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Network Science

Network science aims to develop methods and tools for the collection, processing and analysis of relational data (e.g. from social media or sensor data) which can be modelled as a network. Network models facilitate to explain and predict the dynamics of social systems.

Our main research areas in the field of network science are:

  • Measuring face-to-face interactions via RFID sensors in various environments (e.g. academic conferences) and combining these data with survey data on behavior and personality traits
  • Networks of interactions between users of online platforms (such as Wikipedia, Reddit, Twitter), statistical modelling of patterns of online interactions (regarding information behavior, cooperation, conflict etc.)
  • Generative network models which aim to explain and predict the behavior of subpopulations, e.g. collaborations between female and male researchers
  • Cultural networks that link geographical regions through shared online preferences
  • Shafie, Termeh. 2022. "Goodness of fit tests for random multigraph models." Journal of Applied Statistics online first. doi:
  • Oliveira, Marcos, Fariba Karimi, Maria Zens, Johann Schaible, Mathieu Génois, and Markus Strohmaier. 2022. "Group mixing drives inequality in face-to-face gatherings." Communications Physics 2022 (5): 127. doi:
  • Everett, Martin, and David Schoch. 2022. "An extended family of measures for directed networks." Social Networks 70 (July 2022): 334-340. doi:
  • Schoch, David, Franziska B Keller, Sebastian Stier, and JungHwan Yang. 2022. "Coordination patterns reveal online political astroturfing across the world." Scientific Reports 2022 (12): 4572. doi:
  • Jadidi, Mohsen, Haiko Lietz, Mattia Samory, and Claudia Wagner. 2022. "The Hipster Paradox in Electronic Dance Music: How musicians trade mainstream success off against alternative status." In Proceedings of the Sixteenth International AAAI Conference on Web and Social Media, edited by Ceren Budak, Meeyoung Cha, and Daniele Quercia, 16, 370-380. Association for the Advancement of Artificial Intelligence. doi: