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cs.LG2024
CUQ-GNN: Committee-based Graph Uncertainty Quantification using Posterior Networks
Clemens Damke, Eyke Hüllermeier
In this work, we study the influence of domain-specific characteristics when defining a meaningful notion of predictive uncertainty on graph data. Previously, the so-called Graph P…
cs.LG2024
Linear Opinion Pooling for Uncertainty Quantification on Graphs
Clemens Damke, Eyke Hüllermeier
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…