GHAttack: Generative Adversarial Attacks on Heterogeneous Graph Neural Networks

Summary

GHAttack is a generative adversarial attack method against heterogeneous graph neural networks (HGNNs). A trained perturbation generator produces a perturbation for each target node in a single forward pass, modifying edges across heterogeneous relations to degrade predictions on target nodes. The authors report high efficiency and effectiveness across ten HGNNs and six datasets.

Topics

• Adversarial machine learning

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