Uncertainty Quantification on Graph Learning: A Survey

arXiv:2404.14642v1 Announce Type: new
Abstract: 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 …

abstract arxiv capabilities challenges cs.lg decision fields gnns graph graph learning graph neural networks making networks neural networks quantification survey testing training type uncertainty