Contribution of the course to the overall degree programme goals
The course aims at providing an introduction to social network analysis and the study of complex networks.
The course will explore methods and techniques for big data analysis and the modelling of dynamic complex systems such as social networks.
Expected learning outcomes
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 complex systems.
Programming, statistics, calculus.
- Introduction to Graph Theory
- Social Networks
- Strong and Weak Ties
- Homophily and Social Influence
- Network Dynamics
- Information Cascades
- The Small-World Phenomenon
(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 theoretical questions about the course content and of the presentation of a project previously agreed with the instructor.
Interactive frontal classes.
Type of exam
written and oral
2030 Agenda for Sustainable Development Goals
This subject deals with topics related to the macro-area "Human capital, health, education" and contributes to the achievement of one or more goals of U. N. Agenda for Sustainable Development
Last update of the programme: 29/05/2021
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