MSc Data Science (Social Analytics) / Course details
Year of entry: 2021
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Course unit details:
Social Network Analysis
|Unit level||FHEQ level 7 – master's degree or fourth year of an integrated master's degree|
|Teaching period(s)||Semester 2|
|Offered by||Social Statistics|
|Available as a free choice unit?||Yes|
- To introduce the concepts of social networks and the various kinds of relation that can occur between members of the network.
- To explain how do describe social networks, including visualisation using different software.
- To show how statistical models can be used for social network analysis. To demonstrate the use of software for modelling social networks – in particular the use of R.
Objectives: On completion of this unit successful students will be able to:
- Understand the concept of a social network, and the various kinds of relations that can occur with it.
- Know how to describe and visualise the network using appropriate software and summary measures.
- Be familiar with how to model a social network using appropriate software, and understand the substantive reasons for doing so.
- Be able to relate social network dependencies, and understand the substantive reasons for modeling these, to complex design more broadly.
- To critically assess the use of social network analysis in the social sciences.
- Use Visone and R for social network analysis, and organise the network data for use with each of these software packages.
- Participate in a discussion about the strengths and weaknesses of a given piece of research that involves social network analysis.
- Understand the main arguments in methodological journal articles on social network analysis.
Teaching and learning methods
This short course is taught early in semester 2. Computer labs are an integrated part of the course. The course will comprise four taught days, including interactive hands-on sessions, and two days entirely based on computer workshops.
Knowledge and understanding
One report equivalent to a 3,000 word essay, and comprising two parts: part one (1500 words) on concepts, description and visualisation of social networks and part two (1500) on statistical models for social networks.
- Borgatti, S.P., Everett, M.G., Johnson, J.C. (2013). Analyzing Social Networks. Sage.
- Crossley, N., Bellotti, E., Edwards, G., Everett, M. G., Koskinen, J., & Tranmer, M. (2015). Social Network Analysis for Ego-Nets. SAGE.
- Hanneman, R.A. and Riddle. M. (2005) Introduction to social network methods. Riverside, CA: University of California, Riverside (published in digital form at http://faculty.ucr.edu/~hanneman/
- Scott, J (2000) Social Network Analysis: A handbook. Sage
- Lusher D, Koskinen J, Robins G [editors] (2013). Exponential Random Graph Models for Social Networks: Theory, Methods, and Applications (Structural Analysis in the Social Sciences). NY: Cambridge University Press.
- Robins, G. (2015). Doing Social Networks Research: Network Research Design for Social Scientists. Sage
- Snijders, T. A. (2011). Statistical models for social networks. Annual Review of Sociology 37, 131–153.
Note: for preliminary reading we recommend only the introductory and discussion chapters or sections of these books and papers; we do not expect you to be familiar with all the technical details prior to the course.
See also Statnet in R (for fitting ERGMS): http://statnet.csde.washington.edu
|Scheduled activity hours|
|Independent study hours|
|Termeh Shafie||Unit coordinator|
|Nick Crossley||Unit coordinator|
Semester 2, six-day course from 10.00am - 4.00pm.