Counterfactual Intervention Validation and Decision Transition Learning for Graph and Hypergraph Neural Networks

The decision processes of Graph Neural Networks and Hypergraph Neural Networks often rely on implicit structural propagation and distributed representations. It makes them difficult to interpret. And in multi class tasks, class imbalance and inter-class similarity can lead to ambiguous local decision boundaries. Make minority and easily confused classes difficult to learn.So we propose a counterfactual intervention validation and decision transition learning framework for GNNs and HGNNs. First, we introduce a two-stage counterfactual discovery and intervention validation method. It searches the structural space for key supports and estimates their contributions. The key supports drive a source state toward a restoration target. Node feature interventions are then applied to validate these candidate supports and to generate counterfactual samples. Second, we propose a dual target counterfactual decision transition learning method. Each source sample is paired with counterfactuals corresponding to a restoration target and a class-transition target. The model is jointly optimized using endpoint classification, decision-margin, transition-magnitude, and local decision-gradient alignment constraints.We evaluate on two public datasets using graph and hypergraph neural networks.On the TEP dataset, it improves Macro-F1 to 91.89% and Balanced Accuracy to 90.74%. And increase the restoration endpoint margins to 16.850 and transition endpoint margins to 13.954.On the Paderborn bearing dataset, the model maintains a Macro-F1 of 96.43%.The results show that the proposed method provides more reliable counterfactual explanations and geometrically meaningful decision directions.It generates counterfactuals with cross-intervention consistency. And we can use them to maintain or improve the classification performance of the base model, and improve the recognition of minority and similar classes.