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GNNGuard: Defending Graph Neural Networks against Adversarial Attacks
[article]
2020
arXiv
pre-print
Deep learning methods for graphs achieve remarkable performance across a variety of domains. However, recent findings indicate that small, unnoticeable perturbations of graph structure can catastrophically reduce performance of even the strongest and most popular Graph Neural Networks (GNNs). Here, we develop GNNGuard, a general algorithm to defend against a variety of training-time attacks that perturb the discrete graph structure. GNNGuard can be straight-forwardly incorporated into any GNN.
arXiv:2006.08149v3
fatcat:xkwcnoezwffgvp642hfcixjqwa