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。
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 ...
MicroCloud Hologram Inc. (NASDAQ: HOLO), ("HOLO" or the "Company"), a technology service provider, released learnable quantum spectral filter technology for hybrid graph neural networks. This ...
Abstract: Inverse imaging problems are inherently underdetermined, and hence, it is important to employ appropriate image priors for regularization. One recent popular prior-the graph Laplacian ...
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 ...
MicroCloud Hologram ’s approach uses a logarithmic encoding method to reduce the number of qubits needed, representing an N-dimensional feature space using just log (N) qubits. The system forms an end ...
MultiscaleGraphSignalTransforms.jl is a collection of software tools written in the Julia programming language for graph signal processing including HGLET, GHWT ...
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