  Graphical models, including Graph Neural Networks (GNNs) and Probabilistic
Graphical Models (PGMs), have demonstrated their exceptional capabilities
across numerous fields. These models necessitate effective uncertainty
quantification to ensure reliable decision-making amid the challenges posed by
model training discrepancies and unpredictable testing scenarios. This survey
examines recent works that address uncertainty quantification within the model
architectures, training, and inference of GNNs and PGMs. We aim to provide an
overview of the current landscape of uncertainty in graphical models by
organizing the recent methods into uncertainty representation and handling. By
summarizing state-of-the-art methods, this survey seeks to deepen the
understanding of uncertainty quantification in graphical models, thereby
increasing their effectiveness and safety in critical applications.
