Uncertainty Modeling in Graph Neural Networks via Stochastic Differential Equations
Richard Bergna Sergio Calvo Ordoñez Felix Opolka Pietro Lio José Miguel Hernández-Lobato
We propose a novel Stochastic Differential Equation (SDE) framework to address the problem of learning uncertainty-aware representations for graph-structured data. While Graph Neural Ordinary Differential Equations (GNODEs) have shown promise in learning node representations, they lack the ability to quantify uncertainty. To address this, we introduce Latent Graph Neural Stochastic Differential Equations (LGNSDE), which enhance GNODE by embedding randomness through a Bayesian prior-posterior mechanism for epistemic uncertainty and Brownian motion for aleatoric uncertainty. By leveraging the existence and uniqueness of solutions to graph-based SDEs, we prove that the variance of the latent space bounds the variance of model outputs, thereby providing theoretically sensible guarantees for the uncertainty estimates. Furthermore, we show mathematically that LGNSDEs are robust to small perturbations in the input, maintaining stability over time. Empirical results across several benchmarks demonstrate that our framework is competitive in out-of-distribution detection, robustness to noise perturbations, and active learning, underscoring the ability of LGNSDEs to quantify uncertainty reliably.
Graph Neural Networks, Stochastic Differential Equations, Uncertainty Quantification, Bayesian Machine Learning
We introduce Latent Graph Neural SDEs (LGNSDEs), a model that quantifies uncertainty in graph-structured data while maintaining robustness to input perturbations, supported by theoretical guarantees and empirical evaluation.