Kolmogorov–Arnold Graph Neural Networks
Keywords: Graph Neural Networks, Kolmogorov-Arnold Networks, Interpretability
TL;DR: Adaptation of Kolmogorov-Arnold Networks to graph-structured data enhancing accuracy and interpretability.
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 Kolmogorov–Arnold Network for Graphs (KANG), 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 KANG outperforms state-of-the-art GNN models in node classification, link prediction, and graph classification tasks. In addition to the improved accuracy, KANG’s design inherently provides 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 KANG, highlighting its potential for applications in domains where interpretability is crucial.
Primary Area: learning on graphs and other geometries & topologies
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Submission Number: 9757
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