Abstract
Hypergraphs offer a natural framework for modeling networked data, where dependencies among entities are governed by higher-order interactions. While hypergraph learning methods such as hypergraph neural networks have demonstrated remarkable predictive performance, most existing approaches rely on black-box message-passing architectures, making it difficult to disentangle the contributions of node attributes and higher-order structural information. To address this challenge, we introduce the hypergraph neural additive network (HGNAN), an inherently interpretable framework for learning on hypergraph-structured data. HGNAN extends classical neural additive models to higher-order relational data by integrating feature-wise nonlinear decomposition with hypergraph-aware structural aggregation, enabling transparent prediction for both node- and hyperedge-level tasks. Extensive experiments on benchmark datasets demonstrate that HGNAN achieves performance comparable with state-of-the-art hypergraph learning methods while providing intrinsic and meaningful interpretability.
Keywords
Publication details
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Cite this article
APA 7
Feng, S., Zheng, X., Yang, S., Wang, R., Zhong, C., & Chen, C. (2026). Interpretable Hypergraph Learning via Neural Additive Models. https://omanscience.com/en/articles/interpretable-hypergraph-learning-via-neural-additive-models
MLA 9
Feng, Shihan, et al. "Interpretable Hypergraph Learning via Neural Additive Models." https://omanscience.com/en/articles/interpretable-hypergraph-learning-via-neural-additive-models.
Chicago (author–date)
Feng, Shihan, Xin Zheng, Shiyi Yang, Ren Wang, Chudi Zhong, and Can Chen. 2026. "Interpretable Hypergraph Learning via Neural Additive Models." https://omanscience.com/en/articles/interpretable-hypergraph-learning-via-neural-additive-models.
Harvard
Feng, S., Zheng, X., Yang, S., Wang, R., Zhong, C. and Chen, C. (2026) 'Interpretable Hypergraph Learning via Neural Additive Models', Available at: https://omanscience.com/en/articles/interpretable-hypergraph-learning-via-neural-additive-models.
Vancouver
Feng S, Zheng X, Yang S, Wang R, Zhong C, Chen C. Interpretable Hypergraph Learning via Neural Additive Models. https://omanscience.com/en/articles/interpretable-hypergraph-learning-via-neural-additive-models
IEEE
S. Feng, X. Zheng, S. Yang, R. Wang, C. Zhong, and C. Chen, "Interpretable Hypergraph Learning via Neural Additive Models," https://omanscience.com/en/articles/interpretable-hypergraph-learning-via-neural-additive-models.