English

The course requires knowledge of basic computer science principles and familiarity with linear algebra and statistics.

The course provides an overview and synthesis of research on the analysis of complex networks, made possible by the availability of big data, with a special focus on the economic and social networks and their structure and functions. Students will acquire the relevant tools for the empirical analysis of social and economic networks and get an overview of concepts used to describe and measure networks. Next, students will come into a set of models of how networks form, including random network models as well as strategic formation models, and some hybrids. We will then discuss a series of models of how networks impact behavior, including contagion, diffusion, learning, and peer influences.

At the end of the course, students will be able to design and carry out large-scale social network analysis studies.

The course provides practical applications and computer sessions using the Python programming language and the software Gephi. Students will learn how to manage big databases and map data in network models.

Basic notions for networks from graph theory
The mathematics of networks
Centrality measures
Networks models
Small-world effect
Community detection