GESIS Training Courses

Scientific Coordination

Marlene Mauk
Tel: +49 221 47694-579

Administrative Coordination

Noemi Hartung
Tel: +49 621 1246-211

Social Network Analysis with R

Mannheim B6, 4-5
Course duration:
9:30-16:30 CEST
General Topics:
Course Level:
Software used:
Students: 500 €
Academics: 750 €
Commercial: 1500 €
Additional links
Lecturer(s): Michal Bojanowski

About the lecturer - Michal Bojanowski

Course description

The course will provide a hands-on tour through the important concepts and methods of Social Network Analysis (SNA). The main goal is to put the participants on a well-lit road towards conducting a typical social-network-analytic project comfortably on their own using R. The focus is on the practical application of key ideas of SNA rather than discussing (social) theories standing behind them. Nonetheless, pointers to the relevant theoretical and applied literature will be provided.
To this end the course will discuss importing network data from various formats, managing network data within R, basic SNA descriptives (including density, transitivity, homophily/segregation, and centrality), community detection, creating effective network visualizations. The course will conclude with coverage of the basics of statistical modeling of networks with Exponential-family Random Graph Models (ERGM) and Stochastic Actor-Oriented Models (SAOM).
Course meetings will consist of two parts. The first part will consist of a presentation, demonstration and discussions on various SNA concepts and methods. The second part will be focused on hands-on training in applying the presented concepts and tools using real data. While the instructors will provide datasets for these exercises, participants are encouraged to bring their own data.
For additional details on the course and a day-to-day schedule, please download the full-length syllabus.

Target group

Participants will find the course useful if:
  • they intend to extend their arsenal of empirical research skills to network-related problems and network data
  • they have experience in conducting SNA research using other tools, but wish to learn doing SNA in R

  • Learning objectives

    By the end of the course participants will:
  • acquire the knowledge and skills necessary to conduct basic SNA project using R on their own
  • have an overview of existing SNA-related tools in R
  • acquire knowledge and skills to further expand their knowledge and skills in SNA and R
    Organisational structure of the Course
    The course will be organized in two 3-hour sessions: lecture/presentation and hands-on lab. Presentations will introduce necessary concepts and demonstrate the discussed tools. Lab sessions will enable the participants to practice, with guidance from the instructors, applying SNA concepts and using tools on real network datasets.


  • Basics of R and RStudio: familiarity with R syntax, working with basic types of R objects such as vectors and data frames
  • Basics of quantitative methodology and statistics (e.g. descriptive statistics, linear regression)
  • For those who would like a primer or refresher in R, we recommend taking the online workshop "Introduction to R" that takes place from 05-07 September 2023.
    Software and hardware requirements
    Participants are expected to bring their own laptops with the following software installed:
  • R
  • RStudio
  • R packages: tidygraph, ggraph, graphlayouts, statnet, netrankr, remotes, tidyverse
  • Example network data to be installed with R command: remotes::install_github('schochastics/networkdata')
    Monday, 25.09.
  • R packages for network data and network analysis
  • Network data representations
  • Tuesday, 26.09.
  • Managing network data
  • Network descriptives
  • Wednesday, 27.09.
  • Static network visualizations
  • Interactive network visualizations
  • Dynamic network visualizations (movies)
  • Thursday, 28.09.
  • Components, cliques, and community detection
  • Homophily and segregation
  • Two-mode networks
  • Friday, 29.09.
  • Testing hypotheses about network structure
  • Exponential-family Random Graph Models (ERGM)
  • Stochastic Actor-Oriented Models (SAOM)