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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.
NNV3 is presented, the latest version of the Neural Network Verification tool, a MATLAB framework for formal verification of deep learning models and learning-enabled cyber-physical systems, demonstrating NNV's maturation into a comprehensive, robust, and accessible verification tool for a diverse range of AI systems.
RAD is proposed, a rule-augmented relational anomaly detector that combines heterogeneous graph representation learning with refined symbolic rule signals and improves anomaly ranking over flattened tabular detectors and relational baselines under natural class imbalance.
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