# Welcome to the GSP workshop 2020!

The links to all the plenary talks are listed below:

- GSP2020-PT1: Daniel P. Palomar (Learning Graphs of Stocks)
- GSP2020-PT2: Edwin Hancock (Entropic Analysis of Network Time Series)
- GSP2020-PT3: Patrick J. Wolfe (Modeling variation in network populations)
- GSP2020-PT4: Sergio Barbarossa (Topological Signal Processing)

Moreover, an embedded version of each video along with the abstract and further information about each speaker may be found in the PLENARY SPEAKERS section.

## 2020 Graph Signal Processing Workshop

Following the success of the first four editions of the Graph Signal Processing Workshop, we are pleased to announce the fifth workshop in this series. The GSP workshop will be held at the Madrid-Downtown Campus of the King Juan Carlos University during May 10-12th, 2020.

A graph, or network, is a structure that encodes pairwise relationships and a graph signal is a function defined on the nodes of the graph. The values of the weights on the edges of the graph encode an expectation on the relationship between the respective signal components. A large weight indicates that we expect the signal elements to be similar and a small weight indicates no such expectation except for what is implied by their common proximity to other nodes. The goal of graph signal processing (GSP) is to generalize the classical signal processing toolbox to graph signals.

Graph signal processing applications arise whenever we encounter one of the many signals that are supported on a graph. Examples of applications that will be showcased in the workshop include gene expression patterns defined on top of gene networks, the spread of epidemics over a social network, the congestion level at the nodes of a telecommunication network, and patterns of brain activity defined on top of a brain network.

Besides particular applications, the workshop will also showcase the advancement of the understanding of network data by redesigning traditional tools originally conceived to study signals defined on regular domains (such as time-varying signals or spatially varying images and fields) and extending them to analyze signals on the more complex graph domain. Examples of topics to be showcased in this theoretical track include graph transforms, sampling theorems, and filter design.