Networks or graphs are a natural way to represent a variety of interacting complex systems - from disease spreading and brain networks, to social networks and political networks.
In this course, we will study the mathematical foundations of networks and investigate how the structure of networks affects the spread of signals on the network. Next, we will about ways to evaluate the central nodes in a network and learn about spreading patterns on networks. Finally, we will learn about graph neural networks and the interface between network properties and graph-based learning.
Prereqs: linear algebra, calculus, probability, statistics, python programming. Once the quarter starts, instructor approval is required to maintain enrollment in the course, including if students do not have the listed pre-requisite courses completed.