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GraphKAN: Enhancing Feature Extraction with Graph Kolmogorov Arnold Networks
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Massive number of applications involve data with underlying relationships embedded in non-Euclidean space. Graph neural networks (GNNs) are utilized to extract features by capturing the dependencies within graphs. Despite groundbreaking performances, we argue that Multi-layer perceptrons (MLPs) and fixed activation functions impede the feature extraction due to information loss. Inspired by Kolmogorov Arnold Networks (KANs), we make the first attempt to GNNs with KANs. We discard MLPs and activation functions, and instead used KANs for feature extraction. Experiments demonstrate the effectiveness of GraphKAN, emphasizing the potential of KANs as a powerful tool. Code is available at https://github.com/Ryanfzhang/GraphKan.
Forward citations
Cited by 5 Pith papers
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Khan-GCL: Kolmogorov-Arnold Network Based Graph Contrastive Learning with Hard Negatives
Khan-GCL combines KAN encoders with coefficient-based critical feature identification to generate hard negatives and reports state-of-the-art graph classification results.
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PRKAN: Parameter-Reduced Kolmogorov-Arnold Networks
PRKAN lowers KAN parameter counts to near-MLP levels via attention, convolution/pooling, dimension summation, and feature-vector projections, reaching MLP-like accuracy on MNIST and Fashion-MNIST.
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KAA: Kolmogorov-Arnold Attention for Enhancing Attentive Graph Neural Networks
Swapping attentive GNN score mappings for a single-layer Kolmogorov-Arnold Network improves benchmark performance and, on a specially constructed input matrix, provably achieves zero maximum ranking error.
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Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning
Conformal prediction applied to ensembles of KANs, FBKANs, and MFKANs yields prediction intervals that empirically hit the target 95% coverage on four synthetic problems.
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Free-Knots Kolmogorov-Arnold Network: On the Analysis of Spline Knots and Advancing Stability
A free-knot variant of Kolmogorov-Arnold networks reports higher accuracy with fewer parameters than fixed-grid KAN, but its central smoothing regularizer is mathematically inert and the knot bound proof is not rigorous.
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