Do Deep Ensembles Actually Capture Uncertainty in Graph Neural Networks?
While deep ensembles are widely considered to be the default method for uncertainty quantification in deep learning, their effectiveness for graph-structured data is often simply assumed based on successes in domains like computer vision. We investigate standard deep ensembles specifically for message-passing graph neural networks. Benchmarking across eight datasets representing varied tasks and complexities, we reveal that ensembles provide only marginal improvement over a single model. These marginal gains stem primarily from averaging out optimization noise in point predictions rather than from meaningfully better uncertainty estimates. Through an aleatoric-epistemic decomposition, we ...
| Authors | Pedro C. Vieira, Pedro Ribeiro, Viacheslav Borovitskiy |
|---|---|
| Primary category | cs.LG |
| Published | 21 May 2026 |
| Last updated at source | 13 October 2026 |
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