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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
  • Lietz, Haiko, Andreas Schmitz, and Johann Schaible. 2021. "Analyse sozialer Netzwerke mit digitalen Verhaltensdaten." easy_social_sciences 66 90-98. doi:
  • Lietz, Haiko, Andreas Schmitz, and Johann Schaible. 2021. "Social network analysis with digital behavioral data." easy_social_sciences 66 41-48. doi:
  • Schaible, Johann, Marcos Oliveira, Maria Zens, and Mathieu Génois. 2022. "Sensing Close-Range Proximity for Studying Face-to-Face Interaction." 1. In Handbook of Computational Social Science; Vol 1: Theory, Case Studies and Ethics, edited by Uwe Engel, Anabel Quan-Haase, Sunny Xun Liu, and Lars Lyberg, European Association of Methodology series, 219-239. Abingdon, Oxon: Routledge.
  • Bacaksizlar, N. Gizem. 2019. Understanding Social Movements through Simulations of Anger Contagion in Social Media.
  • Zloch, Matthäus, Maribel Acosta, Daniel Hienert, Stefan Conrad, and Stefan Dietze. 2021. "Characterizing RDF graphs through graph-based measures – framework and assessment." Semantic Web 12 (5): 789-812. doi: