SOURCE-LINKED INTELLIGENCE

Unsupervised Multi-Scale Gromov-Wasserstein Hypergraph Alignment

arXiv · AI, language, vision and robotics · article · Aug 30, 2026 · UTC

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, but they can be computationally demanding and cumbersome for non-uniform hypergraphs. Graph-reduction approaches introduce a different challenge: clique expansions keep the alignment problem on the original node set but collapse all hyperedge evidence into one pairwise graph, whereas bipartite expans

Read original source ↗ Open in workspace

recordType

paper

region

Global