Do Higher-Order Models Win for Higher-Order Reasons? Rethinking Performance Gains in Hypergraph Learning
The research tested if higher-order hypergraph models truly need higher-order information to outperform simpler ones. By perturbing this information while keeping lower-order data intact, the study found that most performance gains persist, suggesting advantages often come from better lower-order mechanisms rather than solely exploiting higher-order details.