4 papers
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…
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…
On Representing Humans' Soft-Ethics Preferences As Dispositions
Donatella Donati, Ziba Assadi, Simone Gozzano +2
The aim of this paper is to represent humans' soft-ethical preferences by means of dispositional properties. We begin by examining real-life situations, termed as scenarios, that i…
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…