[
    {
        "id": "osp-16947",
        "type": "article-journal",
        "title": "Interpretable Hypergraph Learning via Neural Additive Models",
        "author": [
            {
                "family": "Feng",
                "given": "Shihan"
            },
            {
                "family": "Zheng",
                "given": "Xin"
            },
            {
                "family": "Yang",
                "given": "Shiyi"
            },
            {
                "family": "Wang",
                "given": "Ren"
            },
            {
                "family": "Zhong",
                "given": "Chudi"
            },
            {
                "family": "Chen",
                "given": "Can"
            }
        ],
        "URL": "https://omanscience.com/en/articles/interpretable-hypergraph-learning-via-neural-additive-models",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "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."
    }
]