HyperCF: Bidirectional Incidence Counterfactuals for Hypergraph Neural Networks

Hypergraphs model higher-order interactions that pairwise graphs cannot represent, and hypergraph neural networks (HGNNs) are increasingly used to learn over them. As these models enter decision settings, structural counterfactual explanations become important: they ask for a minimal incidence edit under which the prediction would change. Existing hypergraph counterfactual methods are limited to deletion-only interventions on the original incidence structure, so the search fails on many targets. We expand the intervention space to bidirectional incidence editing, allowing a target both to leave incidences and to join existing hyperedges, and present HyperCF , which controls this expansion with a hop-bounded additive candidate space, learning-rate calibration based on deletion gradients with a coupled additive rate, and symmetric redundancy pruning. Out of seven widely adopted benchmarks, HyperCF raises counterfactual success over deletion-only search on six datasets at a two-hop radius and five at a one-hop radius, with gains of up to 40 percentage points, while the mean explanation size remains comparable and often smaller.