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arXiv · 2510.08045

Verifying Quantized GNNs With Readout Is Decidable But Highly Intractable

Artem Chernobrovkin·Marco Sälzer·François Schwarzentruber·Nicolas Troquard
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Abstract

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 it to prove that verification tasks for quantized GNNs with readout are (co)NEXPTIME-complete. This result implies that the verification of quantized GNNs is computationally intractable, prompting substantial research efforts toward ensuring the safety of GNN-based systems. We also experimentally demonstrate that quantized ACR-GNN models are lightweight while maintaining good accuracy and generalization capabilities with respect to non-quantized models.

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BibTeXRIS

Artem Chernobrovkin, Marco Sälzer, François Schwarzentruber, Nicolas Troquard. 2025-10-09. Verifying Quantized GNNs With Readout Is Decidable But Highly Intractable. https://arxiv.org/abs/2510.08045

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