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Data-driven Network Science

Key Information

Course details
Option course for MSc, Hilary Term
Reading list
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Professor Renaud Lambiotte


Networks are an important data representation vehicle. There is a long tradition in social sciences concerning social network analysis, but over the last 20 years alternative network analysis methods have been developed under a complex systems approach. The course merges these two approaches and will enable the students to provide a statistical analysis of social data which come in the form of networks.

Data-driven Network Science will introduce the students to network summaries and network models. Then different methods for analysing network data will be presented; these include likelihood-based methods as well as nonparametric methods.

Key Themes

  • Network summaries
  • Models for networks
  • Sampling from networks
  • Testing hypotheses on networks

Learning Objectives

At the end of this course students will…

  1. Understand inherent randomness in networks
  2. Possess knowledge of standard network summaries
  3. Find meso-scale patterns in networks (motifs, communities)
  4. Have the ability to test hypotheses on networks, including setting up appropriate Monte Carlo tests
  5. Be able to report on the analysis of some network data in a concise fashion