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Bandits for Structure Perturbation-Based Black-Box Attacks To Graph Neural Networks With Theoretical Guarantees

CVPR 2022 · Graph learning › GNN architectures · approach: Robustness, privacy, and fairness of GNNs · 17 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use bandit feedback to guide structure perturbations instead of the RL attackBlack-box GNN attacks · Cora, Citeseer, Pubmed, MNIST, and CIFAR10attack success rate: 10%-20% higherHelped
Use bandit feedback to guide structure perturbations instead of the RL attackBlack-box GNN attacks · Citation graphs and image graphsqueries for comparable attack performance: 1.5-3x queriesHelped
Increase the maximum number of perturbed edgesBandit attack · Citation and image graphsattack success rateHelped
Increase the query budgetBandit attack · Citation and image graphsattack success rateHelped
Increase the total cost budgetBandit attack · GCN on citation graphsattack success rateHelped
Increase the cost budget after it is sufficient for the permitted edge changes (ablation)Bandit attack · GCN on citation graphsattack success rateNo clear effect
Increase projection scale α (ablation)Bandit attack · Cora, Citeseer, Pubmed, MNIST, and CIFAR10attack success rateHelped
Increase update step δ (ablation)Bandit attack · Cora, Citeseer, Pubmed, MNIST, and CIFAR10attack success rateHurt
Vary α or δ across the tested values (ablation)Bandit attack · Node and graph classificationattack success rate variation: no more than 10%No clear effect

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