Reachability-Based Formal Verification of Graph Neural Networks with Node and Edge Features
The results show that GNNV provides tighter robustness guarantees than CORA on graph classification models with ReLU-based activations and, for the first time, delivers edge-aware robustness guarantees for GINE-based PF and OPF models under joint node and edge perturbations.
Anne M. Tumlin, Ben Wooding, Zhenxuan Shao et al.
· SAIV · 1 citation