A Survey of Adversarial Learning on Graphs

Abstract

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.

Preprint; 16 pages, 2 figures

References in corpus (7)

• Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation
• Batch Virtual Adversarial Training for Graph Convolutional Networks
• GraphDefense: Towards Robust Graph Convolutional Networks
• GraphSAC: Detecting anomalies in large-scale graphs
• Adversarial Defense Framework for Graph Neural Network
• Can Adversarial Network Attack be Defended?
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Cited by in corpus (14)

• A Comprehensive Survey on Deep Graph Representation Learning
• A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability
• Single Node Injection Attack against Graph Neural Networks
• Node-Level Membership Inference Attacks Against Graph Neural Networks
• A survey on Adversarial Recommender Systems: from Attack/Defense strategies to Generative Adversarial Networks
• NetFense: Adversarial Defenses against Privacy Attacks on Neural Networks for Graph Data
• Projective Ranking-based GNN Evasion Attacks
• USER: Unsupervised Structural Entropy-based Robust Graph Neural Network
• Learning to Deceive Knowledge Graph Augmented Models via Targeted Perturbation
• Black-box Gradient Attack on Graph Neural Networks: Deeper Insights in Graph-based Attack and Defense
• GraphGallery: A Platform for Fast Benchmarking and Easy Development of Graph Neural Networks Based Intelligent Software
• Adversarial Attack on Large Scale Graph
• I-GCN: Robust Graph Convolutional Network via Influence Mechanism
• DeepInsight: Interpretability Assisting Detection of Adversarial Samples on Graphs