• Teaching

    A Masters-level Course on Networks

    This course is a practical introduction to network science with significant emphasis on dynamics of networks (about 1/3 of the course) and a light dusting of deeper topics in graph theory. A portion of this course was developed for a junior level undergraduate course on Network Modeling within Engineering Systems and Design at the Singapore University of Technology and Design. It was subsequently expanded and is now offered as a masters-level class Dynamics of Complex Networks and Systems at the University of Texas at Dallas. This course is executed as a flipped classroom, in which the in-class portion is largely driven by a set of seven Python-based laboratory assignments. Readings are typically assigned from Newman’s Networks.


    Lab 1: Network Fundamentals, Representations

    Lab 2: Paths, Components, Flow

    Lab 3: Centrality

    Lab 4: Degree Distribution, Random Networks

    Lab 5: Modularity and Community Detection

    Lab 6: Diffusion and Infection Models

    Lab 7: Dynamical Systems and Stability

    Complete Data Files (this omits a large file used in Lab 5; please contact me if you want to get access to this file)