STOR 390 is an introductory course to the mathematical tools and computational training needed to understand large-scale networks. It covers basic methods for working with network data that are fundamental for advanced projects in statistical network analysis and machine learning on networks.

The course will introduce network models and structural descriptors, network dynamics and prediction of processes evolving on graphs, modern algorithms for topology inference, community and anomaly detection. All concepts will be illustrated with numerous applications from technological, social, biological, and information networks.

This course will help answer questions such as:

  • Where does “six degrees of separation” come from?
  • How can we make sense of large social networks?
  • What are the underpinnings of Google’s webpage ranking?
  • What are good models for predicting popularity on Twitter?
  • How can we estimate the size of the Internet?