aiqbench analysis
TechPulse take: HyperLabel introduces an encoder-decoder framework using hypergraph neural networks to explicitly model high-order dependencies among multiple labels, overcoming the limitations of pairwise attention mechanisms in multi-label classification tasks. By integrating feature information with structural label priors through bidirectional message passing, the method achieves state-of-the-art performance on benchmark datasets, particularly improving macro-F1 scores where complex co-occurrence patterns are critical. This work advances the field by demonstrating that explicit hypergraph structures provide superior guidance for capturing intricate relationships beyond standard implicit learning approaches.
HyperLabel: Multi-Label Classification via Hypergraph-Based Label Correlation Modeling
arXiv:2609.32276v1 Announce Type: new 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 .
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 this https URL .
Machine Learning (cs.LG)
arXiv:2609.32276 [cs.LG]
(or arXiv:2609.32276v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.32276 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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