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Social Network Analysis and Interpretation

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Course details
MSc option course, Hilary Term
Reading list
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Dr Fabian Braesemann


Today’s world is full of networks. Using networks as an analytical category is not new, though: food webs, supply chains, or social networks have been looked at from a network perspective for a long time; but it is only today’s abundance of digital data that has allowed for an unprecedented rise of network science’ applications in almost all aspects of nature, life and society. From infection chains or air travel over the success of startups, artwork, and Hollywood movies to voting behaviour, innovation or trade: network analysis is applied everywhere and it is only becoming more important. In many fields, network analysis led to new revelations and a better understanding of the complex interactions that are shaping the social systems we all live in.

The course Social Network Analysis and Interpretation provides an introduction to the promising field of network analysis in the social and economic sciences. The lecture will cover the basics and theoretical concepts of social network analysis before dealing with applications. The course will present some of the most relevant research articles from the field, which have revealed the significant role that networks play in many aspects of our lives. The lab sessions will provide hands-on coding examples and data sets to equip students with the tools they need to apply social network analysis on their own.

Overall, Social Network Analysis and Interpretation aims to introduce relevant concepts, theories and applications of social network analysis in an accessible way based on high-quality research.

Topics covered during the course include:

  • Individual elements of networks: nodes & edges, dyads, triads etc.
  • The classics: The strength of weak ties, small-world networks etc.
  • Communities and network structures
  • Network effects and the role of networks in the economy and society
  • A complex systems perspective on networks: adjacent possible, power laws etc.
  • Economic complexity and quantifying success using networks
  • Network Backboning & state-of-the-art research applications of network science