将来自多个GNN教师模型的社会网络知识迁移至Kolmogorov-Arnold Networks
Transferring Social Network Knowledge from Multiple GNN Teachers to Kolmogorov-Arnold Networks
AI总结：
本研究将KANs整合进GAT、SGC和APPNP三种GNN架构并提出多教师知识融合框架，显著提升了节点分类准确率与无图推理效率。
摘要：
图神经网络（GNNs）在图结构数据上表现出色，但其对图连通性的依赖常限制可扩展性与效率。Kolmogorov-Arnold Networks（KANs）是一种具有可学习一元函数的新架构，提供强非线性表达力与高效推理。本研究将KANs整合进三种流行GNN架构——GAT、SGC和APPNP，得到三个新模型：KGAT、KSGC与KAPPNP。我们进一步采用多教师知识融合框架，将多个基于KAN的GNN的知识蒸馏至一个不依赖图的KAN学生模型。基准数据集上的实验表明，所提模型提升了节点分类准确率，且知识融合方法显著提升了学生模型性能。结果突显了KANs在增强GNN表达力及实现高效无图推理方面的潜力。
英文摘要：
Graph Neural Networks (GNNs) have shown strong performance on graph-structured data, but their reliance on graph connectivity often limits scalability and efficiency. Kolmogorov-Arnold Networks (KANs), a recent architecture with learnable univariate functions, offer strong nonlinear expressiveness and efficient inference. In this work, we integrate KANs into three popular GNN architectures-GAT, SGC, and APPNP-resulting in three new models: KGAT, 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.
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