摘要
Graph Neural Networks (GNNs) perform well on graph-structured data, but their dependence on graph connectivity limits scalability and efficiency. Kolmogorov-Arnold Networks (KANs), a recent architecture with learnable univariate functions, offer strong nonlinear expressiveness and efficient inference. In this paper, we integrate KANs into three widely used GNN architectures, GAT, SGC, and APPNP, resulting in three new models, KGAT, KSGC, and KAPPNP. We use these KAN-based GNN architectures as teacher models and apply multi-teacher knowledge amalgamation to distill their knowledge into a graph-independent student model, KAN. We evaluate our models on several benchmark datasets, and the results show that the proposed KAN-based models improve performance. The student model, KAN, further benefits from the knowledge amalgamation framework built on these models. Our results demonstrate that KANs enhance the expressiveness of GNNs while enabling efficient, graph-free inference.