Teaching

Curriculum: National PhD Program in Autonomuous Systems (DAUSY)

Period: To be fixed

Credits: To be fixed

Objectives

The course introduces the students to the use of graph theory for the analysis of static and dynamic social networks. By using data coming from real networks, basic concepts for network analysis through centrality measurements are discussed. The last part of the course is dedicated to the analysis of opinion dynamics.

Prerequisites

Basics of systems theory, linear algebra and matrices. Elements of programming in Matlab (or R).

Contents

Principles of modeling and concepts of system, model and network. Modeling of social networks through graphs. Different types of graphs: undirected, directed, weighted, unweighted. List of adjacency and adjacency matrix. Centrality measurements: degree, distance, closeness, betweenness, ego-network, eigenvector centrality, page rank, hub, authorities. Clustering analysis: components and dendrograms. Scale-free networks. Multilayer netwroks. Opinion dynamics and consensus: bounded confidence models, French-De Groot model, Friedkin-Johnsen model, Hegselmann-Krause model.

Teaching methods

Lectures and exercises with the use of the computer using the Matlab (or R) software.

Final exam

The exam consists of an oral discussion of the technical report prepared by the student dealing with the analysis of a real case-study of social network.

References

- Francesco Vasca, Lectures notes

- Laslo Barabasi, "Network Science", Cambridge University Press, 2019.

- Mark Newman, “Networks: An Introduction”, Oxford University Press, 2010, ISBN: 978-0-199-20665-0.

- Francesco Bullo, Lectures on Network Systems, Kindle Direct Publishing, 2019, ISBN 978-1-986425-64-3, http://motion.me.ucsb.edu/book-lns/