Signals and datasets that arise in physical and engineering applications, as well as social,
genetics, biomolecular, and many other domains, are becoming increasingly larger and …

Defining a sound shift operator for graph signals, similar to the shift operator in classical
signal processing, is a crucial problem in graph signal processing (GSP), since almost all …

Filters are fundamental in extracting information from data. For time series and image data
that reside on Euclidean domains, filters are the crux of many signal processing and …

Basic operations in graph signal processing consist of processing signals indexed on
graphs either by filtering them or by changing their domain of representation in order to …

Signal processing on graphs finds applications in many areas. In recent years, renewed
interest on this topic was kindled by two groups of researchers. Narang and Ortega …

Graph signal processing has recently emerged as a field with applications across a broad
spectrum of fields including brain connectivity networks, logistics and supply chains, social …

Analysis of signals defined over graphs has been of interest in the recent years. In this
regard, many concepts from the classical signal processing theory have been extended to …

The recovery of missing samples from available noisy measurements is a fundamental
problem in signal processing. This process is also sometimes known as graph signal …

Abstract The Graph Fourier transform (GFT) is a fundamental tool in graph signal
processing. In this paper, based on singular value decomposition of the Laplacian, we …

Multiresolution analysis is important for understanding graph signals, which represent graph-
structured data. Wavelet filterbanks permit multiscale analysis and processing of graph …