trojan Triggerscited by 37
  34. 2022Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagationcited by 25
  35. 2023Learning How to Propagate Messages in Graph Neural Networkscited by 23
  36. 2022Label-invariant Augmentation for Semi-Supervised Graph Classificationcited by 18
  37. 2021Towards Robust Graph Contrastive Learningcited by 16
  38. 2020Efficient, Direct, and Restricted Black-Box Graph Evasion Attacks to Any-Layer Graph Neural Networks via Influence Functioncited by 14

50 most cited of 53 All 53 (JSON)

Built on by

Cited by year

Citing papers per year: 2024: 4; 2025: 1.

5 papers.

  1. 2024A Survey on Self-Supervised Graph Foundation Models: Knowledge-Based Perspectivecited by 9
  2. 2024PreGIP: Watermarking the Pretraining of Graph Neural Networks for Deep Intellectual Property Protectioncited by 2
  3. 2024Are You Using Reliable Graph Prompts? Trojan Prompt Attacks on Graph Neural Networkscited by 2
  4. 2024Rethinking Spectral Augmentation for Contrast-based Graph Self-Supervised Learningcited by 1
  5. 2025Adaptive Multi-view Graph Contrastive Learning via Fractional-order Neural Diffusion Networkscited by 0