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arXiv · 2609.32276

HyperLabel: Multi-Label Classification via Hypergraph-Based Label Correlation Modeling

Peiyu Zhang·Heng Ping·Nikos Kanakaris·Yucheng Zhao·Shixuan Li·Wei Yang·Xiongye Xiao·Paul Bogdan
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Abstract

Multi-label classification (MLC) requires predicting multiple relevant labels for each instance, where a central challenge is modeling complex label dependencies arising from co-occurrence patterns. Existing approaches are limited in capturing high-order label correlations, relying on implicit learning through contrastive objectives or pairwise attention mechanisms without structural guidance. We propose HyperLabel, an encoder-decoder framework that explicitly models label dependencies through hypergraph neural networks. Our contributions are twofold: (i) We construct a label hypergraph where sample-defined hyperedges naturally encode multi-way co-occurrence patterns, providing explicit structural prior knowledge that captures relationships beyond pairwise interactions. (ii) We propose a unified cross-modal learning approach where HGNN+ performs bidirectional message passing to integrate feature information with label structure, and a shared cross-attention decoder processes both modalities through complementary learning objectives. Extensive experiments on seven benchmark datasets demonstrate that HyperLabel achieves state-of-the-art performance, with particularly significant improvements on macro-F1 scores (+10.3% on Delicious, +8.2% on Bibtex), validating that explicit hypergraph structure effectively captures complex label relationships. The code is available at https://github.com/iZHpy/Multi-label_hypergraph .

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Peiyu Zhang, Heng Ping, Nikos Kanakaris, Yucheng Zhao, Shixuan Li, Wei Yang, Xiongye Xiao, Paul Bogdan. 2026-09-26. HyperLabel: Multi-Label Classification via Hypergraph-Based Label Correlation Modeling. https://arxiv.org/abs/2609.32276

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