الملخص
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.
الكلمات المفتاحية
بيانات النشر
- المجلة
- غير متاح
- وصول مفتوح
- وصول مفتوح أخضر
اقتبس هذه المقالة
APA 7
Bu, F., Li, F., Lee, G., Kim, S., Wang, X., Lambiotte, R., & Shin, K. (2026). Do Higher-Order Models Win for Higher-Order Reasons? Rethinking Performance Gains in Hypergraph Learning. https://omanscience.com/ar/articles/do-higher-order-models-win-for-higher-order-reasons-rethinking-performance-gains-in-hypergraph-learning
MLA 9
Bu, Fanchen, et al. "Do Higher-Order Models Win for Higher-Order Reasons? Rethinking Performance Gains in Hypergraph Learning." https://omanscience.com/ar/articles/do-higher-order-models-win-for-higher-order-reasons-rethinking-performance-gains-in-hypergraph-learning.
شيكاغو (المؤلف–التاريخ)
Bu, Fanchen, Fan Li, Geon Lee, Sunwoo Kim, Xiaoyang Wang, Renaud Lambiotte, and Kijung Shin. 2026. "Do Higher-Order Models Win for Higher-Order Reasons? Rethinking Performance Gains in Hypergraph Learning." https://omanscience.com/ar/articles/do-higher-order-models-win-for-higher-order-reasons-rethinking-performance-gains-in-hypergraph-learning.
هارفارد
Bu, F., Li, F., Lee, G., Kim, S., Wang, X., Lambiotte, R. and Shin, K. (2026) 'Do Higher-Order Models Win for Higher-Order Reasons? Rethinking Performance Gains in Hypergraph Learning', Available at: https://omanscience.com/ar/articles/do-higher-order-models-win-for-higher-order-reasons-rethinking-performance-gains-in-hypergraph-learning.
فانكوفر
Bu F, Li F, Lee G, Kim S, Wang X, Lambiotte R, et al. Do Higher-Order Models Win for Higher-Order Reasons? Rethinking Performance Gains in Hypergraph Learning. https://omanscience.com/ar/articles/do-higher-order-models-win-for-higher-order-reasons-rethinking-performance-gains-in-hypergraph-learning
IEEE
F. Bu, F. Li, G. Lee, S. Kim, X. Wang, R. Lambiotte, and K. Shin, "Do Higher-Order Models Win for Higher-Order Reasons? Rethinking Performance Gains in Hypergraph Learning," https://omanscience.com/ar/articles/do-higher-order-models-win-for-higher-order-reasons-rethinking-performance-gains-in-hypergraph-learning.