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.
A nonparametric view of network models and Newman– Girvan and other modularities.Proceedings of the National Academy of Sciences, 106(50): 21068–21073, 2009
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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.