Uncertainty Aware Graph

Uncertainty-aware graph learning aims to address the inherent uncertainties in graph data and models, improving the reliability and robustness of graph-based predictions. Current research focuses on developing methods to quantify and incorporate uncertainty into various graph neural network (GNN) architectures, including Bayesian approaches, committee methods, and techniques leveraging normalizing flows. These advancements are crucial for enhancing the trustworthiness of GNNs in safety-critical applications and improving the accuracy of graph-based analyses across diverse domains, such as knowledge graph completion and causal inference. The field is also exploring efficient algorithms for handling large-scale graphs and incorporating uncertainty into graph generation and hypothesis testing.