# A Survey of Adversarial Learning on Graphs

arXiv: https://arxiv.org/abs/2003.05730 · HTML: https://mlsanity.com/paper/2003.05730/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2020.

## Built on by (most cited first, 30 of 42)

- [A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability](https://mlsanity.com/paper/2204.08570/) (2022)
- [Out-Of-Distribution Generalization on Graphs: A Survey](https://mlsanity.com/paper/2202.07987/) (2022)
- [Node-Level Membership Inference Attacks Against Graph Neural Networks](https://mlsanity.com/paper/2102.05429/) (2021)
- [Model Stealing Attacks Against Inductive Graph Neural Networks](https://mlsanity.com/paper/2112.08331/) (2021)
- [Single Node Injection Attack against Graph Neural Networks](https://mlsanity.com/paper/2108.13049/) (2021)
- [Understanding Structural Vulnerability in Graph Convolutional Networks](https://mlsanity.com/paper/2108.06280/) (2021)
- [A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection](https://mlsanity.com/paper/2205.10014/) (2022)
- [Projective Ranking-based GNN Evasion Attacks](https://mlsanity.com/paper/2202.12993/) (2022)
- [NetFense: Adversarial Defenses against Privacy Attacks on Neural Networks for Graph Data](https://mlsanity.com/paper/2106.11865/) (2021)
- [Spectral Adversarial Training for Robust Graph Neural Network](https://mlsanity.com/paper/2211.10896/) (2022)
- [Are Large Language Models In-Context Graph Learners?](https://mlsanity.com/paper/2502.13562/) (2025)
- [Adversarial Camouflage for Node Injection Attack on Graphs](https://mlsanity.com/paper/2208.01819/) (2022)
- [Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attack](https://mlsanity.com/paper/2202.07114/) (2022)
- [Are Gradients on Graph Structure Reliable in Gray-box Attacks?](https://mlsanity.com/paper/2208.05514/) (2022)
- [GANI: Global Attacks on Graph Neural Networks via Imperceptible Node Injections](https://mlsanity.com/paper/2210.12598/) (2022)
- [Intelligent Zero Trust Architecture for 5G/6G Networks: Principles, Challenges, and the Role of Machine Learning in the context of O-RAN](https://mlsanity.com/paper/2105.01478/) (2021)
- [GraphGallery: A Platform for Fast Benchmarking and Easy Development of Graph Neural Networks Based Intelligent Software](https://mlsanity.com/paper/2102.07933/) (2021)
- [A semantic backdoor attack against Graph Convolutional Networks](https://mlsanity.com/paper/2302.14353/) (2023)
- [Less Can Be More: Unsupervised Graph Pruning for Large-scale Dynamic Graphs](https://mlsanity.com/paper/2305.10673/) (2023)
- [SAILOR: Structural Augmentation Based Tail Node Representation Learning](https://mlsanity.com/paper/2308.06801/) (2023)
- [USER: Unsupervised Structural Entropy-based Robust Graph Neural Network](https://mlsanity.com/paper/2302.05889/) (2023)
- [Rethinking Independent Cross-Entropy Loss For Graph-Structured Data](https://mlsanity.com/paper/2405.15564/) (2024)
- [IDEA: Invariant Defense for Graph Adversarial Robustness](https://mlsanity.com/paper/2305.15792/) (2023)
- [Survey of Graph Neural Network for Internet of Things and NextG Networks](https://mlsanity.com/paper/2405.17309/) (2024)
- [Black-box Gradient Attack on Graph Neural Networks: Deeper Insights in Graph-based Attack and Defense](https://mlsanity.com/paper/2104.15061/) (2021)
- [Person Text-Image Matching via Text-Feature Interpretability Embedding and External Attack Node Implantation](https://mlsanity.com/paper/2211.08657/) (2022)
- [GUARD: Graph Universal Adversarial Defense](https://mlsanity.com/paper/2204.09803/) (2022)
- [What Does the Gradient Tell When Attacking the Graph Structure](https://mlsanity.com/paper/2208.12815/) (2022)
- [Graph Adversarial Immunization for Certifiable Robustness](https://mlsanity.com/paper/2302.08051/) (2023)
- [Graph Neural Network approaches for single-cell data: A recent overview](https://mlsanity.com/paper/2310.09561/) (2023)

## 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.