Kolmogorov-Arnold Graph Neural Networks
This work addresses the need for transparent decision-making in domains requiring interpretability, offering a novel method that improves both accuracy and interpretability for graph-based tasks.
The paper tackles the problem of interpretability in graph neural networks (GNNs) by proposing the Graph Kolmogorov-Arnold Network (GKAN), which uses spline-based activation functions to enhance accuracy and provide clear insights into decision-making, outperforming state-of-the-art models on five benchmark datasets.
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.