用可学习的单变量函数提升图神经网络性能，实现高效无图推理。
Transferring Social Network Knowledge from Multiple GNN Teachers to Kolmogorov-Arnold Networks
- 将KAN替换原有GNN中的线性层，构建新型KAN-GNN模型。
- 多教师知识蒸馏使无图学生模型在多个数据集上准确率显著提升。
- 适合关注模型效率与非图结构推理的研究者参考。
图神经网络（GNN）在图结构数据上表现优异，但依赖图连通性限制了其可扩展性和效率。柯尔莫戈罗夫-阿诺德网络（KAN）是一种基于可学习单变量函数的新架构，具有强非线性表达能力和高效推理特性。本文将KAN融入三种主流GNN架构——GAT、SGC和APPNP，分别构建新模型KGAT、KSGC和KAPPNP。进一步采用多教师知识融合框架，将多个基于KAN的GNN教师模型的知识蒸馏到一个与图无关的KAN学生模型中。在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增强学生模型性能。研究结果表明，KAN有望提升GNN表达能力，并支持高效、无图推理。
原文摘要 · Abstract (English)
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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