Data Science for Complex Networks

This course examines complex systems through the eyes of a computer scientist. We will use the representative power of graphs to model networks of social, technological, or biological interactions. Network analysis provides many computational, algorithmic, and modeling challenges. We begin by studying graph theory, allowing us to quantify the structure and interactions of social and other networks. We will then explore how to practically analyze network data and how to reason about it through mathematical models of network structure and evolution. We will also investigate algorithms that extract basic properties of networks in order to find communities and infer node properties. Finally, we will study a range of applications including robustness and fragility of networks such as the internet, spreading processes used to study epidemiology or viral marketing, and the ranking of webpages based on the structure of the webgraph. This course combines concepts from computer science and applied mathematics to study networked systems using data mining. Prerequisites: CSE 247, ESE 326, MATH 309, and programming experience (note: we will parse data and analyze networks using Python)