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

2026-09-26

AI Summary

This study addresses the challenge of capturing high-order label dependencies in multi-label classification by proposing the HyperLabel framework. The method constructs a label hypergraph with samples as hyperedges, providing structural priors that transcend pairwise interactions. Building upon hypergraph neural networks, an encoder-decoder architecture is designed to achieve unified cross-modal fusion of feature and structural information through cross-attention and bidirectional message passing mechanisms. Experimental results demonstrate that the proposed framework achieves state-of-the-art performance across seven benchmark datasets. Notably, it yields substantial improvements of 10.3% and 8.2% in Macro-F1 on the Delicious and Bibtex datasets, respectively. These findings validate the effectiveness of explicitly modeling complex label co-occurrence patterns for advancing multi-label classification performance.

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 .

Problem

• Multi-label classification
• Label correlation
• High-order dependencies
• Hypergraph

Innovation

• Multi-Label Classification
• Hypergraph Neural Networks
• Label Correlation Modeling
• Cross-Modal Learning
• High-order Dependencies