Quantile-Free Uncertainty Quantification in Graph Neural Networks
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 coverage and interval width without requiring quantile inputs or post-processing. QpiGNN employs a dual-head architecture that decouples prediction and unc...
| Authors | Soyoung park, Hwanjun Song, Sungsu Lim |
|---|---|
| Primary category | cs.LG |
| Published | 6 May 2026 |
| Last updated at source | 12 June 2026 |
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