Academic year 2019/2020 Syllabus of previous years
Official course title SOCIAL NETWORK ANALYSIS
Course code CT0540 (AF:314741 AR:168587)
Modality For teaching methods (in presence/online) please check the timetable
ECTS credits 6
Degree level Bachelor's Degree Programme
Educational sector code INF/01
Period 1st Semester
Course year 3
Moodle Go to Moodle page
Contribution of the course to the overall degree programme goals
Methodologies for the analysis, modeling, and forecasting of complex dynamical systems with a specific focus on social networks
Expected learning outcomes
The student will be able to operate with massive datasets and to model them in the complex systems framework for predictive analytics.
The main objective of the course will be for the student to have a comprehensive understanding of the state of the art in the field and about its future development.
Programming, Calculus and Probability.
Introduction to Data and Complexity
Data and Privacy
Stochastic Processes
Complex Networks (Random, Small-World, Scale-Free)
Community Detection Algorithms
Homophily and Assortativity
Assortative Mixing, Link Prediction
Modeling Dynamical Processes on Complex Networks
Introduction to Sentiment Analysis
Spreading Phenomena (Epidemics and Information Diffusion)
Opinion Dynamics
Data Visualization
Referral texts
Barrat, A., Barthelemy, M., & Vespignani, A. (2008). Dynamical processes on complex networks. Cambridge university press.

Network Science, A.L. Barabàsi
Assessment methods
Written Test and Group Project
Teaching methods
Slides, Practical Sessions, Discussion Papers,
Teaching language
Type of exam
written and oral
Definitive programme.
Last update of the programme