GHAttack: Generative Adversarial Attacks on Heterogeneous Graph Neural Networks

This research paper introduces GHAttack, a new method for attacking heterogeneous graph neural networks, or HGNNs (AI systems that learn from complex data structures with multiple types of relationships). Instead of slowly computing attacks through complicated math problems, GHAttack uses a generative model (an AI trained to create outputs) to quickly generate perturbations (small modifications) that damage how well HGNNs make predictions on target nodes. The authors tested their method on multiple HGNNs and datasets to show it works efficiently and effectively.