杏吧论坛

Dr Eleanor Power COL7.09 and Dr Milena Tsvetkova COL8.03

This course is available on the MSc in Applied Social Data Science, MSc in Data Science, MSc in Human Geography and Urban Studies (Research), MSc in Media and Communications (Data and Society), MSc in Social Research Methods, MSc in Statistics, MSc in Statistics (Financial Statistics), MSc in Statistics (Financial Statistics) (杏吧论坛 and Fudan), MSc in Statistics (Financial Statistics) (Research), MSc in Statistics (Research), MSc in Statistics (Social Statistics) and MSc in Statistics (Social Statistics) (Research). 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 Applied Social Data Science, 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).

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 R.

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.

15 hours of lectures and 15 hours of computer workshops in the LT.

Students will be expected to produce 5 problem sets in the LT.

Type: Structured formative problem sets in five of the weeks will build on what was covered in the staff-led lab session, to be completed by the student outside of class. Answers should be formatted and submitted for assessment. 

• 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.

Problem sets (50%) and take-home assessment (50%) in the LT.

5 summative problem sets will be marked in five of the weeks. These will constitute 50% of the final overall mark.