I joined the DMIR group for my PhD studies after receiving my masters degree in Computer Science at the university of Würzburg in early 2019. At first, i was working on click trail analysis and human behavior prediction in the web based on Wikipedia click trails.
I started to work in the REGIO project. My contribution in the project is to analyse locally successful research cooperations based on different properties of the co-author network. For this we leverage the Bayesian approach HypTrails and apply it on graph- structured data. End 2021 after the project has been successfully finalized, I started working on the HydrAS project, which is a natural succession for my research topic.
The goal of this work is to implement a Python framework, which automatically creates all possible hypothesis and calculates their respective evidences according to HypTrail. The input for this framework is supposed to be an attributed multigraph and some distance measures for each attribute of the nodes.
Possible thesis: BA, MP, MA
 Singer, P., Helic, D., Hotho, A. & Strohmaier, M. (2015). HypTrails: A Bayesian Approach for Comparing Hypotheses About Human Trails on the Web. Proceedings of the 24th International Conference on World Wide Web (p./pp. 1003--1013), New York, NY, USA: ACM. ISBN: 978-1-4503-3469-3
 Espín-Noboa, L., Lemmerich, F., Strohmaier, M. et al. JANUS: A hypothesis-driven Bayesian approach for understanding edge formation in attributed multigraphs. Appl Netw Sci 2, 16 (2017). https://doi.org/10.1007/s41109-017-0036-1
Winter term 22/23:
ummer term 22:
Winter term 21/22:
Summer term 21:
Winter term 20/21:
Summer term 20:
Summer term 19:
Winter term 18/19:
CoBERT: Scientific Collaboration Prediction via Sequential Recommendation, in 2021 International Conference on Data Mining Workshops (ICDMW), 45–54, available: https://doi.org/10.1109/ICDMW53433.2021.00013.(2021)
Proximity dimensions and the emergence of collaboration: a HypTrails study on German AI research, Scientometrics, available: https://doi.org/10.1007/s11192-021-03922-1.(2021)
The German and International AI Network Data Set, available: https://doi.org/10.5281/zenodo.3693604.(2020)
Where to Submit Helping Researchers to Choose the Right Venue, in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings, Online: Association for Computational Linguistics, 878–883, available: https://www.aclweb.org/anthology/2020.findings-emnlp.78.(2020)
On the Right Track! Analysing and Predicting Navigation Success in Wikipedia, in Proceedings of the 30th ACM Conference on Hypertext and Social Media, HT ’19, Hof, Germany: ACM, 143–152, available: https://doi.org/10.1145/3342220.3343650.(2019)