Linear Opinion Pooling for Uncertainty Quantification on Graphs

Damke, Clemens und Hüllermeier, Eyke (15. July 2024): Linear Opinion Pooling for Uncertainty Quantification on Graphs. 40th Conference on Uncertainty in Artificial Intelligence, Barcelona, Spain, 15. - 19. July 2024. Kiyavash, Negar und Mooij, Joris M. (eds.) : Proceedings of Machine Learning Research. Vol. 244 PMLR. pp. 919-929

Abstract

We address the problem of uncertainty quantification for graph-structured data, or, more specifically, the problem to quantify the predictive uncertainty in (semi-supervised) node classification. Key questions in this regard concern the distinction between two different types of uncertainty, aleatoric and epistemic, and how to support uncertainty quantification by leveraging the structural information provided by the graph topology. Challenging assumptions and postulates of state-of-the-art methods, we propose a novel approach that represents (epistemic) uncertainty in terms of mixtures of Dirichlet distributions and refers to the established principle of linear opinion pooling for propagating information between neighbored nodes in the graph. The effectiveness of this approach is demonstrated in a series of experiments on a variety of graph-structured datasets.

• Item Type: Conference or Workshop Item (Paper)
• Faculties: Mathematics, Computer Science and Statistics > Computer Science > Artificial Intelligence and Machine Learning
• Subjects: 000 Computer science, information and general works > 004 Data processing computer science