mlsanityData to 7 Oct 2026

Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attack

arXiv 2202.07114 ↗First posted 15 Feb 2022cs.LG cs.CR cs.IRcited by 10

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Abstract (arXiv)

Deep graph learning (DGL) has achieved remarkable progress in both business and scientific areas ranging from finance and e-commerce to drug and advanced material discovery. Despite the progress, applying DGL to real-world applications faces a series of reliability threats including inherent noise, distribution shift, and adversarial attacks. This survey aims to provide a comprehensive review of recent advances for improving the reliability of DGL algorithms against the above threats. In contrast to prior related surveys which mainly focus on adversarial attacks and defense, our survey covers more reliability-related aspects of DGL, i.e., inherent noise and distribution shift. Additionally, we discuss the relationships among above aspects and highlight some important issues to be explored in future research.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 53 of its 84 references that are in the map. First posted on arXiv 15 Feb 2022.

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53 of 84 references.

References · 53

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  8. 2016Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approachcited by 663
  9. 2018FastGCN: Fast Learning with Graph Convolutional Networks via Importance Samplingcited by 583
  10. 2018Adversarial Attacks on Neural Networks for Graph Datacited by 395
  11. 2020Graph Structure Learning for Robust Graph Neural Networkscited by 368
  12. 2020Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsterscited by 248
38 more
  1. 2020Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddingscited by 212
  2. 2019Topology Attack and Defense for Graph Neural Networks: An Optimization Perspectivecited by 196
  3. 2018Adversarial Attack and Defense on Graph Data: A Surveycited by 166
  4. 2022Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphscited by 144
  5. 2020Learning to Drop: Robust Graph Neural Network via Topological Denoisingcited by 127
  6. 2020Backdoor Attacks to Graph Neural Networkscited by 126
  7. 2021DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networkscited by 117
  8. 2020Graph Backdoorcited by 112
  9. 2021Robustness of Graph Neural Networks at Scalecited by 100
  10. 2019Graph Adversarial Training: Dynamically Regularizing Based on Graph Structurecited by 93
  11. 2021Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibrationcited by 91
  12. 2021Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training Datacited by 86
  13. 2021Size-Invariant Graph Representations for Graph Classification Extrapolationscited by 82
  14. 2020Bayesian Graph Neural Networks with Adaptive Connection Samplingcited by 81
  15. 2021NRGNN: Learning a Label Noise-Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphscited by 73
  16. 2019A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding Modelscited by 68
  17. 2021TDGIA:Effective Injection Attacks on Graph Neural Networkscited by 67
  18. 2021Elastic Graph Neural Networkscited by 67
  19. 2022Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labelscited by 63
  20. 2021On the Unreasonable Effectiveness of Feature propagation in Learning on Graphs with Missing Node Featurescited by 62
  21. 2019Transferring Robustness for Graph Neural Network Against Poisoning Attackscited by 58
  22. 2020Certified Robustness of Graph Neural Networks against Adversarial Structural Perturbationcited by 57
  23. 2022DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise Annotationscited by 54
  24. 2020Scalable Attack on Graph Data by Injecting Vicious Nodescited by 50
  25. 2020Adversarial Attacks and Defenses on Graphs: A Review, A Tool and Empirical Studiescited by 42
  26. 2020A Survey of Adversarial Learning on Graphscited by 42
  27. 2021Explainability-based Backdoor Attacks Against Graph Neural Networkscited by 42
  28. 2020Adversarial Attack on Large Scale Graphcited by 39
  29. 2021Understanding Structural Vulnerability in Graph Convolutional Networkscited by 37
  30. 2021GraphMixup: Improving Class-Imbalanced Node Classification on Graphs by Self-supervised Context Predictioncited by 36
  31. 2021Graph Domain Adaptation: A Generative Viewcited by 35
  32. 2019Learning Graph Neural Networks with Noisy Labelscited by 29
  33. 2022Fair Node Representation Learning via Adaptive Data Augmentationcited by 27
  34. 2022Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagationcited by 25
  35. 2021Reliable Graph Neural Networks for Drug Discovery Under Distributional Shiftcited by 22
  36. 2021Unified Robust Training for Graph NeuralNetworks against Label Noisecited by 21
  37. 2020Graph Universal Adversarial Attacks: A Few Bad Actors Ruin Graph Learning Modelscited by 18
  38. 2020A Targeted Universal Attack on Graph Convolutional Networkcited by 12

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Cited by year

Citing papers per year: 2022: 1; 2023: 3; 2024: 2; 2025: 4.

10 papers.

  1. 2024A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challengescited by 54
  2. 2023ERASE: Error-Resilient Representation Learning on Graphs for Label Noise Tolerancecited by 11
  3. 2023Less Can Be More: Unsupervised Graph Pruning for Large-scale Dynamic Graphscited by 7
  4. 2023Uncertainty-Aware Robust Learning on Noisy Graphscited by 6
  5. 2022GUARD: Graph Universal Adversarial Defensecited by 2
  6. 2024Towards Robust Graph Structural Learning Beyond Homophily via Preserving Neighbor Similaritycited by 1
  7. 2025Robustness in Text-Attributed Graph Learning: Insights, Trade-offs, and New Defensescited by 1
  8. 2025Catch Causal Signals from Edges for Label Imbalance in Graph Classificationcited by 0
  9. 2025Framework GNN-AID: Graph Neural Network Analysis Interpretation and Defensecited by 0
  10. 2025PISA: Prioritized Invariant Subgraph Aggregation for Out-of-Distribution Generalization on Graphscited by 0