mlsanityData to 7 Oct 2026

Towards Robust Graph Contrastive Learning

arXiv 2102.13085 ↗First posted 25 Feb 2021cs.LG cs.AI cs.SIcited by 16

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Abstract (arXiv)

We study the problem of adversarially robust self-supervised learning on graphs. In the contrastive learning framework, we introduce a new method that increases the adversarial robustness of the learned representations through i) adversarial transformations and ii) transformations that not only remove but also insert edges. We evaluate the learned representations in a preliminary set of experiments, obtaining promising results. We believe this work takes an important step towards incorporating robustness as a viable auxiliary task in graph contrastive learning.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 35 of its 42 references that are in the map. First posted on arXiv 25 Feb 2021.

Builds on

35 of 42 references.

References · 35

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  3. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  4. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  5. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  6. 2013Intriguing properties of neural networkscited by 5,114
  7. 2020Bootstrap your own latent: A new approach to self-supervised Learningcited by 4,712
  8. 2020Supervised Contrastive Learningcited by 2,803
  9. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
  10. 2015DeepFool: a simple and accurate method to fool deep neural networkscited by 1,315
  11. 2018Learning deep representations by mutual information estimation and maximizationcited by 1,265
  12. 2020Graph Contrastive Learning with Augmentationscited by 1,203
23 more
  1. 2018Deep Graph Infomaxcited by 1,101
  2. 2016Revisiting Semi-Supervised Learning with Graph Embeddingscited by 970
  3. 2015Image-based Recommendations on Styles and Substitutescited by 702
  4. 2020Contrastive Multi-View Representation Learning on Graphscited by 651
  5. 2020Deep Graph Contrastive Representation Learningcited by 535
  6. 2020Graph Contrastive Learning with Adaptive Augmentationcited by 519
  7. 2020GCC: Graph Contrastive Coding for Graph Neural Network Pre-Trainingcited by 461
  8. 2019InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximizationcited by 441
  9. 2018Adversarial Attacks on Neural Networks for Graph Datacited by 395
  10. 2020Graph Structure Learning for Robust Graph Neural Networkscited by 368
  11. 2020GPT-GNN: Generative Pre-Training of Graph Neural Networkscited by 302
  12. 2018Adversarial Attack on Graph Structured Datacited by 300
  13. 2019Adversarial Attacks on Graph Neural Networks via Meta Learningcited by 261
  14. 2020Graph Representation Learning via Graphical Mutual Information Maximizationcited by 217
  15. 2020GNNGuard: Defending Graph Neural Networks against Adversarial Attackscited by 195
  16. 2020Robust Pre-Training by Adversarial Contrastive Learningcited by 144
  17. 2020Adversarial Self-Supervised Contrastive Learningcited by 138
  18. 2019Graph Adversarial Training: Dynamically Regularizing Based on Graph Structurecited by 93
  19. 2020Contrastive Learning with Adversarial Examplescited by 91
  20. 2020DeepRobust: A PyTorch Library for Adversarial Attacks and Defensescited by 78
  21. 2020Adversarial Attacks and Defenses on Graphs: A Review, A Tool and Empirical Studiescited by 42
  22. 2019Pre-Training Graph Neural Networks for Generic Structural Feature Extractioncited by 24
  23. 2020Self-supervised Graph Representation Learning via Bootstrappingcited by 8

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Cited by year

Citing papers per year: 2021: 3; 2022: 7; 2023: 3; 2024: 3.

16 papers.

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  1. 2021Graph Self-Supervised Learning: A Surveycited by 289
  2. 2022SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentationcited by 171
  3. 2021Self-supervised Learning on Graphs: Contrastive, Generative,or Predictivecited by 136
  4. 2022Adversarial Graph Contrastive Learning with Information Regularizationcited by 38
  5. 2022Line Graph Contrastive Learning for Link Predictioncited by 13
  6. 2022Data Augmentation on Graphs: A Technical Surveycited by 10
  7. 2023Graph Contrastive Learning with Generative Adversarial Networkcited by 8
  8. 2024Exploring Correlations of Self-Supervised Tasks for Graphscited by 7
  9. 2022Heterogeneous Graph Contrastive Learning with Meta-path Contexts and Adaptively Weighted Negative Samplescited by 6
  10. 2022ARIEL: Adversarial Graph Contrastive Learningcited by 5
  11. 2023Certifiably Robust Graph Contrastive Learningcited by 5
  12. 2023On the Adversarial Robustness of Graph Contrastive Learning Methodscited by 2
4 more
  1. 2024Structure-Aware Consensus Network on Graphs with Few Labeled Nodescited by 2
  2. 2021Self-Supervised Graph Learning with Proximity-based Views and Channel Contrastcited by 0
  3. 2022Coarse-to-Fine Contrastive Learning on Graphscited by 0
  4. 2024REGE: A Method for Incorporating Uncertainty in Graph Embeddingscited by 0