3 papers
cs.LO2026
Verifying Quantized GNNs With Readout Is Decidable But Highly Intractable
Artem Chernobrovkin, Marco Sälzer, François Schwarzentruber +1
We introduce a logical language for reasoning about quantized aggregate-combine graph neural networks with global readout (ACR-GNNs). We provide a logical characterization and use…
cs.LO2025
Verifying Quantized Graph Neural Networks is PSPACE-complete
Marco Sälzer, François Schwarzentruber, Nicolas Troquard
In this paper, we investigate the verification of quantized Graph Neural Networks (GNNs), where some fixed-width arithmetic is used to represent numbers. We introduce the linear-co…
cs.AI2025
A Logic for Reasoning About Aggregate-Combine Graph Neural Networks
Pierre Nunn, Marco Sälzer, François Schwarzentruber +1
We propose a modal logic in which counting modalities appear in linear inequalities. We show that each formula can be transformed into an equivalent graph neural network (GNN). We…