Uncertainty is not Enough: Misclassification Detection via Distribution-aware Bayesian Graph Neural Networks

Bayesian Graph Neural Networks (BGNNs) have achieved significant accomplishments in uncertainty quantification for graph-structured data, particularly in isolating epistemic uncertainty. However, traditional BGNNs primarily parameterize uncertainty over model weights and posterior predictive distributions, which leads to miscalibration under structural and attribute noise. For node classification tasks, this challenge is often noticed as confident misclassifications – underestimating prediction uncertainty on out-of-distribution (OOD) samples or under adversarial perturbations – and results in severe risks for downstream applications such as medical diagnostics and threat detection. To address this challenge, we propose Distribution-Aware Bayesian Graph Neural Networks (DA-BGNNs), a novel framework designed for robust node-level classification. DA-BGNN leverages Monte Carlo sampling alongside a distributional shift metric that explicitly measures the discrepancy between correctly classified and misclassified node representations. By combining both the post-training variational posterior and local neighborhood feature distributions, while considering localized heterophily via variational inference, our framework effectively pinpoints misclassifications produced by adversarial perturbations and topological noise. Our method demonstrated state-of-the-art (SOTA) results that consistently outperform conventional non-Bayesian GNN methods and standard BGNN baselines. Under both in-distribution and adversarial perturbation, DA-BGNN indicates an average improvement of 16% in AUPRC and reduces Expected Calibration Error (ECE) by 25% for node classification tasks. Furthermore, it enables more reliable and faithful uncertainty estimation across predictive entropy, variance, and distribution shift.