  Predicting edge weights on graphs has various applications, from
transportation systems to social networks. This paper describes a Graph Neural
Network (GNN) approach for edge weight prediction with guaranteed coverage. We
leverage conformal prediction to calibrate the GNN outputs and produce valid
prediction intervals. We handle data heteroscedasticity through error
reweighting and Conformalized Quantile Regression (CQR). We compare the
performance of our method against baseline techniques on real-world
transportation datasets. Our approach has better coverage and efficiency than
all baselines and showcases robustness and adaptability.
