Unsupervised Multi-Scale Gromov-Wasserstein Hypergraph Alignment

arXiv:2608.29635 · cs.LG, cs.SI · Submitted 2026-08-30 · Read on arXiv

cs.LG, cs.SI

Submitted: 2026-08-30

Updated: 2026-08-30

Comments: Accepted at ICDM 2026

Code: https://github.com/constantinosskitsas/Framework

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Hypergraph — 110× in this paper · explained in 2 episodes
• Alignment — 65× in this paper · explained in 1 episode
• Benchmark — 19× in this paper · explained in 2 episodes
• Accuracy — 17× in this paper · explained in 1 episode
• Aggregation — 11× in this paper · explained in 1 episode
• Alignment Problem — 6× in this paper · explained in 1 episode
• Optimal Transport — 5× in this paper · explained in 5 episodes
• Consistency — 4× in this paper · explained in 3 episodes
• Quadratic Assignment Problem — 4× in this paper
• Robustness — 4× in this paper · explained in 5 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Clustering — 3× in this paper · explained in 2 episodes
• Wasserstein Distance — 3× in this paper · explained in 4 episodes
• Alignment Methods — 2× in this paper · explained in 1 episode
• Embeddings — 2× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Adversarial Training — 1× in this paper · explained in 1 episode
• Combinatorial Optimization — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes

Sources

• CURSOR: Scalable Mixed-Order Hypergraph Matching with CUR Decomposition
• Deep Hypergraph Structure Learning

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