Dr Eleanor Power

This course is available on the BSc in Politics and Data Science. This course is available as an outside option to students on other programmes where regulations permit and to General Course students.

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).

A combination of classes and lectures totalling 30 hours across Lent Term. This course has a Reading Week in Week 6 of LT.

Students will be expected to submit two formative problem sets that build 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.

Student groups will give an oral presentation of their plan for their group project in the final seminar of the term, for which they will receive peer feedback and verbal feedback from staff.

• 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.

Project (20%) and group project (20%) in the ST.
Problem sets (60%) in the LT.

Four summative problem sets will be marked in four of the weeks. These will constitute 60% of the final overall mark. The group project will be a structured, independent exploration of a social network dataset written up as a report. 20% of the final overall mark will based on the subsection of the group report written by the student, and 20% of the final overall mark will be based on the collectively written sections of the group report.