GSPBOX: A toolbox for signal processing on graphs

By Nathanael Perraudin, Johan Paratte, David I. Shuman, Vassilis Kalofolias, Pierre Vandergheynst, David K. Hammond. 2014

354 citations recorded by the source corpus.

This document introduces the Graph Signal Processing Toolbox (GSPBox) a framework that can be used to tackle graph related problems with a signal processing approach. It explains the structure and the organization of this software. It also contains a general description of the important modules.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics

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Citations in the corpus

Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs (2017, background): “Synthetic point clouds are created from meshes by uniformly sampling 1000 points on mesh faces according to face area (a simulation of acquisition from multiple viewpoints) and rescaled into a unit sphere.”
Global and Local Uncertainty Principles for Signals on Graphs (2016, methodology): “The experiment was carried out using open-source code: the UNLocBoX [71] and the GSPBox [72].”
Graph Wavelet Neural Network (2019, methodology): “Figure 1 shows two wavelet bases at different scale on an example network, depicted using GSP toolbox (Perraudin et al., 2014).”
Rethinking Graph Neural Networks for Anomaly Detection (2022, methodology): “In Figure 1, we study the effect of anomalies on two types of graph: a Barabási–Albert graph with 500 nodes on Figure 1 (a)-(b) and a Minnesota road graph with 2,642 nodes (Perraudin et al., 2014) in Figure 1 (c)-(d).”
Accelerated filtering on graphs using Lanczos method (2015, methodology): “All experiments were performed with the signal processing toolbox GSPBox [12] and can be downloaded at https://lts2.epfl.ch/lanczos-filtering/ .”
A Multiscale Pyramid Transform for Graph Signals (2013, background): “The authors would like to thank the anonymous reviewers for their many constructive comments on earlier versions of this paper.”
Graph Signal Processing: Overview, Challenges, and Applications (2017, methodology): “Finally, it is worth mentioning that many of the basic GSP tools described here are available in several Matlab/Python toolboxes: GSPBox [215], GraSP [216] and PyGSP [217].”