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Graph Matching Relaxations and Amortization for Supervised Graph Prediction

This story is from 2026-09-15. It is preserved in the archive; the latest stories are on the live feed.

arXiv:2609.15437v1 Announce Type: new Abstract: End-to-end Supervised Graph Prediction (SGP) requires a permutation-invariant loss to compare predicted and target graphs with arbitrary node orderings. Such losses typically involve a costly graph-matching problem. We first study three Optimal Transp…

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  1. 2026-09-15 04:00 · arXiv stat.ML
    Graph Matching Relaxations and Amortization for Supervised Graph Prediction

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