Network Analysis in R

The course provides an introduction to social network analysis, covering concepts, methods, and data analysis techniques. The focus lies on practical aspects and how to conduct social network research within the statistical programming language R. Theories are not discussed in great detail, but the material is provided for participants to read up on.
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. Besides the analysis, we will also discuss different visualization techniques for networks that can enhance the interpretability of structural features of the network. More advanced topics include a short introduction to statistical modeling tools such as exponential random graph models.
The course is divided into two 3-hour slots, where the first slot is an interactive lecture that gives some theoretical background and relevant functions and packages from the R ecosystem.
These are exemplified by empirical examples from the social sciences and related fields. The second part will be used to work through a worksheet with room for exploring individual interests and research questions, related to the topic of the day. Participants are thus welcome to bring their own research data and questions which can be explored during the interactive part of the course.

Course leader

Dr. David Schoch

Target group

Participants will find the course useful if:
- they wish to use SNA in their research and need an overview of existing methodology
- they have some experience using SNA software (e.g. pajek or visone) but want to transition to R

Course aim

By the end of the course participants will:
- have acquired a broad skill set to read, analyze and visualize network data in R
- understand the ecosystem of R packages around SNA
- know where to get help and find additional resources for SNA in R