arXiv:2605.04847v2 Announce Type: replace-cross Abstract: Uncertainty quantification (UQ) in graph neural networks (GNNs) is crucial in high-stakes domains but remains a significant challenge. In graph settings, message passing often relies on strong assumptions such as exchangeability, which are rarely satisfied in practice, and achieving reliable UQ typically requires costly resampling or post-hoc calibration. To address these issues, we introduce Quantile-free Prediction Interval GNN (QpiGNN), a framework that builds on quantile regression (QR) to enable GNN-based UQ by directly optimizing
The increasing deployment of GNNs in high-stakes domains necessitates robust uncertainty quantification, driving research into more reliable and efficient methods.
Reliable UQ in GNNs is critical for trusted AI applications, especially in areas where model errors can have significant consequences.
This research introduces a novel, quantile-free approach to UQ in GNNs, potentially leading to more accurate and less computationally intensive uncertainty estimations.
- · AI safety researchers
- · High-stakes AI industries
- · GNN developers
- · Healthcare AI
- · AI systems lacking UQ
- · Methods relying on costly resampling
- · Industries with low AI trust
Improved reliability and explainability of graph-based AI models will accelerate their adoption in critical applications.
Enhanced UQ capabilities could reduce regulatory hurdles for AI deployment in sensitive sectors, fostering wider innovation.
The development of robust UQ frameworks for GNNs might inspire similar advancements across other complex deep learning architectures, boosting overall AI trustworthiness.
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Read at arXiv cs.AI