用KAN改进符号图神经网络，提升嵌入质量与可解释性。
KAN KAN Buff Signed Graph Neural Networks?
- 将KAN替代传统MLP，增强符号图卷积网络的表达能力
- 在符号社区检测和链接符号预测任务中表现相当或更优
- 适合关注可解释性与参数效率的图学习研究者
图表示学习旨在为节点和边生成能捕捉其特征与关系的有效嵌入。图神经网络（GNN）利用神经网络建模复杂图结构。近期，科尔莫戈罗夫-阿诺尔德网络（KAN）作为传统多层感知机（MLP）的有前途替代方案，以更少参数实现更高精度与更强可解释性。本文提出将KAN融入符号图卷积网络（SGCN），构建增强型KAN-SGCN（KASGCN）。我们在符号社区检测与链接符号预测任务上评估KASGCN，以提升符号网络中的嵌入质量。实验结果表明，KASGCN在所评估任务中表现与标准SGCN相当或更优，性能差异取决于符号图特性与参数设置。这些发现表明，KASGCNs在上下文相关的有效性下，具有提升符号图分析的潜力。
原文摘要 · Abstract (English)
Graph Representation Learning aims to create effective embeddings for nodes and edges that encapsulate their features and relationships. Graph Neural Networks (GNNs) leverage neural networks to model complex graph structures. Recently, the Kolmogorov-Arnold Neural Network (KAN) has emerged as a promising alternative to the traditional Multilayer Perceptron (MLP), offering improved accuracy and interpretability with fewer parameters. In this paper, we propose the integration of KANs into Signed Graph Convolutional Networks (SGCNs), leading to the development of KAN-enhanced SGCNs (KASGCN). We evaluate KASGCN on tasks such as signed community detection and link sign prediction to improve embedding quality in signed networks. Our experimental results indicate that KASGCN exhibits competitive or comparable performance to standard SGCNs across the tasks evaluated, with performance variability depending on the specific characteristics of the signed graph and the choice of parameter settings. These findings suggest that KASGCNs hold promise for enhancing signed graph analysis with context-dependent effectiveness.
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