Abstract: Spectral graph neural networks (GNNs) have shown great advantages in various graph-related fields. The core of spectral GNNs is the graph convolution operator based on the graph spectral ...

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Hyperspectral images (HSIs) have very high dimensionality and typically lack sufficient labeled samples, which significantly challenges their processing and analysis. These challenges contribute to ...

This paper presents the practical applications of Laplacian and signless Laplacian spectra across various fields including theoretical chemistry, computer science, electrical engineering, and complex ...

If you’re like me, you’ve heard plenty of talk about entity SEO and knowledge graphs over the past year. But when it comes to implementation, it’s not always clear which components are worth the ...

Abstract: Multi-view spectral clustering has achieved impressive performance by learning multiple robust and meaningful similarity graphs for clustering. Generally, the existing literatures often ...