GraphKAN: An Efficient and Interpretable Kolmogorov-Arnold Graph Network for Source Detection

Le Cheng, Peican Zhu, Yangming Guo, Chao Gao, Keke Tang

Keywords: Source Detection, Social Networks, Kolmogorov-Arnold Graph Network

TL;DR: An Efficient and Interpretable Kolmogorov-Arnold Graph Network for Source Detection

Abstract: Source detection in graphs offers a viable solution to critical challenges such as rumor tracing. Yet existing GCN-based approaches squander non-embedding parameters and rely on fixed activation functions. We present GraphKAN: An Efficient and Interpretable Kolmogorov–Arnold Graph Network for Source Detection, which capitalizes on Kolmogorov–Arnold Networks (KANs) by assigning learnable activation functions to edge weights. Node features are first diffused through B-spline–based univariate activations, yielding expressive and localized transformations. We further devise a sparsity-aware neighborhood aggregation rooted in community clusters, where edge-level attention is adaptively strengthened through KAN-driven kernel learning. Unlike black-box GCNs, GraphKAN exposes interpretable intermediate representations via its learnable basis functions. Extensive experiments on twelve real-world datasets demonstrate that GraphKAN consistently outperforms state-of-the-art baselines in accuracy, efficiency, and interpretability.

Primary Area: learning on graphs and other geometries & topologies

Submission Number: 24287