Dr Eleanor Power and Dr Milena Tsvetkova

This will be listed as an option for the new BSc in Politics and Data Science.

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 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 detection, to random graphs, network formation, and information flow. 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 modelling, analysis and visualisation using R (a statistical programming language).

16 hours and 40 minutes of lectures and 13 hours and 30 minutes of classes in the LT.

A combination of classes and lectures totalling 33.5 hours across Lent Term. (counting the 50 mins above as an hour). This course has a Reading Week in Week 6 of LT.

Students will work on weekly, structured problem sets in the staff-led class sessions. Five of these will be for formative assessment. Example solutions will be provided at the end of each week.

• 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.

Take-home assessment (60%) and problem sets (40%) in the LT.

Five summative problem sets will be marked in five of the weeks. These will constitute 40% of the final overall mark. The take-home assessment (60%) will be submitted in the week following the end of the Lent Term.

Some departments have produced short videos to introduce their courses. Please refer to the course selection videos index page for further information.