Networks 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 foundations of networks and investigate how the structure of networks affects the spread of signals on the network. Next, we will learn about the geometry and topology of networks, and topological data analysis on networks. Finally, we will learn about graph neural networks and the interface between network properties and learning. This course contains a project.
Topics: network modeling, centrality, community detection, random graph models, network dynamics, simplicial complexes, topological data analysis, graph neural networks.
Prerequisites: PSTAT120A-B, CMPSC 165A-B or equivalent