Verifying Quantized GNNs With Readout Is Decidable But Highly Intractable
📰 ArXiv cs.AI
Verifying quantized GNNs with readout is decidable but highly intractable, making safety assurance a challenging task
Action Steps
- Define a logical language for reasoning about quantized ACR-GNNs using tools like Coq or Isabelle
- Formulate verification tasks for quantized GNNs with readout using the defined logical language
- Apply model checking techniques to verify quantized GNNs with readout using tools like PRISM or SPIN
- Analyze the computational complexity of verification tasks for quantized GNNs with readout using complexity theory
- Develop approximation techniques or heuristics to mitigate the intractability of verification tasks for quantized GNNs with readout
Who Needs to Know This
Researchers and developers working with graph neural networks (GNNs) and formal verification methods can benefit from understanding the decidability and intractability of quantized GNNs with readout
Key Insight
💡 The verification of quantized GNNs with readout is (co)NEXPTIME-complete, making it computationally intractable
Share This
💡 Verifying quantized GNNs with readout is decidable but highly intractable! 🤖💻
Key Takeaways
Verifying quantized GNNs with readout is decidable but highly intractable, making safety assurance a challenging task
Full Article
Title: Verifying Quantized GNNs With Readout Is Decidable But Highly Intractable
Abstract:
arXiv:2510.08045v2 Announce Type: replace-cross 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-ba
Abstract:
arXiv:2510.08045v2 Announce Type: replace-cross 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-ba
DeepCamp AI