Transferring Social Network Knowledge from Multiple GNN Teachers to Kolmogorov-Arnold Networks

--Graph Neural Networks (GNNs) have shown strong performance on graph-structured data, but their reliance on graph connectivity often limits scalability and efficiency. In this work, we integrate KANs into three popular GNN architectures --GA T, SGC, and APPNP --resulting in three new models: KGA T, KSGC, and KAPPNP . We further adopt a multi-teacher knowledge amalgamation framework, where knowledge from multiple KAN-based GNNs is distilled into a graph-independent KAN student model. Experiments on benchmark datasets show that the proposed models improve node classification accuracy, and the knowledge amalgamation approach significantly boosts student model performance. Our findings highlight the potential of KANs for enhancing GNN expressiveness and for enabling efficient, graph-free inference. With the rapid development of deep learning technologies, Graph Neural Networks (GNNs) have demonstrated exceptional performance in processing graph-structured data [1]. GNNs have been widely applied in social network analysis, recommendation systems, knowledge graphs, and bioinfor-matics.