Graphs are widely used as a popular representation of the network structure of connected
data. Graph data can be found in a broad spectrum of application domains such as social …

Dealing with data and observations has always been an important aspect of discovery in
science. The idea that science is related to data was brilliantly summarised by Fourier in his …

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 …

To reduce the cost of storing, processing, and visualizing a large-scale point cloud, we
propose a randomized resampling strategy that selects a representative subset of points …

Graphons are infinite-dimensional objects that represent the limit of convergent sequences
of graphs as their number of nodes goes to infinity. This paper derives a theory of graphon …

The goal of this paper is to propose novel strategies for adaptive learning of signals defined
over graphs, which are observed over a (randomly) time-varying subset of vertices. We …

We study the best arm identification problem in linear bandits, where the mean reward of
each arm depends linearly on an unknown $ d $-dimensional parameter vector $\theta …

This work proposes a normalized least-mean-squares (NLMS) algorithm for online
estimation of bandlimited graph signals (GS) using a reduced number of noisy …

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 …

This paper investigates the graph representation for speech signals and proposes a novel
multilayer graph topology for capturing both the inter-frame and intra-frame relationships …