Network Science

Network science aims at developing methods and tools for the collection, processing, and analysis of relational data (e.g., from social media or sensor data) which can be modeled as a network. Network models facilitate the explanation and prediction of the structure and dynamics of social systems.

Our research on Network Science

  • 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 modeling 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
  • Volle, Jonas, Andreas Schmitz, Haiko Lietz, and Richard Münch. 2024. "Group formation in science between homogenization and differentiation: Modeling the development of U.S. and German sociology." International Journal of Sociology. doi: https://doi.org/10.1080/00207659.2024.2357908.
  • Ell, Theresia, Lydia Repke, and Henning Silber. 2024. "Persönliche und mediierte Kommunikation und ihre Auswirkungen auf die mentale Gesundheit aus Netzwerkperspektive." Frühjahrstagung DGNet und DGS:„Digitale Netzwerke: Soziale Formationen im und ums Internet“ , Philipps-Universität Marburg, Marburg, 2024-03-14.
  • Repke, Lydia, Theresia Ell, and Henning Silber. 2023. "Beyond Distancing: An Examination of Social Networks and Mental Health in the Covid-19 Era." Social networks and wellbeing of older adults, Universität zu Köln, Köln, 2023-12-07.
  • Mehta, Aditya, Arun Paudyal, Atul Sharma, Zyanya Ambros, Ipek Baris, Jun Sun, Oul Han, and Akram Sadat Hosseini. 2020. "Does the First Mover Advantage Exist on GitHub?" doi: https://doi.org/10.48550/arXiv.2006.02193.
  • Kunegis, Jérôme, Jun Sun, and Eiko Yoneki. 2023. Guided Graph Generation: Evaluation of Graph Generators in Terms of Network Statistics, and a New Algorithm. ArXiV Preprint. doi: https://doi.org/10.48550/arXiv.2303.00635.

Find out more about our consulting and services: