This mini-course covers computational and statistical methods to characterize the structure and dynamics of complex social networks. We cover methods such as clustering, classification, spectral analysis and Montecarlo and also specific applications to social network data and spreading processes on these networks. We discuss current research and ethical questions raised by applications.
This advanced course will give students insight into the questions that can be answered analyzing network data and into the related challenges. They will be exposed to the main methods that can be used to tackle these questions and learn about the shortcomings of these current methods. We will also raise students awareness of some of the ethical questions raised, mainly in the realm of privacy, by the types of data collected and the influence on individual behavior that can be achieved through technologies built on the methods presented in class. Students will be encouraged to apply their knowledge to a specific network dataset by producing a research proposal.
Prerequisites / Notice
Students must be in their 5th semester or more advanced. Knowledge of basic: linear algebra, differential equations, probability, statistics and programming.
Performance assessment information (valid until the course unit is held again)