Research in graph signal processing (GSP) aims to develop tools for processing data
defined on irregular graph domains. In this paper, we first provide an overview of core ideas …

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 …

We study the problem of selecting the best sampling set for bandlimited reconstruction of
signals on graphs. A frequency domain representation for graph signals can be defined …

This chapter characterises the scope of this book. It explains the reasons why one should be
interested in cluster analysis, lists major application areas, basic theoretical and practical …

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 …

Can one reduce the size of a graph without significantly altering its basic properties? The
graph reduction problem is hereby approached from the perspective of restricted spectral …

This paper focuses on spectral graph convolutional neural networks (ConvNets), where
filters are defined as elementwise multiplication in the frequency domain of a graph. In …

Graph Signal Processing (GSP) is an emerging research field that extends the concepts of
digital signal processing to graphs. GSP has numerous applications in different areas such …