Extends a CRM-based sparse graph model from vertex-exchangeable to edge-exchangeable framework to achieve extreme sparsity where edges scale near-linearly with nodes.
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2026 2verdicts
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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.
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A Generative Model for Extremely Sparse Edge-Exchangeable Networks
Extends a CRM-based sparse graph model from vertex-exchangeable to edge-exchangeable framework to achieve extreme sparsity where edges scale near-linearly with nodes.