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.

@article{carlo2025_2406.18354,
  title={ Kolmogorov-Arnold Graph Neural Networks },
  author={ Gianluca De Carlo and Andrea Mastropietro and Aris Anagnostopoulos },
  journal={arXiv preprint arXiv:2406.18354},
  year={ 2025 }
}