Title: KAN KAN Buff Signed Graph Neural Networks? Show Chinese title

Title: KAN KAN 缓冲有符号图神经网络？

Abstract: 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. Show Chinese abstract

Abstract: 图表示学习旨在创建有效的节点和边嵌入，这些嵌入包含它们的特征和关系。图神经网络（GNNs）利用神经网络来建模复杂的图结构。最近，Kolmogorov-Arnold神经网络（KAN）作为一种传统多层感知器（MLP）的有前途的替代方案出现，它在参数更少的情况下提供了更高的准确性和可解释性。在本文中，我们提出将KAN集成到有符号图卷积网络（SGCNs）中，从而开发出增强的KAN-SGCNs（KASGCN）。我们在有符号社区检测和链接符号预测等任务上评估KASGCN，以提高有符号网络中的嵌入质量。我们的实验结果表明，KASGCN在所评估的任务中表现出与标准SGCNs相当或具有竞争力的性能，性能变化取决于有符号图的具体特征和参数设置的选择。这些发现表明，KASGCNs在具有上下文依赖性的有效性方面有望增强有符号图分析。