Kolmogorov-Arnold Graph Neural Networks
<|reference_start|>Kolmogorov-Arnold Graph Neural Networks: 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.<|reference_end|>
@article{de carlo2024kolmogorov-arnold,
    title={Kolmogorov-Arnold Graph Neural Networks},
    author={Gianluca De Carlo, Andrea Mastropietro, Aris Anagnostopoulos},
    journal={arXiv preprint arXiv:2406.18354},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.18354},
    primaryClass={cs.LG cs.AI}
}