When does distribution shift break graph neural networks calibration?
Graph neural networks (GNNs) are increasingly deployed in real-world applications where distribution shift is un-avoidable. However, how such shifts affect model calibration, defined as the agreement between predictive confidence and actual accuracy, remains poorly understood, and existing graph calibration methods typically rely on labeled validation data from the deployment distribution. In this work, I present the first closed-form theoretical characterization of GNN calibration under distribution shift. I show that calibration is governed by a single scalar quantity that explicitly depends on structural changes between the source and target graphs, as well as feature quality. This cha...
| Authors | Abderaouf Bahi |
|---|---|
| Primary category | cs.LG |
| Published | 12 July 2026 |
| Last updated at source | 12 July 2026 |
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