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
  • Kunegis, Jérôme, Jun Sun, Pawan Kumar, Anna Samoilenko, and Giuseppe Pirró. 2023. SynGraphy: Succinct Summarisation of Large Networks via Small Synthetic Representative Graphs. ArXiV Preprint. doi: https://doi.org/10.48550/arXiv.2302.07755.
  • Sun, Jun, Steffen Staab, and Fariba Karimi. 2018. "Decay of Relevance in Exponentially Growing Networks." In Proceedings of the 10th ACM Conference on Web Science (WebSci '18), doi: https://doi.org/10.1145/3201064.3201084.
  • Sun, Jun, Jérôme Kunegis, and Steffen Staab. 2016. "Predicting User Roles in Social Networks using Transfer Learning with Feature Transformation." In 2016 IEEE International Conference on Data Mining Workshop (ICDMW), doi: https://doi.org/10.1109/ICDMW.2016.0026.
  • Sun, Jun, and Jérôme Kunegis. 2016. Wiki-talk Datasets. doi: https://doi.org/10.5281/zenodo.49561.
  • Sun, Jun, Steffen Staab, and Jérôme Kunegis. 2018. "Understanding Social Networks Using Transfer Learning." doi: https://doi.org/10.1109/MC.2018.2701640.

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