HGKAN: Hypergraph Kolmogorov-Arnold Networks for Interpretable Prediction of Herb-Symptom Associations

With the increasing availability of large-scale traditional Chinese medicine (TCM) data, accurate prediction of herb-symptom associations (HSAs) has become a crucial task in natural drug discovery. Existing computational approaches mainly employ graph neural networks (GNNs) to model herb-symptom relationships in biological information networks. However, these methods are limited to low-order pairwise associations, failing to capture herbs' high-order effects and providing limited interpretability. To address these challenges, we model HSAs as a hierarchical hypergraph, where ingredients and targets form two distinct node layers, herbs and symptoms are represented as hyperedges connecting multiple nodes, and inter-layer edges capture ingredient-target interactions. Based on this structure, we propose hypergraph Kolmogorov-Arnold networks (HGKAN) comprising two modules: a binary interaction KAN (BiKAN) with bidirectional cross-attention for encoding pairwise ingredient-target interactions, and two high-order interaction KAN (HiKAN) branches for node-hyperedge message passing and selective high-order feature aggregation. Extensive experiments on two public TCM datasets show that HGKAN significantly outperforms 6 state-of-the-art baselines. Case studies further highlight the model's interpretability and provide mechanistic insights into herbal treatment.