CSCI 5760G - Information and Social Networks: Theory and Application

This course studies commonalities across diverse engineered and physical networks such as computer networks, information networks and social networks. It focuses on rigorous data-driven methods aimed at understanding the structure and dynamics of these networks. We will cover recent research on analysis of large social and information networks and on models and algorithms that abstract their basic properties. Class also reviews fundamental algorithms behind high-impact companies such as Google, Facebook, etc. We explore how to measure and predict the structure and dynamics of large-scale networks, measure the robustness of networks, make networks more robust, predict the dynamics of information cascades, and develop and test our own data-driven hypotheses about networks. Students are expected to have prior background in linear algebra, probability theory and Python programming.
Credit hours: 3