Microcredential komex Social Network Analysis in R

A comprehensive course on Social Network Analysis to gain advanced insights and skills for analyzing and interpreting complex social networks.

What Is This Course About?
The course provides an introduction to social network analysis, covering concepts, statistical methods and data analysis techniques. 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. More advanced topics include statistical modeling tools such as exponential random graph models. The practical part will be taught within the statistical programming language R.

Learning Goals
By the end of the course participants will:

• Learn to collect, manipulate, visualize and analyze social network data using R.
• Master key concepts and metrics of social network analysis, such as centrality, clusters, and bridges.
• Apply network analysis techniques to real-world datasets, interpreting results to draw meaningful conclusions.
• Have the ability to draw inference about key network mechanisms from observations
• Gain hands-on experience in building, analyzing, and presenting network models to uncover hidden patterns and insights.
• Develop proficiency in using R packages for network analysis and understanding the package ecosystem

Assignments for the Course
Daily quizzes and a final written assignment (to pass)

Schedule

• Monday
  09.00-10.30 – Introduction to the R ecosystem for Networks
  10.30-11.00 – Break
  11.00-12.30 – Fundamental Network Concepts and their Application (Descriptives, Centrality and Cohesive Subgroups)
  12.30-13.30 – Lunch break
  13.30-14.30 – Hands-on examples
• Tuesday
  09.00-10.30 –  Beyond Standard Networks and Network Visualizations
  10.30-11.00 – Break
  11.00-12.30 – Two-Mode Networks, Signed Networks, Multilevel Networks
  12.30-13.30 – Lunch break
  13.30-14.30 – Hands-on examples
• Wednesday
  09.00-10.30 – Introduction to Statistical Models of Networks
  10.30-11.00 – Break
  11.00-12.30 – Parametric and Non-Parametric Methods
  12.30-13.30 – Lunch break
  13.30-14.30 – Hands-on examples
• Thursday
  09.00-10.30 – Cross-Sectional Network Models: Exponential Random Graph Models (ERGMs)
  10.30-11.00 – Break
  11.00-12.30 – ERGMs on two-mode networks and clustering
  12.30-13.30 – Lunch break
  13.30-14.30 – Hands-on examples
• Friday
  09.00-10.30 – Longitudinal Network Models (STERGMs  and SAOMs)
  10.30-11.00 – Break
  11.00-12.30 – Relational Event Models
  12.30-13.30 – Lunch break
  13.30-14.30 – Hands-on examples

Recommended Readings for the Course

• “R for Social Network Analysis” David Schoch, and Termeh Shafie. https://schochastics.github.io/R4SNA/
• “Network Analysis: Integrating Social Network Theory, Method, and Application with R” Craig Rawlings, Jeffrey A. Smith, James Moody, and Daniel McFarland
• Exponential Random Graph Models for Social Networks: Theory, Methods, and Applications” Lusher, Dean, Johan Koskinen, and Garry Robins, eds. (2012), Cambridge: Cambridge University Press.

Who Is Your Instructor?
Termeh Shafie is Professor of Computational Social Science and Data Science at the University of Konstanz. She is a statistician by training and primarily working on developing statistical methods and models to analyze social networks. She has also developed two R packages on the topic ('multigraphr' and 'netropy'). (website: http://mrs.schochastics.net, X: @termehshafie, github: @termehs)

David Schoch is the team lead for “Transparent Social Analytics” in the Department for Computational Social Science at GESIS – Leibniz Institute for the Social Sciences. He has more than a decade of experience in social network analysis, both in empirical and methodological work. David has (co)-organized several workshops and summer schools on SNA and is very experienced with the R ecosystem. He has (co)-authored 20 R packages which cover a large variety of network analytic tasks and have been downloaded more than 1 million times. (website: http://mr.schochastics.net, X: @schochastics, github: @schochastics)