Intervention-Verifiable Hierarchical Self-Explaining Hypergraph Nueral Networks

Hypergraph neural networks effectively capture higher-order interactions, but identifying the structural basis of their predictions remains challenging. Post-hoc explainers attribute predictions to explanation subhypergraphs or hyperedge subsets after training without establishing whether the identified structure drives the output. To this end, we introduce Intervention-Verifiable Hierarchical Self-Explaining Hypergraph Neural Network (IV-SENN), a framework that enables direct verification of prediction changes under structural interventions on hypergraphs. IV-SENN represents higher-order hypergraph structure through h-motifs and captures recurring feature patterns using learnable feature prototypes. The additive concept-interaction head decomposes each class logit into linear, compositional, and quadratic interaction terms over the full concept encoding. With the trained head held fixed, IV-SENN exactly decomposes intervention-induced logit changes into structural and feature-linked contributions for counterfactual verification. Global explanations identify class-critical h-motifs from the learned structural weights of the additive concept-interaction head. Local explanations report signed structural and feature concept contributions to individual node predictions and ground influential structural concepts in concrete h-motif instances. Across four real-world datasets, IV-SENN remains competitive in node classification while attaining the highest Fid_KL and lowest SHD. On N-SYNTH, its estimated intervention effects achieve a Pearson correlation of 0.95 with the ground-truth changes in the predefined score.