Tobias Koopmann, M.Sc.

Chair of Data Science (Informatik X)
University of Würzburg
Am Hubland
97074 Würzburg
Germany
Email: koopmann@informatik.uni-wuerzburg.de
Phone: (+49 931) 31 - 89363
Office: Room B100 (Computer Science Building M2)
Fingerprint: CBC7 EACE BBF3 8CF8 EB3F 3478 A5FB FAA7 8F34 AD4E
Projects and Research Interests
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.
Current Thesis/Practica Topics
Recommendation of Co-Authorship using Hypergraph Networks
Analysis of Transformer Networks with respect to Layer Dropout and Dataset Splits
Implementation of Automatic HypTrails Framework

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[1]. The input for this framework is supposed to be an attributed multigraph[2] and some distance measures for each attribute of the nodes.
Possible thesis: BA, MP, MA
[1] 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
[2] 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
Teaching
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:
Publications
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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)
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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)
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The German and International AI Network Data Set, available: https://doi.org/10.5281/zenodo.3693604.(2020)
- [ BibTeX ]
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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)
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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)