mlsanityData to 7 Oct 2026

A Survey of Adversarial Learning on Graphs

arXiv 2003.05730 ↗First posted 10 Mar 2020cs.LG cs.AI stat.MLcited by 42

Not on the map. Why?

Abstract (arXiv)

Deep learning models on graphs have achieved remarkable performance in various graph analysis tasks, e.g., node classification, link prediction, and graph clustering. However, they expose uncertainty and unreliability against the well-designed inputs, i.e., adversarial examples. Accordingly, a line of studies has emerged for both attack and defense addressed in different graph analysis tasks, leading to the arms race in graph adversarial learning. Despite the booming works, there still lacks a unified problem definition and a comprehensive review. To bridge this gap, we investigate and summarize the existing works on graph adversarial learning tasks systemically. Specifically, we survey and unify the existing works w.r.t. attack and defense in graph analysis tasks, and give appropriate definitions and taxonomies at the same time. Besides, we emphasize the importance of related evaluation metrics, investigate and summarize them comprehensively. Hopefully, our works can provide a comprehensive overview and offer insights for the relevant researchers. Latest advances in graph adversarial learning are summarized in our GitHub repository https://github.com/EdisonLeeeee/Graph-Adversarial-Learning.

Key facts. Cited by 42 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Mar 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 9; 2022: 13; 2023: 8; 2024: 4; 2025: 5; 2026: 3.

42 papers.

Sort:

By area:

  1. 2022A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainabilitycited by 118
  2. 2022Out-Of-Distribution Generalization on Graphs: A Surveycited by 97
  3. 2021Node-Level Membership Inference Attacks Against Graph Neural Networkscited by 80
  4. 2021Model Stealing Attacks Against Inductive Graph Neural Networkscited by 50
  5. 2021Single Node Injection Attack against Graph Neural Networkscited by 45
  6. 2021Understanding Structural Vulnerability in Graph Convolutional Networkscited by 37
  7. 2022A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protectioncited by 20
  8. 2022Projective Ranking-based GNN Evasion Attackscited by 14
  9. 2021NetFense: Adversarial Defenses against Privacy Attacks on Neural Networks for Graph Datacited by 13
  10. 2022Spectral Adversarial Training for Robust Graph Neural Networkcited by 12
  11. 2025Are Large Language Models In-Context Graph Learners?cited by 12
  12. 2022Adversarial Camouflage for Node Injection Attack on Graphscited by 11
30 more
  1. 2022Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attackcited by 10
  2. 2022Are Gradients on Graph Structure Reliable in Gray-box Attacks?cited by 10
  3. 2022GANI: Global Attacks on Graph Neural Networks via Imperceptible Node Injectionscited by 10
  4. 2021Intelligent Zero Trust Architecture for 5G/6G Networks: Principles, Challenges, and the Role of Machine Learning in the context of O-RANcited by 9
  5. 2021GraphGallery: A Platform for Fast Benchmarking and Easy Development of Graph Neural Networks Based Intelligent Softwarecited by 8
  6. 2023A semantic backdoor attack against Graph Convolutional Networkscited by 8
  7. 2023Less Can Be More: Unsupervised Graph Pruning for Large-scale Dynamic Graphscited by 7
  8. 2023SAILOR: Structural Augmentation Based Tail Node Representation Learningcited by 6
  9. 2023USER: Unsupervised Structural Entropy-based Robust Graph Neural Networkcited by 5
  10. 2023IDEA: Invariant Defense for Graph Adversarial Robustnesscited by 4
  11. 2024Rethinking Independent Cross-Entropy Loss For Graph-Structured Datacited by 4
  12. 2024Survey of Graph Neural Network for Internet of Things and NextG Networkscited by 4
  13. 2021Black-box Gradient Attack on Graph Neural Networks: Deeper Insights in Graph-based Attack and Defensecited by 3
  14. 2022Person Text-Image Matching via Text-Feature Interpretability Embedding and External Attack Node Implantationcited by 3
  15. 2022GUARD: Graph Universal Adversarial Defensecited by 2
  16. 2022What Does the Gradient Tell When Attacking the Graph Structurecited by 2
  17. 2023Graph Adversarial Immunization for Certifiable Robustnesscited by 2
  18. 2023Graph Neural Network approaches for single-cell data: A recent overviewcited by 2
  19. 2021Adversarial Robustness of Probabilistic Network Embedding for Link Predictioncited by 1
  20. 2023Explainability-Based Adversarial Attack on Graphs Through Edge Perturbationcited by 1
  21. 2024A Clean-graph Backdoor Attack against Graph Convolutional Networks with Poisoned Label Onlycited by 1
  22. 2022Are All Edges Necessary? A Unified Framework for Graph Purificationcited by 0
  23. 2024Certifying Robustness of Graph Convolutional Networks for Node Perturbation with Polyhedra Abstract Interpretationcited by 0
  24. 2025Quantifying the Noise of Structural Perturbations on Graph Adversarial Attackscited by 0
  25. 2025Budgeted Indirect Adversarial Attack on Graph-Based Anomaly Detection in Sensor Networkscited by 0
  26. 2025Unveiling the Vulnerability of Graph-LLMs: An Interpretable Multi-Dimensional Adversarial Attack on TAGscited by 0
  27. 2025Topologically-Stabilized Graph Neural Networks: Empirical Robustness Across Domainscited by 0
  28. 2026PEANUT: Perturbations by Eigenvector Alignment for Attacking Graph Neural Networks Under Topology-Driven Message Passingcited by 0
  29. 2026Trapping Attacker in Dilemma: Examining Internal Correlations and External Influences of Trigger for Defending GNN Backdoorscited by 0
  30. 2026Active Learning on Adversarially Corrupted Graphscited by 0