MY461      Half Unit
Social Network Analysis

This information is for the 2017/18 session.

Teacher responsible

 Dr Eleanor Power COL 7.09 and Dr Milena Tsvetkova COL 8.03


This course is available on the MSc in Data Science, MSc in Media and Communications (Data and Society) and MSc in Social Research Methods. This course is available with permission as an outside option to students on other programmes where regulations permit.

Priority will be given to students in the MSc in Data Science, the MSc in Social Research Methods, and then to students from Statistics and Media and Communications (in particular the MSc in Media and Communications (Data and Society) track).

Course content

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 from social media APIs, and structure and analyze this data using R.

Social networks have always been at the center 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 behavior 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 examine the key papers in the development of social network analysis, and 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 measurement, to random graphs, network formation, information flow, and strategic games. Alongside this we will read a series of substantive and seminal papers, shaped in part by the interests of the students and their various backgrounds, with a particular focus on the difficult task of causal inference in social networks. The course will also provide an introduction to network modeling and analysis using R, and network visualization using R and Gephi.


20 hours of lectures and 10 hours of seminars in the LT.

Formative coursework

Students will be expected to produce 10 problem sets in the LT.

Type: Weekly, structured problem sets with a beginning component to be started in the staff-led lab sessions, to be completed by the student outside of class. Answers should be formatted and submitted for assessment. 

Indicative reading

Easley and Kleinberg, Networks, Crowds, and Markets: Reasoning about a highly connected world. Cambridge Univ. Press, 2010.

Newman, Networks: An introduction. Oxford Univ. Press, 2010.

Jackson, Social and Economic Networks. Princeton Univ. Press, 2008.

Borgatti, Stephen P and Martin G. Everett. Analyzing Social Networks. Sage, 2013.

Luke, Douglas A. A User's Guide to Network Analysis in R. Springer, 2016.


In class assessment (50%) and take home exam (50%) in the LT.

Key facts

Department: Methodology

Total students 2016/17: Unavailable

Average class size 2016/17: Unavailable

Controlled access 2016/17: No

Value: Half Unit

Guidelines for interpreting course guide information

Personal development skills

  • Self-management
  • Team working
  • Problem solving
  • Application of information skills
  • Communication
  • Application of numeracy skills
  • Specialist skills