Training-Free Edge Sanitization Method Defends Graph Neural Networks from Structural Attacks

A new arXiv paper proposes a defense for graph neural networks that strips out attacker-inserted edges from a graph's topology before inference, requiring no retraining of the model. The method relies on kernel-complexity signals to identify edges likely introduced through adversarial manipulation, and the authors support the design with theoretical guarantees. It addresses threats that target graph structure rather than node features.