Interpretable structural temperature scaling for calibrating graph neural networks and point-cloud segmentation

An interpretable approach targets calibration in graph and point-cloud segmentation models.

Graph-aware calibrators make graph neural networks (GNNs) more trustworthy by predicting a temperature for each node, usually with auxiliary networks whose decisions are hard to inspect.

ArXiv CS.LG2026-11-03