TK – KRTK, 2022 spring
Dorottya Kisfalusi (TK), Balázs Lengyel (KRTK), László Lőrincz (KRTK), Bence Ságvári (TK)
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: https://www.r-project.org/
Downloading Rstudio: https://www.rstudio.com/products/rstudio/download/#download
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)
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 Networks, 32(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