GraphUAT: Uncertainty Attribution in Graph Neural Networks

Models that recognize when their predictions are unreliable inspire greater trust than those that make confident but incorrect decisions. While recent advances enable uncertainty quantification in GNNs, understanding why a model is uncertain, whether due to noisy features, conflicting neighborhoods, or sparse training coverage, remains unsolved. We address this gap with GraphUAT, the first uncertainty attribution framework for GNNs, which identifies the sources of uncertainty rather than aiming to improve its quantification. GraphUAT employs a teacher-student paradigm, where the trained GNN's behavior is distilled into an uncertainty-aware student that disentangles aleatoric and epistemic uncertainties. We then use targeted probes to trace the student's uncertainty back to the responsible features and structural components. Experiments across benchmarks show that removing the identified elements consistently reduces the teacher's uncertainty, validating the faithfulness of our attributions.