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Unsupervised Multi-Scale Gromov-Wasserstein Hypergraph Alignment
We study unsupervised hypergraph alignment, where the goal is to infer node correspondences between two hypergraphs using only structural information, without node features, labels, seed matches, or side information. Direct higher-order formulations can represent hyperedge interactions faithfully, b
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