Graph curvature and Laplacian operators form a vibrant area of research at the intersection of differential geometry and graph theory. The concept of graph curvature, inspired by classical Ricci ...

本研究では、大規模なグラフデータに対するスペクトルグラフニューラルネットワーク（Spectral GNNs）のスケーラビリティを向上させるために、ラプラシアンの疎化を用いた新しい手法「Spectral Graph Neural Networks with Laplacian Sparsification (SGNN-LS)」を提案しています。

Abstract: Graph signal processing (GSP) is a prominent framework for analyzing signals on non-Euclidean domains. The graph Fourier transform (GFT) uses the combinatorial graph Laplacian matrix to ...

Abstract: This paper introduces a graph Laplacian regularization in the hyperspectral unmixing formulation. The proposed regularization relies upon the construction of a graph representation of the ...

MultiscaleGraphSignalTransforms.jl is a collection of software tools written in the Julia programming language for graph signal processing including HGLET, GHWT ...

I co-created Graph Neural Networks while at Stanford. I recognized early on that this technology was incredibly powerful. Every data point, every observation, every piece of knowledge doesn’t exist in ...