Topology-Guided Adversarial Attack on Graph Neural Networks Based on Dynamic Node Selection

Summary

Researchers propose DNS-TGA, a topology-guided adversarial attack on Graph Neural Networks that works in black-box settings. The method builds a graph representation aligned with the victim model using a similarity measure against a shadow model, then uses the volcanic eruption algorithm (VEA) to select critical nodes. A dual-discriminator built from the shadow and victim models generates perturbation samples. The authors report superior attack performance across six representative GNN models, plus scalability and transferability.

Topics

• Adversarial machine learning

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