AI 中文总结
提出HyperLabel框架，利用超图神经网络显式建模多标签间的复杂依赖关系，通过双向消息传递和交叉注意力解码器整合特征与标签结构，在七个基准数据集上取得最优性能，宏F1分数显著提升。
AI 中文摘要
多标签分类（MLC）要求为每个实例预测多个相关标签，其核心挑战在于建模由共现模式产生的复杂标签依赖关系。现有方法在捕获高阶标签相关性方面存在局限，它们依赖通过对比目标或成对注意力机制进行隐式学习，缺乏结构引导。我们提出HyperLabel，一种通过超图神经网络显式建模标签依赖关系的编码器-解码器框架。我们的贡献有两方面：（i）我们构建一个标签超图，其中样本定义的超边自然地编码多路共现模式，提供显式的结构先验知识，能够捕获超越成对交互的关系。（ii）我们提出一种统一的跨模态学习方法，其中HGNN+执行双向消息传递以整合特征信息与标签结构，并且一个共享的交叉注意力解码器通过互补的学习目标处理两种模态。在七个基准数据集上的大量实验表明，HyperLabel达到了最先进的性能，特别是在宏F1分数上有显著提升（在Delicious上+10.3%，在Bibtex上+8.2%），验证了显式超图结构能有效捕获复杂的标签关系。代码可在该https URL获取。
英文摘要
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 .
Comments15 pages, 3 figures
