Verifying Quantized GNNs With Readout Is Decidable But Highly Intractable
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866917435604467712 |
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| author | Chernobrovkin, Artem Sälzer, Marco Schwarzentruber, François Troquard, Nicolas |
| author_facet | Chernobrovkin, Artem Sälzer, Marco Schwarzentruber, François Troquard, Nicolas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_08045 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Verifying Quantized GNNs With Readout Is Decidable But Highly Intractable Chernobrovkin, Artem Sälzer, Marco Schwarzentruber, François Troquard, Nicolas Logic in Computer Science Artificial Intelligence Computational Complexity Machine Learning 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. |
| title | Verifying Quantized GNNs With Readout Is Decidable But Highly Intractable |
| topic | Logic in Computer Science Artificial Intelligence Computational Complexity Machine Learning |
| url | https://arxiv.org/abs/2510.08045 |