SOURCE-LINKED INTELLIGENCE
Reachability-Based Formal Verification of Graph Neural Networks with Node and Edge Features
Graph neural networks (GNNs) have become a prominent approach for developing fast, topology-aware surrogates in electric power systems, supporting tasks such as power flow (PF) analysis, optimal power flow (OPF) estimation, and cascading failure analysis (CFA). Despite this growing use, formally verifying GNN-based models remains challenging, with existing methods limited in scope. We extend the neural network verification (NNV) framework to graph-structured inputs through GraphStar sets, a generalization of Star sets that captures uncertainty over both node and edge features. This extension e
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-24T16:29:32.000Z
First collected: 2026-09-25T06:12:46.948Z. This is not the publication date.