For many real-world applications, understanding feature-outcome relationships is as crucial as achieving high predictive accuracy. While traditional neural networks excel at prediction, their black-box nature obscures underlying functional relationships. Kolmogorov--Arnold Networks (KANs) address this by employing learnable spline-based activation functions on edges, enabling recovery of symbolic representations while maintaining competitive performance. However, KAN's architecture presents unique challenges for network pruning. Conventional magnitude-based methods become unreliable due to sensitivity to input coordinate shifts. We propose \textbf{ShapKAN}, a pruning framework using Shapley value attribution to assess node importance in a shift-invariant manner. Unlike magnitude-based approaches, ShapKAN quantifies each node's actual contribution, ensuring consistent importance rankings regardless of input parameterization. Extensive experiments on synthetic and real-world datasets demonstrate that ShapKAN preserves true node importance while enabling effective network compression. Our approach improves KAN's interpretability advantages, facilitating deployment in resource-constrained environments.

翻译：对于许多实际应用而言，理解特征与结果之间的关系与实现高预测精度同等重要。传统神经网络虽擅长预测，但其黑箱特性掩盖了潜在的函数关系。Kolmogorov-Arnold网络（KANs）通过在边上采用可学习的样条基激活函数来解决这一问题，在保持竞争性性能的同时能够恢复符号化表示。然而，KAN的架构为网络剪枝带来了独特挑战。传统基于幅值的方法因对输入坐标偏移敏感而变得不可靠。我们提出**ShapKAN**，一个利用Shapley值归因以平移不变方式评估节点重要性的剪枝框架。与基于幅值的方法不同，ShapKAN量化每个节点的实际贡献，确保无论输入参数化如何都能获得一致的重要性排序。在合成和真实数据集上的大量实验表明，ShapKAN在实现有效网络压缩的同时保留了真实的节点重要性。我们的方法提升了KAN的可解释性优势，有助于在资源受限环境中的部署。