[
    {
        "id": "osp-16871",
        "type": "article-journal",
        "title": "Do Higher-Order Models Win for Higher-Order Reasons? Rethinking Performance Gains in Hypergraph Learning",
        "author": [
            {
                "family": "Bu",
                "given": "Fanchen"
            },
            {
                "family": "Li",
                "given": "Fan"
            },
            {
                "family": "Lee",
                "given": "Geon"
            },
            {
                "family": "Kim",
                "given": "Sunwoo"
            },
            {
                "family": "Wang",
                "given": "Xiaoyang"
            },
            {
                "family": "Lambiotte",
                "given": "Renaud"
            },
            {
                "family": "Shin",
                "given": "Kijung"
            }
        ],
        "URL": "https://omanscience.com/en/articles/do-higher-order-models-win-for-higher-order-reasons-rethinking-performance-gains-in-hypergraph-learning",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Higher-order models (e.g., hypergraph neural networks) often outperform lower-order baselines on hypergraph learning benchmarks, and their advantages are commonly attributed to their ability to exploit higher-order information. However, better performance alone does not establish this explanation. We therefore ask: Do higher-order models win for higher-order reasons? To investigate this question, we introduce a controlled performance-attribution framework that perturbs higher-order information while preserving the lower-order, i.e., pairwise, information. Across 25 commonly used hypergraph learning benchmarks spanning three tasks, we frequently observe an intriguing pattern: higher-order models originally outperform lower-order baselines, yet retain most of their advantage after perturbation. This suggests that much of the observed advantage remains achievable without the higher-order information. We then investigate potential lower-order explanations for these remaining gaps. We find that simple additions to a lower-order baseline, e.g., richer pairwise weighting, more steps of pairwise feature propagation, and normalization, reduce the remaining performance gaps, supporting lower-order explanations for part of the observed advantage. Our analysis calls for the hypergraph learning community to rethink performance attribution by distinguishing performance gains from their explanations, adopt stronger lower-order baselines, and use suitable benchmarks that better test the value of higher-order information."
    }
]