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 …

Two architectures that generalize convolutional neural networks (CNNs) for the processing
of signals supported on graphs are introduced. We start with the selection graph neural …

We study the optimal design of graph filters (GFs) to implement arbitrary linear
transformations between graph signals. GFs can be represented by matrix polynomials of …

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 …

Signal processing (SP) excels at analyzing, processing, and inferring information defined
over regular (first continuous, later discrete) domains such as time or space. Indeed, the last …

The problem of simultaneously learning several related tasks has received considerable
attention in several domains, especially in machine learning, with the so-called multitask …

Driven by the outstanding performance of neural networks in the structured euclidean
domain, recent years have seen a surge of interest in develo** neural networks for graphs …

Graph filters are one of the core tools in graph signal processing. A central aspect of them is
their direct distributed implementation. However, the filtering performance is often traded …

We study linear filters for processing signals supported on abstract topological spaces
modeled as simplicial complexes, which may be interpreted as generalizations of graphs …

Efficient numerical linear algebra is a core ingredient in many applications across almost all
scientific and industrial disciplines. With this survey we want to illustrate that numerical linear …