KA-GAT: Kolmogorov–Arnold based Graph Attention Networks

Jianfan Chen, Xuebiao Yuchi, Zhiwei Yan, Kejun Dong, Hongtao Li

Keywords: Graph Neural Networks, Kolmogorov-Arnold Networks, Graph Attention Networks, Multi-head Attention Mechanism, Model Interpretability

TL;DR: KA-GAT is a graph neural network combining KAN and GAT, optimised for high-dimensional data processing and model interpretability.

Abstract:

Graph Neural Networks (GNNs) have demonstrated remarkable capabilities in processing graph-structured data, but they often struggle with high-dimensional features and complex, nonlinear relationships. To address these challenges, we propose KA-GAT, a novel model that integrates Kolmogorov-Arnold Networks (KANs) with Graph Attention Networks (GATs). KA-GAT leverages KAN to decompose and reconstruct high-dimensional features, enhancing representational capacity, while a multi-head attention mechanism dynamically focuses on key graph components, improving interpretability. Experimental results on benchmark datasets, including Cora and Citeseer, demonstrate that KA-GAT achieves significant accuracy improvements compared to baseline models like GAT, with a relative gain of 4.5% on Cora. These findings highlight KA-GAT’s robustness and potential as an interpretable and scalable solution for high-dimensional graph data, paving the way for further advancements in GNN research.

Primary Area: learning on graphs and other geometries & topologies

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Submission Number: 1509