Empirical two-sample error for powered even-order Gromov-Wasserstein functionals is bounded by n^{-2/max{min(d_x,d_y),4}} up to logs when min dimension equals 4.
The space of spaces: Curvature bounds and gradient flows on the space of metric measure spaces.arXiv:1208.0434,
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Trained MPNNs factor through bounded step-graphon-signals that embed via an explicit map into disjoint caps on the n-sphere, producing a topological fingerprint for model comparison and retrieval.
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Empirical Convergence of Even-Order Gromov-Wasserstein Functionals
Empirical two-sample error for powered even-order Gromov-Wasserstein functionals is bounded by n^{-2/max{min(d_x,d_y),4}} up to logs when min dimension equals 4.
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A Topological Characterization of Graph Neural Networks via Stochastic Block Model Embeddings on the n-Sphere
Trained MPNNs factor through bounded step-graphon-signals that embed via an explicit map into disjoint caps on the n-sphere, producing a topological fingerprint for model comparison and retrieval.