Massive number of applications involve data with underlying relationships
embedded in non-Euclidean space. Graph neural networks (GNNs) are utilized to
extract features by capturing the dependencies within graphs. Despite
groundbreaking performances, we argue that Multi-layer perceptrons (MLPs) and
fixed activation functions impede the feature extraction due to information
loss. Inspired by Kolmogorov Arnold Networks (KANs), we make the first attempt
to GNNs with KANs. We discard MLPs and activation functions, and instead used
KANs for feature extraction. Experiments demonstrate the effectiveness of
GraphKAN, emphasizing the potential of KANs as a powerful tool. Code is
available at this https URL

大量的应用涉及嵌入在非欧几里得空间中的具有潜在关系的数据。图神经网络 (GNNs) 用于通过捕捉图中的依赖关系来提取特征。尽管具有突破性的性能，但我们认为多层感知器 (MLPs) 和固定激活函数阻碍了特征提取，因为信息丢失。受科尔莫戈洛夫 - 阿诺德网络 (KANs) 的启发，我们首次尝试将 GNN 与 KANs 结合使用。我们放弃了 MLPs 和激活函数，而是使用 KANs 进行特征提取。实验证明了 GraphKAN 的有效性，强调了 KANs 作为强大工具的潜力.