mlsanityData to 7 Oct 2026

Adversarial Graph Contrastive Learning with Information Regularization

arXiv 2202.06491 ↗First posted 14 Feb 2022cs.LGcited by 38

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Abstract (arXiv)

Contrastive learning is an effective unsupervised method in graph representation learning. Recently, the data augmentation based contrastive learning method has been extended from images to graphs. However, most prior works are directly adapted from the models designed for images. Unlike the data augmentation on images, the data augmentation on graphs is far less intuitive and much harder to provide high-quality contrastive samples, which are the key to the performance of contrastive learning models. This leaves much space for improvement over the existing graph contrastive learning frameworks. In this work, by introducing an adversarial graph view and an information regularizer, we propose a simple but effective method, Adversarial Graph Contrastive Learning (ARIEL), to extract informative contrastive samples within a reasonable constraint. It consistently outperforms the current graph contrastive learning methods in the node classification task over various real-world datasets and further improves the robustness of graph contrastive learning. The code is at https://github.com/Shengyu-Feng/ARIEL.

Key facts. Cited by 38 papers in the map (data through 7 Oct 2026). Builds on 34 of its 46 references that are in the map. First posted on arXiv 14 Feb 2022.

Builds on

34 of 46 references.

References · 34

  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. 2017Graph Attention Networkscited by 8,854
  4. 2018Representation Learning with Contrastive Predictive Codingcited by 7,662
  5. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,482
  6. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  7. 2021Convex optimizationcited by 2,479
  8. 2016node2vec: Scalable Feature Learning for Networkscited by 1,913
  9. 2014DeepWalk: Online Learning of Social Representationscited by 1,543
  10. 2018Learning deep representations by mutual information estimation and maximizationcited by 1,265
  11. 2020Graph Contrastive Learning with Augmentationscited by 1,203
  12. 2018Deep Graph Infomaxcited by 1,101
22 more
  1. 2018Pitfalls of Graph Neural Network Evaluationcited by 994
  2. 2016Revisiting Semi-Supervised Learning with Graph Embeddingscited by 970
  3. 2020Contrastive Multi-View Representation Learning on Graphscited by 651
  4. 2020Contrastive Learning with Hard Negative Samplescited by 619
  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. 2018Adversarial Attacks on Neural Networks for Graph Datacited by 395
  9. 2019Adversarial Attacks on Graph Neural Networks via Meta Learningcited by 261
  10. 2020Graph Representation Learning via Graphical Mutual Information Maximizationcited by 217
  11. 2020Data Augmentation for Graph Neural Networkscited by 200
  12. 2021Adversarial Graph Augmentation to Improve Graph Contrastive Learningcited by 200
  13. 2019Topology Attack and Defense for Graph Neural Networks: An Optimization Perspectivecited by 196
  14. 2020Robust Pre-Training by Adversarial Contrastive Learningcited by 144
  15. 2020Adversarial Self-Supervised Contrastive Learningcited by 138
  16. 2018Adversarial Attacks on Node Embeddings via Graph Poisoningcited by 109
  17. 2020Contrastive Learning with Adversarial Examplescited by 91
  18. 2021HDMI: High-order Deep Multiplex Infomaxcited by 82
  19. 2018AceKG: A Large-scale Knowledge Graph for Academic Data Miningcited by 28
  20. 2021Adversarial Attack on Graph Neural Networks as An Influence Maximization Problemcited by 20
  21. 2021Towards Robust Graph Contrastive Learningcited by 16
  22. 2018AURORA: Auditing PageRank on Large Graphscited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 8; 2023: 14; 2024: 12; 2025: 3; 2026: 1.

38 papers.

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  1. 2022Data Augmentation for Deep Graph Learning: A Surveycited by 132
  2. 2022A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainabilitycited by 118
  3. 2022Spectral Augmentation for Self-Supervised Learning on Graphscited by 38
  4. 2023FOCAL: Contrastive Learning for Multimodal Time-Series Sensing Signals in Factorized Orthogonal Latent Spacecited by 31
  5. 2022Augmentations in Hypergraph Contrastive Learning: Fabricated and Generativecited by 28
  6. 2024Heterogeneous Contrastive Learning for Foundation Models and Beyondcited by 27
  7. 2024Towards Graph Contrastive Learning: A Survey and Beyondcited by 26
  8. 2022MentorGNN: Deriving Curriculum for Pre-Training GNNscited by 18
  9. 2023HomoGCL: Rethinking Homophily in Graph Contrastive Learningcited by 18
  10. 2022COIN: Co-Cluster Infomax for Bipartite Graphscited by 14
  11. 2024PyG-SSL: A Graph Self-Supervised Learning Toolkitcited by 14
  12. 2023Spatial-Temporal Graph Learning with Adversarial Contrastive Adaptationcited by 13
26 more
  1. 2024Can Graph Neural Networks Learn Language with Extremely Weak Text Supervision?cited by 13
  2. 2023STERLING: Synergistic Representation Learning on Bipartite Graphscited by 12
  3. 2025Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecastingcited by 12
  4. 2024Causality-Aware Spatiotemporal Graph Neural Networks for Spatiotemporal Time Series Imputationcited by 11
  5. 2022MA-GCL: Model Augmentation Tricks for Graph Contrastive Learningcited by 10
  6. 2023Mastering Long-Tail Complexity on Graphs: Characterization, Learning, and Generalizationcited by 8
  7. 2023Faithful and Consistent Graph Neural Network Explanations with Rationale Alignmentcited by 6
  8. 2023Fairness-aware Multi-view Clusteringcited by 6
  9. 2023SEGA: Structural Entropy Guided Anchor View for Graph Contrastive Learningcited by 6
  10. 2023Similarity Preserving Adversarial Graph Contrastive Learningcited by 6
  11. 2022ARIEL: Adversarial Graph Contrastive Learningcited by 5
  12. 2023Certifiably Robust Graph Contrastive Learningcited by 5
  13. 2024Automated Contrastive Learning Strategy Search for Time Seriescited by 5
  14. 2023Local Structure-aware Graph Contrastive Representation Learningcited by 4
  15. 2024From Overfitting to Robustness: Quantity, Quality, and Variety Oriented Negative Sample Selection in Graph Contrastive Learningcited by 4
  16. 2023EdgePruner: Poisoned Edge Pruning in Graph Contrastive Learningcited by 1
  17. 2024Graph Contrastive Learning with Low-Rank Regularization and Low-Rank Attention for Noisy Node Classificationcited by 1
  18. 2024Edge Contrastive Learning: An Augmentation-Free Graph Contrastive Learning Modelcited by 1
  19. 2023HAT-GAE: Self-Supervised Graph Auto-encoders with Hierarchical Adaptive Masking and Trainable Corruptioncited by 0
  20. 2023Robust Graph Contrastive Learning with Information Restorationcited by 0
  21. 2024Dual Adversarial Perturbators Generate rich Views for Recommendationcited by 0
  22. 2024Towards Fair Graph Representation Learning in Social Networkscited by 0
  23. 2024Uncovering Capabilities of Model Pruning in Graph Contrastive Learningcited by 0
  24. 2025Negative Metric Learning for Graphscited by 0
  25. 2025Adaptive Multi-view Graph Contrastive Learning via Fractional-order Neural Diffusion Networkscited by 0
  26. 2026Disentangled Dual-Branch Graph Learning for Conversational Emotion Recognitioncited by 0