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arXiv · 2502.16244

Verifying Quantized Graph Neural Networks is PSPACE-complete

Marco Sälzer·François Schwarzentruber·Nicolas Troquard
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Abstract

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-constrained validity (LVP) problem for verifying GNNs properties, and provide an efficient translation from LVP instances into a logical language. We show that LVP is in PSPACE, for any reasonable activation functions. We provide a proof system. We also prove PSPACE-hardness, indicating that while reasoning about quantized GNNs is feasible, it remains generally computationally challenging.

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Marco Sälzer, François Schwarzentruber, Nicolas Troquard. 2025-02-22. Verifying Quantized Graph Neural Networks is PSPACE-complete. https://arxiv.org/abs/2502.16244

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