GNNGuard: Defending Graph Neural Networks against Adversarial Attacks

15 Jun 2020Xiang ZhangMarinka Zitnik

Deep learning methods for graphs achieve remarkable performance on many tasks. However, despite the proliferation of such methods and their success, 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)... (read more)

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