Learning to Predict Graphs with Fused Gromov-Wasserstein Barycenters. (arXiv:2202.03813v3 [stat.ML] UPDATED)

This paper introduces a novel and generic framework to solve the flagship
task of supervised labeled graph prediction by leveraging Optimal Transport
tools. We formulate the problem as regression with the Fused Gromov-Wasserstein
(FGW) loss and propose a predictive model relying on a FGW barycenter whose
weights depend on inputs. First we introduce a non-parametric estimator based
on kernel ridge regression for which theoretical results such as consistency
and excess risk bound are proved. Next we propose an interpretable parametric …

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