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A Survey of Adversarial Learning on Graphs
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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.
Forward citations
Cited by 4 Pith papers
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Budgeted Indirect Adversarial Attack on Graph-Based Anomaly Detection in Sensor Networks
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Quantifying the Noise of Structural Perturbations on Graph Adversarial Attacks
A new link-noise metric, computed from node degrees and entropy-based dissimilarity, ranks candidate adversarial edges and prunes the search space for targeted graph attacks, yielding competitive or better attack succ...
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Mitigating the Structural Bias in Graph Adversarial Defenses
De2GNN reduces degree bias in adversarial defenses by removing heterophilic links, adding homophilic links to tail nodes, and fusing a kNN feature view through node-wise attention, improving tail-node accuracy under M...
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