Gasper: GrAph Signal ProcEssing in R

The emerging field of Graph Signal Processing (GSP) aims to bridge the gap between signal processing and spectral graph theory. One of the objectives is to generalize fundamental analysis operations from regular grid signals to irregular structures in the form of graphs. There is an abundant literature on GSP, in particular we refer the reader to Shuman et al. (2013) and Ortega et al. (2018) for an introduction to this field and an overview of recent developments, challenges and applications. GSP has also given rise to numerous applications in machine/deep learning: convolutional neural networks (CNN) on graphs Bruna et al. (2014), Henaff et al. (2015), Defferrard et al. (2016), semi-supervised classification with graph CNN Kipf and Welling (2017), Hamilton et al. (2017), community detection Tremblay and Borgnat (2014), to name just a few. Different software programs exist for processing signals on graphs, in different languages. The Graph Signal Processing toolbox (GSPbox) is an easy to use matlab toolbox that performs a wide variety of operations on graphs. This toolbox was port to Python as the PyGSP Perraudin et al. (2014). There is also another matlab toolbox the Spectral Graph Wavelet Transform (SGWT) toolbox dedicated to the implementation of the SGWT developed in Hammond et al. (2011). However, to our knowledge, there are not yet any tools dedicated to GSP in R. A development version of the gasper package is currently available online