David I Shuman

TL;DR: The field of signal processing on graphs merges algebraic and spectral graph theoretic concepts with computational harmonic analysis to process high-dimensional data on graphs as discussed by the authors, which are the analogs to the classical frequency domain and highlight the importance of incorporating the irregular structures of graph data domains when processing signals on graphs.

TL;DR: This tutorial overview outlines the main challenges of the emerging field of signal processing on graphs, discusses different ways to define graph spectral domains, which are the analogs to the classical frequency domain, and highlights the importance of incorporating the irregular structures of graph data domains when processing signals on graphs.

TL;DR: 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 and explains the structure and the organization of this software.

TL;DR: In this article, the authors generalize windowed Fourier analysis to the graph domain and design dictionaries and transform methods to identify and exploit structure in signals on weighted graphs, but they need to account for the intrinsic geometric structure of the underlying graph data domain.

TL;DR: This paper generalizes one of the most important signal processing tools - windowed Fourier analysis - to the graph setting and designs dictionaries and transform methods to identify and exploit structure in signals on weighted graphs.