Post date: 28-may-2018 10:12:10
Title: Data Science for Networks: a Graph Signal Processing Perspective
Date: 01-Junio-2018, 13:00
Aula: 302, Aulario III
Material:
Speaker: Santiago Segarra
Abstract:
Understanding networks and networked behavior has emerged as one of the foremost intellectual challenges of the 21st century.
While we obviously master the technology to engineer transformational networks — from communication infrastructure to online social networks — our theoretical understanding of fundamental phenomena that arise in networked systems remains limited. My goal is to combine network science and signal processing in order to leverage the structure of networks to better understand data defined on them. In this context, the term Data Science for Networks can be understood as a joint effort to understand both network structures and network data.
After a general overview of Data Science for Networks, the talk consists of three parts. First, we introduce the fundamental building blocks of graph signal processing (GSP) as a toolbox to study network data, and we sediment these concepts by analyzing in some detail the problems of sampling bandlimited graph signals and blind deconvolution of graph filters. We then apply the GSP concepts to study the problem of inferring the topology of a network from the observation of graph signals. Leveraging results from GSP and sparse recovery, efficient topology inference algorithms with theoretical guarantees are put forth. Lastly, we illustrate the ubiquity of Data Science for Networks via several real-world examples.