The study of sampling signals on graphs, with the goal of building an analog of sampling for
standard signals in the time and spatial domains, has attracted considerable attention …

Modern data analytics applications on graphs often operate on domains where graph
topology is not known a priori, and hence its determination becomes part of the problem …

The fractional Fourier transform (FRFT) parametrically generalizes the Fourier transform (FT)
by a transform order, representing signals in intermediate time-frequency domains. The …

The graph fractional Fourier transform (GFRFT) has been applied to graph signal processing
and has become an important tool in graph signal processing. However, most of the graph …

We consider the problem of recovering random graph signals from nonlinear
measurements. For this setting, closed-form Bayesian estimators are usually intractable and …

Graph signals arise in various applications, ranging from sensor networks to social media
data. The high-dimensional nature of these signals implies that they often need to be …

Vertex based and spectral based GSP sampling has been studied recently. The literature
recognizes that methods in one domain do not have a counterpart in the other domain. This …

While frequency-domain algorithms have been demonstrated to be powerful for
conventional nonlinear signal processing, there is still not much progress in literature …

The prevalence of multi-dimensional (mD) graph signals in various real-world applications,
such as digital images and data with spatial and temporal dimensions, highlights their …

In this paper, we consider the problem of representing and recovering graph signals with a
nonlinear measurement model. We propose a two-stage graph signal processing (GSP) …