Graph signals residing on the vertices of a graph have recently gained prominence in research of various fields, including neural networks, social networks, traffic patterns, and sensors. Many methodologies have been proposed to analyze graph signals by adapting classical signal processing tools. In this study, we focus on graph signal decomposition, which reduces the complexity of the graph signal and increases its interpretability. Recently, several notable graph signal decomposition methods have been proposed, which include graph Fourier decomposition based on graph Fourier transform, graph empirical mode decomposition, and statistical graph empirical mode decomposition. We provide an R package GSD to efficiently implement multiscale analysis applicable to various fields. The R package GSD offers an effective tool for visualizing and decomposing graph signals.
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Univ Wisconsin, Dept Hort, 1575 Linden Dr, Madison, WI 53706 USAUniv Wisconsin, Dept Hort, 1575 Linden Dr, Madison, WI 53706 USA
Covarrubias-Pazaran, Giovanny
Diaz-Garcia, Luis
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Univ Wisconsin, Dept Hort, 1575 Linden Dr, Madison, WI 53706 USA
Inst Nacl Invest Forestales & Agr Pecuarias, Campo Expt Pabellon, Aguascalientes, MexicoUniv Wisconsin, Dept Hort, 1575 Linden Dr, Madison, WI 53706 USA
Diaz-Garcia, Luis
Schlautman, Brandon
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Univ Wisconsin, Dept Hort, 1575 Linden Dr, Madison, WI 53706 USAUniv Wisconsin, Dept Hort, 1575 Linden Dr, Madison, WI 53706 USA
Schlautman, Brandon
Salazar, Walter
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Univ Wisconsin, Dept Hort, 1575 Linden Dr, Madison, WI 53706 USAUniv Wisconsin, Dept Hort, 1575 Linden Dr, Madison, WI 53706 USA
Salazar, Walter
Zalapa, Juan
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Univ Wisconsin, Dept Hort, 1575 Linden Dr, Madison, WI 53706 USA
Univ Wisconsin, USDA ARS, Vegetable Crops Res Unit, Madison, WI 53706 USAUniv Wisconsin, Dept Hort, 1575 Linden Dr, Madison, WI 53706 USA