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.
Requirements:
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
Schedule
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 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