A comprehensive five-day in-person course on Social Network Analysis to gain advanced insights and skills for analyzing and interpreting social networks.
What Is This Course About?
The five-day in-person course provides students to social network analysis, covering concepts, statistical methods and data analysis techniques. Topics covered in this course include the examination of structural properties of the network (e.g. density, homophily, transitivity), identifying key actors via centrality measures and detecting communities. More advanced topics include statistical modelling tools such as exponential random graph models. The practical part will be taught within the statistical programming language R.
Learning Goals
By the end of the course participants will:
Learn to collect, manipulate, visualize and analyze social network data using R.
Master key concepts and metrics of social network analysis, such as centrality, clusters, and bridges.
Apply network analysis techniques to real-world datasets, interpreting results to draw meaningful conclusions.
Have the ability to draw inference about key network mechanisms from observations
Gain hands-on experience in building, analyzing, and presenting network models to uncover hidden patterns and insights.
Develop proficiency in using R packages for network analysis and understanding the package ecosystem
Recommended Readings for the Course
- R for Social Network Analysis” David Schoch, and Termeh Shafie. https://schochastics.github.io/R4SNA/
- Network Analysis: Integrating Social Network Theory, Method, and Application with R” Craig Rawlings, Jeffrey A. Smith, James Moody, and Daniel McFarland
- Exponential Random Graph Models for Social Networks: Theory, Methods, and Applications” Lusher, Dean, Johan Koskinen, and Garry Robins, eds. (2012), Cambridge: Cambridge University Press.
Assignments for the Course
- Daily exercises.
- Final written assignment (to pass).
Schedule
Monday
09:00-10:30h Introduction to SNA and the R Ecosystem for Networks
10:30-11:00h Break
11:00-12:30h R: Network Visualizations
12:30-13:30h Lunch Break
13:30-14:30h Hands-on Examples
Tuesday
09:00-10:30h Fundamental Network Concepts and Their Applications
10:30-11:00h Break
11:00-12:30h R: Visualizing Two-Mode Networks, Signed Networks, and Multilevel Networks
12:30-13:30h Lunch Break
13:30-14:30h Hands-on Examples
Wednesday
09:00-10:30h Introduction to Statistical Models of Networks: Parametric and Non-Parametric Methods
10:30-11:00h Break
11:00-12:30h R: Conditional Uniform Graph Distributions
12:30-13:30h Lunch Break
13:30-14:30h Hands-on Examples
Thursday
09:00-10:30j Cross-Sectional Network Models: Exponential Random Graph Models (ERGMs)
10:30-11:00h Break
11:00-12:30h R: ERGMs on One-Mode and Two-Mode Networks
12:30-13:30h Lunch Break
13:30-14:30h Hands-on Examples
Friday
09:00-10:30h Modeling Longitudinal Network Data (SAOMs, REMs)
10:30-11:00h Break
11:00-12:30h R: Modeling Longitudinal Network Data
12:30-13:30h Lunch Break
13:30-14:30h Hands-on Examples
Recommended Readings for the Course
- “R for Social Network Analysis” David Schoch, and Termeh Shafie. https://schochastics.github.io/R4SNA/
- “Network Analysis: Integrating Social Network Theory, Method, and Application with R” Craig Rawlings, Jeffrey A. Smith, James Moody, and Daniel McFarland
- Exponential Random Graph Models for Social Networks: Theory, Methods, and Applications” Lusher, Dean, Johan Koskinen, and Garry Robins, eds. (2012), Cambridge: Cambridge University Press.
Who Is Your Instructor?
Termeh Shafie is Professor of Computational Social Science and Data Science at the University of Konstanz. She is a statistician by training and primarily working on developing statistical methods and models to analyze social networks. She has also developed two R packages on the topic.
http://mrs.schochastics.net
github @termehs
