KAN KAN Buff Signed Graph Neural Networks?

Graph Representation Learning focuses on creating embeddings for nodes and edges that capture their features and connections. Graph Neural Networks (GNNs) use neural networks to model complex graph relationships. The Kolmogorov-Arnold Neural Network (KAN) has recently emerged as an alternative to the Multi-Layer Perceptron (MLP), offering better accuracy and interpretability with fewer parameters. KANs have been applied to GNN tasks. This paper introduces the integration of KANs into Signed Graph Convolutional Networks (SGCNs). We evaluate KAN-enhanced SGCNs (KASGCN) on signed community detection and link sign prediction tasks to improve embedding quality in signed networks. While the results show some variability, KASGCN performs competitively with or similarly to the standard SGCN in the functions tested. Its effectiveness depends on the specific context, such as the signed graph and parameter settings.