SOCIAL NETWORK ANALYSIS

The course aims at providing an introduction to social network analysis. The course will explore methods and techniques for big data analysis and the modelling of complex systems such as social networks.

The course aims at providing skills and expertise for:
- Managing, manipulating, visualising and interpreting big data;
- Understanding network science principles;
- Understanding main techniques and methods for the analysis of social networks.

Programming, statistics, calculus

- Introduction to Graph Theory
- Social Networks
- Strong and Weak Ties
- Homophily and Social Influence
- Network Dynamics
- The Small-World Phenomenon
- Echo chambers and polarization

(This outline may be subject to change.)

Easley, David, and Jon Kleinberg. Networks, crowds, and markets. Cambridge University Press, 2010.
Barabási, Albert-László. Network Science. Cambridge University Press, 2016

The final exam consists of an oral test (approximately 40 minutes) organized in two parts: 1) theoretical questions and exercises on the topics covered in the course; 2) presentation and discussion of a project previously agreed upon with the instructor. The objective of the project is to develop an original analysis by applying the methods, tools, and techniques covered during the course. The project can be carried out in groups of two students.

The evaluation of the final exam is structured as follows:
- Oral test (30%): expressive skills, theoretical knowledge, and language accuracy will contribute to the evaluation.
- Project (70%): the accuracy of the project's execution according to the guidelines will determine the evaluation.

Interactive frontal classes.

Italian

oral