  Graph neural networks (GNNs) excel in learning from network-like data but
often lack interpretability, making their application challenging in domains
requiring transparent decision-making. We propose the Graph Kolmogorov-Arnold
Network (GKAN), a novel GNN model leveraging spline-based activation functions
on edges to enhance both accuracy and interpretability. Our experiments on five
benchmark datasets demonstrate that GKAN outperforms state-of-the-art GNN
models in node classification, link prediction, and graph classification tasks.
In addition to the improved accuracy, GKAN's design inherently provides clear
insights into the model's decision-making process, eliminating the need for
post-hoc explainability techniques. This paper discusses the methodology,
performance, and interpretability of GKAN, highlighting its potential for
applications in domains where interpretability is crucial.
