Spring 2023: This course will meet on Mondays and Wednesdays from 1:00 - 2:30pm PST. We'll use Slack as the course website.

Teaching Style: My teaching style is incredibly collaborative and much of the learning of the material is done with students in groups discussing the mathematics, as opposed to me explaining it via lecture.

About the Course: This course will be an introduction to the field of network science with an emphasis on the mathematical aspects and properties of networks. A network is an accessible yet powerful structure used to represent and study relationships. In practice, networks model different phenomena arising in fields such as biology, economics, sociology, computer science, and physics. In this class, we’ll look rigorously at the mathematical structure of networks (this field is often referred to as graph theory), while also considering real world models, such as spread of disease, web link analysis, and financial networks. We'll work with real world datasets and use a Python library, NetworkX, to analyze the data. This course has no prerequisites.

An accessible example of a network is a social network. A social network is created when we represent people by vertices (or nodes) and then draw an edge between two people who know each other. On a social network, we can study things like the spread of information or disease throughout the network or the Six Degrees of Separation problem. 

The mathematical field of graph theory and the more applied field of network science both study the same types of structures (networks/graphs), but are often treated separately. In this class, I will combine tools from each so that students learn both the theory behind and applications of networks. By considering different real world examples of networks each week, we will develop our own motivation for properties that are useful to analyze.

Here is the syllabus from Spring 2024, the last time I taught this course.