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
- Schoch, David. 2022. "Mathematical Foundations of Network Centrality." Seminar Series at Network Science and Social Network Analysis group, University of Utah, 2022-08-12.
- Shafie, Termeh. 2023. "Goodness of fit tests for random multigraph models." Journal of Applied Statistics 50 (15): 3062-3087. doi: https://doi.org/10.1080/02664763.2022.2099816.
- 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: https://doi.org/10.1038/s42005-022-00896-1.
- Everett, Martin, and David Schoch. 2022. "An extended family of measures for directed networks." Social Networks 70 (July 2022): 334-340. doi: https://doi.org/10.1016/j.socnet.2022.03.005.
- 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: https://doi.org/10.1038/s41598-022-08404-9.