GSD

Graph signals residing on the vertices of a graph have recently gained prominence in research in various fields. Many methodologies have been proposed to analyze graph signals by adapting classical signal processing tools. 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. This package efficiently implements multiscale analysis applicable to various fields, and offers an effective tool for visualizing and decomposing graph signals. For the detailed methodology, see Ortega et al. (2018) doi:10.1109/JPROC.2018.2820126, Shuman et al. (2013) doi:10.1109/MSP.2012.2235192, Tremblay et al. (2014) https://www.eurasip.org/Proceedings/Eusipco/Eusipco2014/HTML/papers/1569922141.pdf, and Cho et al. (2024) "Statistical graph empirical mode decomposition by graph denoising and boundary treatment".

Last 30 days

This package has been downloaded 177 times in the last 30 days. More than a random curiosity, but not quite a blockbuster. Still, it's gaining traction! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 8 times.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 1,907 times in the last 365 days. Consider this 'mid-tier influencer' status—if it were a TikTok, it would get a nod from nieces and nephews. The day with the most downloads was Sep 11, 2024 with 19 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.