GraphKAN：利用图柯尔莫哥洛夫-阿诺德网络增强特征提取
GraphKAN: Enhancing Feature Extraction with Graph Kolmogorov Arnold Networks
AI总结：
针对图神经网络中MLP和固定激活函数导致信息损失、限制特征提取的问题，研究者首次将KAN与GNN结合，提出GraphKAN模型，用KAN替代MLP和激活函数做特征提取，实验验证了其有效性，展现了KAN在图学习领域的应用潜力。
摘要：
大量应用涉及的非欧空间数据都嵌入了潜在关系。图神经网络（GNNs）被用于通过捕捉图内的依赖关系来提取特征。尽管其取得了突破性的性能，我们认为多层感知器（MLPs）和固定激活函数会因信息损失而阻碍特征提取。受柯尔莫哥洛夫-阿诺德网络（KANs）启发，我们首次尝试将KANs与GNNs结合。我们摒弃了MLPs和激活函数，转而使用KANs进行特征提取。实验证明了GraphKAN的有效性，凸显了KANs作为强大工具的潜力。代码可在https://github.com/Ryanfzhang/GraphKan获取。
英文摘要：
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 https://github.com/Ryanfzhang/GraphKan.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
