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Unsupervised Multi-Scale Gromov-Wasserstein Hypergraph Alignment

Paper recorded by Signals 4 on 2026-08-30 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-08-30 on arXiv · recorded by Signals 4 on 2026-09-01

Category: cs.LG · 机器学习 · first seen 2026-09-01

Abstract

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

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#187 most recent of 215 cs.LG papers we have recorded · ↑ newer: Reward-guided Fine-Tuning of One-Step Generative Models via Wasserstei · ↓ older: Cross-lingual Functional Vectors for Emotion Detection in Large Langua
Cite this page: Unsupervised Multi-Scale Gromov-Wasserstein Hypergraph Alignment: the #187 most recent of 215 cs.LG papers we have recorded (as of 2026-08-30). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/unsupervised-multi-scale-gromov-wasserstein-hypergraph-alignment.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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