Statistical Network Analysis

3 units

This is an advanced course on the statistical analysis of network data, covering theory, methodology, and applications. The course will cover basic graph theory, commonly used models for random graphs (e.g. Erdos-Renyi, exponential random graph models, stochastic block models, random dot product graphs, graphons), and canonical statistical inference problems within these frameworks (e.g., hypothesis testing, community detection, construction of prediction intervals). Time permitting, additional topics such as dynamic network modeling and causal inference under network interference will also be discussed. This course is intended for PhD students with appropriately strong mathematical background. However, the course is open to anyone interested in the topic.