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Graph prediction models struggle to quantify uncertainty. A new approach uses Z-Gromov-Wasserstein distances for conformal graph prediction, which could improve uncertainty estimates. This might help in applications where graph outputs are critical, but we don't know yet how well it works in practice.
“arXiv:2603.02460v5 Announce Type: replace-cross Abstract: Supervised graph prediction addresses regression problems where the outputs are structured graphs. Although several approaches exist for graph-valued prediction, principled uncertain…”
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Z-Gromov-Wasserstein distances, arXiv:2603.02460v5
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Aug 8, 2026