Conformal Graph Prediction with Z-Gromov Wasserstein Distances
Abstract
Supervised graph prediction addresses regression problems where the outputs are structured graphs. Although several approaches exist for graph-valued prediction, principled uncertainty quantification remains limited. We propose a conformal prediction framework for graph-valued outputs, providing distribution-free coverage guarantees in structured output spaces. Our method defines nonconformity via the Z-Gromov-Wasserstein distance, instantiated in practice through Fused Gromov-Wasserstein (FGW), enabling permutation invariant comparison between predicted and candidate graphs. To obtain adaptive prediction sets, we introduce Score Conformalized Quantile Regression (SCQR), an extension of Conformalized Quantile Regression (CQR) to handle complex output spaces such as graph-valued outputs. We evaluate the proposed approach on a synthetic task.
Keywords
Cite
@article{arxiv.2603.02460,
title = {Conformal Graph Prediction with Z-Gromov Wasserstein Distances},
author = {Gabriel Melo and Thibaut de Saivre and Anna Calissano and Florence d'Alché-Buc},
journal= {arXiv preprint arXiv:2603.02460},
year = {2026}
}