Interpretable Hypergraph Learning via Neural Additive Models
The Hypergraph Neural Additive Network (HGNAN) is an interpretable framework for learning from hypergraph data. It extends neural additive models by combining feature decomposition with structure-aware aggregation to achieve state-of-the-art performance. This method allows researchers to understand how node and hyperedge features contribute to predictions through transparent, intrinsic explanations.