Photo credit: Anna Logue

IE 676: Network Analysis



Due to the clash of first scheduled lecture (4th September, 13:45-15:15) with the presentation of the team projects, we will have the first lecture on *Thursday, 5th of September, 12:00-13:30, room C012 (A5.6, Building C)* .  There will be no practical session on the first week. 


The study of networks has received a growing interest over the last decade, particularly due to the increasing „connectedness“ of our society, or better said the change in patterns of connectedness: while some decades ago social relations were restricted to geographically close neighborhoods, the rapid growth of the Internet together with the growing affordability of air transportation have removed such restrictions. Social relations, information as well as epidemics, computer viruses and financial crisis spread around the globe with surprising speed and intensity. These phenomena are possible because of the networks that connect us, and their understanding and analysis is part of the field of network analysis.  

Network analysis is an interdisciplinary field that combines ideas and findings from mathematics, social sciences, biology, economics, physics, and many other areas. Each discipline has contributed techniques and perspectives. This class provides an overview of the field, covering the main phenomena that can be revealed by analysing the structure of complex networks. We will cover the following topics:

  • Basic concepts and network representation
  • Measures of centrality (degree, closeness, betweenness, eigenvector centrality, PageRank and HITS) 
  • Transitivity in networks (clustering coefficient), assortative mixing and homophily (the „birds of a feather flock together“ phenomenon)
  • The large-scale structure of real networks: degree distributions, the small-world phenomenon („six degrees of separation“)
  • Partitioning and community detection: traditional and scalable algorithms
  • Formation and growth of networks: random networks and growing random networks, preferential attachment („rich-get-richer“ phenomenon)
  • Models of epidemics (SI, SIS and SIR)
  • Models of diffusion of innovations, influence maximization, social learning: threshold models, DeGroot model.


Each lecture will mention precisely the relevant pages from each book. Additional material and articles will also be provided when needed. 

  • Mark Newmann. Networks: An Introduction. Oxford University Press, 2010
  • David Easley and Jon Kleinberg. Networks, Crowds and Markets. Cambridge University Press, 2010
  • Matthew O. Jackson.Social and Economic Networks. Princeton University Press, 2008