A Survey of Adversarial Learning on Graphs

2020/03/10 by Liang Chen, Jintang Li, Chen, Liang +14 · 5 citations
Computer Science · Physics and Astronomy · #Advanced Graph Neural Networks #Adversarial Robustness in Machine Learning #Complex Network Analysis Techniques

paper · pdf · doi:10.48550/arxiv.2003.05730

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.

Cited by

• PEANUT: Perturbations by Eigenvector Alignment for Attacking Graph Neural Networks Under Topology-Driven Message Passing
• Topologically-Stabilized Graph Neural Networks: Empirical Robustness Across Domains
• Black-box Gradient Attack on Graph Neural Networks: Deeper Insights in Graph-based Attack and Defense
• Graph neural networks: A review of methods and applications
• Unveiling the Vulnerability of Graph-LLMs: An Interpretable Multi-Dimensional Adversarial Attack on TAGs
• Node-Level Membership Inference Attacks Against Graph Neural Networks

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