Rethinking Epistemic Uncertainty in Node Classification through Information Growth

The paper introduces a statistical framework to test whether epistemic uncertainty in node classification decreases as more information about the data‑generating process becomes available. It shows that existing graph evidential deep learning methods fail to meet a consistency criterion under this framework, because they regulate uncertainty via hyperparameters rather than data uncertainty. The authors propose graph bootstrap ensembles, which reduce epistemic uncertainty more effectively than standard deep ensembles and satisfy the consistency criterion in controlled experiments.

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