Graph-Based Data Science for Networked Systems

3.0 credits · Graduate

The goal of this course is to introduce data structural and computational models that are indexed by the irregular support of a graph. The graph represents the network that couples the dynamics of many agents, or it can be a more abstract Bayesian graphical model that explains how observations are conditionally dependent. The course will start from introducing basic concepts in graph theory followed by an introduction to random graphs models. This part will be followed by network dynamical models that model the observations from these processes. Bayesian graphical models will be briefly covered as a more general statistical abstraction and computational framework to perform inferences. The course will then introduce the students to the emerging field of graph signal processing, a theory that generalizes digital and image processing to graph signals.