MY361      Half Unit
Social Network Analysis

This information is for the 2025/26 session.

Course Convenor

Availability

Requisites

Additional requisites:

None, although prior knowledge of statistics, including logistic regression, and/or some background in social theory, is desirable.

This course focuses on data about connections, forming structures known as networks. Networks and network data describe an increasingly vast part of the modern world, through connections on social media, communications, financial transactions, and other ties. This course covers the fundamentals of network structures, network data structures, and the analysis and presentation of network data. Students will work directly with network data, and structure and analyse these data using the R statistical programming language.

Social networks have always been at the centre of human interaction, but especially with the explosive growth of the internet, network analysis has become increasingly central to all branches of the social sciences. How do people influence each other, bargain with each other, exchange information (or germs), or interact online? A diverse array of deep questions about human behaviour can only be answered by examining the social networks encompassing and shifting around us. Network analysis has emerged as a cross-disciplinary science in its own right, and has in fact proven to be of even greater generality and broader applicability than just the social, extending to ecology, physics, genetics, computer science, and other domains.

This course will develop the theory and methodological tools needed to model and predict social networks and use them in social sciences as diverse as sociology, political science, economics, health, psychology, history, or business. The core of the course will comprise the essential tools of network analysis, from centrality, homophily, and community detection, to random graphs, network formation, and information flow. The course will also provide an introduction to network modelling, analysis and visualisation using R (a statistical programming language).

15 hours of lectures and 15 hours of classes in the Winter Term.

This course has a reading week in Week 6 of Winter Term.

Students will be expected to submit one formative problem set that builds on what was covered in the staff-led lab sessions, to be completed by the student outside of class. Example answers and written feedback will be given.

• Newman, M.E.J. (2010). Networks: An introduction. Oxford, UK: Oxford University Press.
• Scott, J. (2017). Social Network Analysis. Los Angeles: SAGE. 4th edition.
• Easley, D., and Kleinberg, J. (2010). Networks, Crowds, and Markets: Reasoning about a highly connected world. New York: Cambridge University Press.

Exam (40%), duration: 120 Minutes in the Spring exam period

Presentation (15%)

Problem sets (45%)