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arXiv · 2406.18354

Kolmogorov-Arnold Graph Neural Networks

Gianluca De Carlo·Andrea Mastropietro·Aris Anagnostopoulos
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Abstract

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.

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Gianluca De Carlo, Andrea Mastropietro, Aris Anagnostopoulos. 2024-06-26. Kolmogorov-Arnold Graph Neural Networks. https://arxiv.org/abs/2406.18354

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