Analysis of Social Networks
People
Description
The goal of social network analysis is to understand how agents in a system are connected, and how connectivity affects their individual and collective behavior. This course is an introduction to the concepts, statistical methods and applications of social network analysis. Emphasis will be on descriptive, inferential and model-based methods for the representation and analysis of various kinds of social networks. The application of those methods is illustrated through examples and practical sessions involving the analysis of network data using the software R. No prior experience with R will be assumed.
Objectives
By the end of the course participants will be able to master the main mathematical and computational methods used in the analysis of social networks, and know how to apply those methods for the analysis to real-life network data.
Teaching mode
In presence
Learning methods
Lectures and practical exercises
Slides and lecture notes provided by the lecturer
Examination information
One midterm test (20%), three individual assignments (30%), a final exam (50%)
Bibliography
-
Butts, Carter T.. "Social network analysis: A methodological introduction" Asian Journal Of Social Psychology, 11, 1 (2008): 13-41.
10.1111/j.1467-839x.2007.00241.x - Hennig, Marina, Borgatti, Stephen P.. Studying social networks: a guide to empirical research. Frankfurt: Campus, 2012.
- Kolaczyk, Eric D., Csárdi, Gábor, Gábor, Csárdi. Statistical analysis of network data with R. New York: Springer, 2014.
- Kolaczyk, Eric D.. Statistical analysis of network data: methods and models. New York: Springer, 2009.
-
Labianca, Giuseppe, Brass, Daniel J., Mehra, Ajay, Borgatti, Stephen P.. "Network Analysis in the Social Sciences" Science, 323, 5916 (2009): 892-895.
10.1126/science.1165821 - Wasserman, Stanley, Faust, Katherine. Social Network Analysis: Social Network Analysis. Cambridge University Press, 1994.
Education
- Master of Science in Computational Science, Lecture, Elective, 1st year
- Master of Science in Computational Science, Lecture, Elective, 2nd year
