Stefan Neumann
Assistant Prof. Dr.techn. / BSc MSc
Research Focus
- Information Systems Engineering: 51%
- Logic and Computation: 49%
Research Areas
- Data Mining, Machine Learning, Graph Algorithms, social network analysis, Combinatorial Optimization, Theoretical Compurter Science, Algorithms and Data Structures
About
My research focus is on algorithms for data science and for social network analysis. In particular, I am interested in the following topics:
Foundations of data science: I develop practical data science algorithms with provable guarantees. I am particularly interested in the beyond worst-case analysis of algorithms.
Social network analysis: I study how interventions, such as timeline algorithms, influence the polarization and the disagreement in (online) social networks.
I am also generally interested in graph algorithms and (dynamic) data structures.
For more information, please see my homepage https://neumannstefan.com/.
Role
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Assistant Professor
Machine Learning, E194-06
Courses
2024W
- Bachelor Thesis in Computer Science / 194.112 / PR
- Fundamentals of Digital Systems / 192.134 / VU
- Project in Computer Science 1 / 194.145 / PR
- Seminar for PhD Students / 194.110 / SE
2025S
- Bachelor Thesis in Computer Science / 194.112 / PR
- Project in Computer Science 1 / 194.145 / PR
Projects
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Towards Trustworthy Recommendation Systems for Online Social Networks
2023 – 2030 / Vienna Science and Technology Fund (WWTF)
Publications
- Mitigating Polarization and Disagreement Based on User Interests / Neumann, S. (2024, November 5). Mitigating Polarization and Disagreement Based on User Interests [Keynote Presentation]. MAMMOth Workshop — Part of an EU Horizon Project, Wien, Austria.
- Sublinear-Time Opinion Estimation / Neumann, S. (2024, September 24). Sublinear-Time Opinion Estimation [Conference Presentation]. Dagstuhl Seminar 24391: Statistical and Probabilistic Methods in Algorithmic Data Analysis, Germany.
- Sublinear-Time Clustering Oracle for Signed Graphs / Neumann, S. (2024, May 16). Sublinear-Time Clustering Oracle for Signed Graphs [Conference Presentation]. SIAM Linear Algebra 2024, Paris, France.
- Sublinear-Time Opinion Estimation in the Friedkin--Johnsen Model / Neumann, S., Dong, Y., & Peng, P. (2024). Sublinear-Time Opinion Estimation in the Friedkin--Johnsen Model. In Proceedings of the ACM on Computer Graphics and Interactive Techniques (pp. 2563–2571). Association for Computing Machinery (ACM). https://doi.org/10.1145/3589334.3645572
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The Impact of External Sources on the Friedkin–Johnsen Model
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Out, C., Tu, S., Neumann, S., & Zehmakan, A. N. (2024). The Impact of External Sources on the Friedkin–Johnsen Model. In CIKM ’24: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (pp. 1815–1824). ACM. https://doi.org/10.1145/3627673.3679780
Download: PDF (2.69 MB) -
Modeling the Impact of Timeline Algorithms on Opinion Dynamics Using Low-rank Updates
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Zhou, T., Neumann, S., Garimella, K., & Gionis, A. (2024). Modeling the Impact of Timeline Algorithms on Opinion Dynamics Using Low-rank Updates. In Proceedings of the ACM Web Conference 2024 (pp. 2694–2702). ACM. https://doi.org/10.1145/3589334.3645714
Download: PDF (1.4 MB)