Statistical Analysis of Network Data

Over the past decade, the study of so-called "complex networks" — that is, network-based representations of complex systems — has taken the sciences by storm. Researchers from biology to physics, from economics to mathematics, and from computer science to sociology, are more and more involved with the collection, modeling and analysis of network-indexed data. With this enthusiastic embrace of networks across the disciplines comes a multitude of statistical challenges of all sorts — many of them decidedly non-trivial. In this short course, we will cover a brief overview of the foundations common to the statistical analysis of network data across the disciplines, from a statistical perspective, in the context of topics like network summary and visualization, network sampling, network modeling and inference, and network processes. Concepts will be illustrated drawing on examples from bioinformatics, computer network traffic analysis, neuroscience, and social networks.

Biography: Eric Kolaczyk is Professor of Statistics, and Director of the Program in Statistics, in the Department of Mathematics and Statistics at Boston University. Prof. Kolaczyk's main research interests currently revolve around the statistical analysis of network-indexed data, and include both the development of basic methodology and inter-disciplinary work with collaborators in bioinformatics, computer science, geography, neuroscience, and sociology. Besides various research articles on these topics, he has also authored two books in this area — Statistical Analysis of Network Data: Methods and Models (Springer, 2009) and Statistical Analysis of Network Data with R (Springer, 2014, with Gábor Csárdi). He has given various short courses on material from his books, including for the Center for Disease Control (CDC) and the Statistical and Applied Mathematical Sciences Institute (SAMSI) in the US as well as similar venues in Belgium, England, and France.

Material in the short course will be based on Prof. Kolaczyk's books, although the course will not assume that participants have purchased either book.

Course Outline:

Day 1:
• Introduction, Background, and Descriptive Statistics
• Network Mapping
• Network Characterization

Day 2:
• Network Sampling
• Network Topology Inference
• Network Modeling