Data to 7 Oct 2026

Adversarial Attack on Graph Structured Data

arXiv 1806.02371 ↗First posted 6 Jun 2018cs.LG cs.CR cs.SIcited by 300

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Abstract (arXiv)

Deep learning on graph structures has shown exciting results in various applications. However, few attentions have been paid to the robustness of such models, in contrast to numerous research work for image or text adversarial attack and defense. In this paper, we focus on the adversarial attacks that fool the model by modifying the combinatorial structure of data. We first propose a reinforcement learning based attack method that learns the generalizable attack policy, while only requiring prediction labels from the target classifier. Also, variants of genetic algorithms and gradient methods are presented in the scenario where prediction confidence or gradients are available. We use both synthetic and real-world data to show that, a family of Graph Neural Network models are vulnerable to these attacks, in both graph-level and node-level classification tasks. We also show such attacks can be used to diagnose the learned classifiers.

Key facts. Cited by 300 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Jun 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 72; 2022: 72; 2023: 61; 2024: 49; 2025: 31; 2026: 15.

300 papers.

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2022Scaling Laws for Reward Model Overoptimizationcited by 1,056
2021Recent Advances in Adversarial Training for Adversarial Robustnesscited by 276
2021Gradient-based Adversarial Attacks against Text Transformerscited by 233
2021A Comprehensive Survey on Graph Anomaly Detection with Deep Learningcited by 230
2022Finding Global Homophily in Graph Neural Networks When Meeting Heterophilycited by 179
2022SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentationcited by 171
2023Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examplescited by 163
2021Subgraph Federated Learning with Missing Neighbor Generationcited by 132
2022A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainabilitycited by 118
2021SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networkscited by 108
2021Robustness of Graph Neural Networks at Scalecited by 100
2022Trustworthy Graph Neural Networks: Aspects, Methods and Trendscited by 88

38 more

2021Membership Inference Attack on Graph Neural Networkscited by 81
2021Node-Level Membership Inference Attacks Against Graph Neural Networkscited by 80
2021A Survey on Graph Structure Learning: Progress and Opportunitiescited by 70
2021TDGIA:Effective Injection Attacks on Graph Neural Networkscited by 67
2021LinkTeller: Recovering Private Edges from Graph Neural Networks via Influence Analysiscited by 65
2022Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labelscited by 63
2022Understanding and Improving Graph Injection Attack by Promoting Unnoticeabilitycited by 62
2022Graph Data Augmentation for Graph Machine Learning: A Surveycited by 61
2024A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challengescited by 54
2021Model Stealing Attacks Against Inductive Graph Neural Networkscited by 50
2021Single Node Injection Attack against Graph Neural Networkscited by 45
2023Unnoticeable Backdoor Attacks on Graph Neural Networkscited by 45
2021Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learningcited by 44
2021Understanding Structural Vulnerability in Graph Convolutional Networkscited by 37
2021Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustnesscited by 35
2022Reliable Representations Make A Stronger Defender: Unsupervised Structure Refinement for Robust GNNcited by 34
2024Graph Machine Learning in the Era of Large Language Models (LLMs)cited by 33
2021Graph Transplant: Node Saliency-Guided Graph Mixup with Local Structure Preservationcited by 30
2023OpenGSL: A Comprehensive Benchmark for Graph Structure Learningcited by 30
2021Recognizing Predictive Substructures with Subgraph Information Bottleneckcited by 28
2021Probabilistic Entity Representation Model for Reasoning over Knowledge Graphscited by 28
2023Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directionscited by 28
2021Topological Relational Learning on Graphscited by 27
2022Challenges and Opportunities in Deep Reinforcement Learning with Graph Neural Networks: A Comprehensive review of Algorithms and Applicationscited by 27
2021A Hard Label Black-box Adversarial Attack Against Graph Neural Networkscited by 26
2022Model Inversion Attacks against Graph Neural Networkscited by 26
2022Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagationcited by 25
2021Understanding the Robustness of Skeleton-based Action Recognition under Adversarial Attackcited by 23
2021How does Heterophily Impact the Robustness of Graph Neural Networks? Theoretical Connections and Practical Implicationscited by 21
2022Model-Agnostic Augmentation for Accurate Graph Classificationcited by 21
2023Complex Query Answering on Eventuality Knowledge Graph with Implicit Logical Constraintscited by 21
2021Adversarial Attack on Graph Neural Networks as An Influence Maximization Problemcited by 20
2022Self-Supervised Graph Structure Refinement for Graph Neural Networkscited by 20
2023Graph Decision Transformercited by 20
2022Transferable Graph Backdoor Attackcited by 19
2022Motif-Backdoor: Rethinking the Backdoor Attack on Graph Neural Networks via Motifscited by 19
2022Let Graph be the Go Board: Gradient-free Node Injection Attack for Graph Neural Networks via Reinforcement Learningcited by 19
2023Revisiting Robustness in Graph Machine Learningcited by 19

50 most cited of 300 All 300 (JSON)