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, Mathieu Génois, Johann Schaible, Maria Zens, and Marcos Oliveira. 2023. "Community formation at IC²S² 2017." International Conference on Computational Social Science (IC²S² 2023), Copenhagen, 2023-07-18.
- Génois, Mathieu, Maria Zens, Marcos Oliveira, Clemens Lechner, Johann Schaible, and Markus Strohmaier. 2023. "Combining Sensors and Surveys to Study Social Interactions: A Case of Four Science Conferences." Personality Science 4: 1-24. doi: https://doi.org/10.5964/ps.9957.
- Lietz, Haiko. 2022. "Identifying endogenous time to slice longitudinal network data." XLII Sunbelt International Social Network Conference, Cairns, Australia, 2022-08-30.
- Génois, Mathieu, Maria Zens, Marcos Oliveira, and Johann Schaible. 2023. "[Poster:] Exploration of contact behaviour during scientific conferences." IC2S2 2023: International Conference on Computational Social Science, 2023-07-17.
- Schoch, David. 2023. "signnet: An R package for analyzing signed networks." Journal of Open Source Software 8 (81): 4987. doi: https://doi.org/10.21105/joss.04987.