A General Framework for quantifying Aleatoric and Epistemic uncertainty in Graph Neural Networks

Understand

Graph Neural Networks (GNN) provide a powerful framework that elegantly integrates Graph theory with Machine learning for modeling and analysis of networked data.

• We consider the problem of quantifying the uncertainty in predictions of GNN stemming from modeling errors and measurement uncertainty.
• We consider aleatoric uncertainty in the form of probabilistic links and noise in feature vector of nodes, while epistemic uncertainty is incorporated via a probability distribution over the model parameters.
• We propose a unified approach to treat both sources of uncertainty in a Bayesian framework, where Assumed Density Filtering is used to quantify aleatoric uncertainty and Monte Carlo dropout captures uncertainty in model parameters.