Course: Statistical analysis of network data

  • Tim Chumley
  • Areeb Khichi
  • Shirley Xu
  • Young Yang

Textbook: Statistical analysis of network data with R, by Eric D. Kolaczyk and Gábor Csárdi, ISBN: 978-1-4939-0982-7


Networks and network analysis are arguably one of the largest recent growth areas in the quantitative sciences. With roots in mathematics and statistics (graph theory), computer science, and the social sciences, its widely interdisciplinary nature makes it a particularly interesting subject. In this independent study reading course, we take the approach of studying networks and network data through statistical analyses aided by the use of computational tools such as R.

Our main goals are the following:

  • learn some fundamental concepts graph theory and random graph models
  • learn statistical techniques to study real world network data examples
  • use the igraph R package to do visualization and and quantitative analyses of models
  • more to be updated


  • Texts:
    • Statistical analysis of network data with R, by Eric D. Kolaczyk and Gábor Csárdi
    • Statistical Analysis of Network Data: Methods and Models by Eric D. Kolaczyk
    • Networks: An Introduction by Mark Newman
  • 36-720, Statistical Network Models course at CMU run by Cosma Shalizi


We’ll meet once a week on Fridays at 4:00pm. Topics discussed are noted below, together with corresponding R code and Markdown files that accompany the week’s reading.

Week Topic Activities
Sep 4 - Sep 8 Organizational meeting
Sep 11 - Sep 15 Manipulating network data Chapter 2 notes
Sep 18 - Sep 22 Visualizing network data Chapter 3 notes
Sep 25 - Sep 29 Descriptive analysis of network properties
Oct 2 - Oct 6 Counting \(k\)-cycles Chapter 4 notes
Oct 9 - Oct 13 Classical random graph models Random graph notes
Oct 16 - Oct 20 break for Lynk Symposium
Oct 23 - Oct 27 More on structure of classical models Chapter 5 notes
Oct 30 - Nov 3 Small world models and preferential attachment
Nov 6 - Nov 10 Intro to ERGMs, part 1 Chapter 6 notes
Nov 13 - Nov 17 no meeting
Nov 20 - Nov 24 Intro to ERGMs, part 2
Nov 27 - Dec 1 Stochastic root finding Robbins, Monro paper
Dec 4 - Dec 8 Fitting ERGMs ERGM fitting notes