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New method uses Optimal Transport for supervised graph prediction

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method for supervised graph prediction (SGP) that addresses the challenge of comparing predicted and target graphs with arbitrary node orderings. The approach utilizes Optimal Transport relaxations, specifically the Gromov-Wasserstein objective, which has been identified as the most suitable for SGP. To improve efficiency, the method amortizes the graph matching problem by using a parametric matcher based on the Sinkhorn algorithm, allowing the graph prediction module and the matcher to be learned jointly. AI

IMPACT This research may improve the accuracy and efficiency of graph-based machine learning tasks, particularly in areas like molecular structure prediction.

RANK_REASON The cluster contains an academic paper detailing a new method for supervised graph prediction. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New method uses Optimal Transport for supervised graph prediction

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The cluster contains an academic paper detailing a new method for supervised graph prediction. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Federico M\'endez, Paul Krzakala, Gabriel Melo, Charlotte Laclau, R\'emi Flamary, Florence d'Alch\'e-Buc ·

    Graph Matching Relaxations and Amortization for Supervised Graph Prediction

    arXiv:2609.15437v1 Announce Type: cross 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…

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