Semi-relaxed Gromov-Wasserstein framework for unlabeled network learning achieves O(1/n) gap to deterministic assignments plus consistency and minimax rates for SBM and graphons.
Estimating network edge probabilities by neigh- bourhood smoothing.Biometrika, 104(4):771–783, December 2017
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Network Learning with Semi-relaxed Gromov-Wasserstein
Semi-relaxed Gromov-Wasserstein framework for unlabeled network learning achieves O(1/n) gap to deterministic assignments plus consistency and minimax rates for SBM and graphons.