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Scientific Coordination

Dr.
Marlene Mauk
Tel: +49 221 47694-579

Administrative Coordination

Claudia O'Donovan-Bellante
Tel: +49 621 1246-221

Network Analysis in R

About
Location:
Mannheim B6, 4-5
 
General Topics
Course Level
Format
Software used
Duration
Language
Fees
Students: 500 €
Academics: 750 €
Commercial: 1500 €
 
Keywords
Additional links
Lecturer(s): Dr. David Schoch, tba

About the lecturer - Dr. David Schoch

Course description

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.
 
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 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


  • Learning objectives

    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
  •  
    Organizational structure of the course
    The course is structured around three hours of classroom introduction and three hours of hands-on lab sessions. During the lab sessions, participants will work through a lab sheet and can explore their own ideas with a range of provided data. Lecturers will also be available for individual consultations on participants' projects and to support work on assignments during the lab session.


    Prerequisites

  • Basic knowledge of R and RStudio. Participants without prior experience in R should consider taking these courses: Intro to R (workshop) and Introduction to CSS with R (week 1).
  • Basic knowledge of quantitative research methods 
  •  
    Software and hardware requirements
    Participants should bring their own laptops and pre-install the following software/packages:
  • R
  • RStudio
  • igraph, network, sna, graphlayouts, ggraph, backbone, ergm, netrankr
  • Please also download and install one package from GitHub via:
  • devtools::install_github("schochastics/networkdata")
     
    Recommended related courses
  • Egocentric Networks: Theory, Methods, and Applications (Workshop, online, 17.05. - 19.05.2022)
  • R 101 (Workshop, online, 31.08. - 01.09.2022)
  • Introduction to Computational Social Science with R (Fall Seminar, Mannheim, Week 1)
  • Automated Web Data Collection with R (Fall Seminar, Mannheim, Week 2)


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