Social Network Analysis

TK – KRTK, 2022 spring

Dorottya Kisfalusi, Balázs Lengyel, László Lőrincz, Bence Ságvári

Course aim

The course intends to introduce the theoretical and methodological aspects of social network analysis.


Basic knowledge in R.

Preparations for the course

Participants are expected to use their own laptops. Please install R and Rstudio on your laptop prior to the first lab.

Downloading R:

Downloading Rstudio:


Week 1 (2*80 minutes)-  7th April, 9 - 12 AM: Introduction, elementary social network concepts, descriptive analysis

Week 2 (2*80 minutes) - 14th April, 9 - 12 AM: Visualization, spatial networks

Week 3 (2*80 minutes) - 21th April, 9 - 12 AM: Network models, introduction to exponential random graph models (ERGMs)

Week 4 (2*80 minutes) - 28th April, 9 - 12 AM: Modelling selection and influence in social networks: Stochastic actor-oriented models (SAOMs)

Suggested literature

Kolaczyk, E. D., & Csárdi, G. (2014). Statistical Analysis of Network Data with R. New York: Springer.

Lusher, D., Koskinen, J., & Robins, G. (Eds.). (2013). Exponential random graph models for social networks: Theory, methods, and applications. Cambridge University Press.

Snijders, T. A., Van de Bunt, G. G., & Steglich, C. E. (2010). Introduction to stochastic actor-based models for network dynamics. Social Networks32(1), 44-60.

Steglich, Ch., T.A.B. Snijders, and M. Pearson (2010). Dynamic networks and behavior: Separating selection from influence. Sociological Methodology 40: 329‐393