Stefan Neumann

Stefan Neumann

Assistant Professor TU Wien

Publications

You can also find my articles on DBLP and on Google Scholar.

Note that the publication culture in Computer Science is to publish mostly at conferences and that papers in conference proceedings are fully refereed publications. In Data Science and Machine Learning, authors are usually ordered by contribution; in Algorithms, authors are ordered alphabetically.

Conference Publications

  1. Sebastian Lüderssen, Stefan Neumann, and Pan Peng. “Near-Optimal Four-Cycle Counting in Graph Streams”. In: ACM-SIAM Symposium on Discrete Algorithms (SODA). Authors ordered alphabetically. 2026, pp. 4285–4326.
  2. Sebastian Lüderssen, Stefan Neumann, and Pan Peng. “Four-Cycle Counting in Low-Degeneracy Graph Streams”. In: ACM International Conference on Knowledge Discovery & Data Mining (KDD). To appear. Authors ordered alphabetically. 2026.
  3. Peter Blohm, Florian Chen, Aristides Gionis, and Stefan Neumann. “Discovering Opinion Intervals from Conflicts in Signed Graphs”. In: Neural Information Processing Systems (NeurIPS). Oral presentation (Top 77 submissions out of 21,575). 2025.
  4. Tianyi Zhou, Stefan Neumann, Kiran Garimella, and Aristides Gionis. “Calibrated and Diverse News Coverage”. In: International Conference on Information and Knowledge Management (CIKM). 2025, pp. 4509–4518.
  5. Stefan Neumann, Yinhao Dong, and Pan Peng. “Sublinear-Time Opinion Estimation in the Friedkin-Johnsen Model”. In: ACM Web Conference 2024 (WebConf, formerly WWW). 2024, pp. 2563–2571.
  6. Charlotte Out, Sijing Tu, Stefan Neumann, and Ahad N. Zehmakan. “The Impact of External Sources on the Friedkin-Johnsen Model”. In: International Conference on Information and Knowledge Management (CIKM). 2024, pp. 1815–1824.
  7. Tianyi Zhou, Stefan Neumann, Kiran Garimella, and Aristides Gionis. “Modeling the Impact of Timeline Algorithms on Opinion Dynamics Using Low-rank Updates”. In: ACM Web Conference (WebConf, formerly WWW). Oral presentation (Top 189 submissions out of 2,008). 2024, pp. 2694–2702.
  8. Corinna Coupette, Stefan Neumann, and Aristides Gionis. “Reducing Exposure to Harmful Content via Graph Rewiring”. In: ACM International Conference on Knowledge Discovery & Data Mining (KDD). 2023, pp. 323–334.
  9. Sijing Tu, Stefan Neumann, and Aristides Gionis. “Adversaries with Limited Information in the Friedkin-Johnsen Model”. In: ACM International Conference on Knowledge Discovery & Data Mining (KDD). 2023, pp. 2201–2210.
  10. Klaus-Tycho Foerster, Thibault Marette, Stefan Neumann, Claudia Plant, Ylli Sadikaj, Stefan Schmid, and Yllka Velaj. “Analyzing the Communication Clusters in Datacenters”. In: ACM Web Conference (WebConf, formerly WWW). Authors ordered alphabetically. 2023, pp. 3022–3032.
  11. Thibault Marette, Pauli Miettinen, and Stefan Neumann. “Visualizing Overlapping Biclusterings and Boolean Matrix Factorizations”. In: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML PKDD). 2023, pp. 743–758.
  12. Stefan Neumann and Pan Peng. “Sublinear-Time Clustering Oracle for Signed Graphs”. In: International Conference on Machine Learning (ICML). Oral presentation (Top 118 submissions out of 5,630). 2022, pp. 16496–16528.
  13. Sijing Tu and Stefan Neumann. “A Viral Marketing-Based Model for Opinion Dynamics in Online Social Networks”. In: ACM Web Conference (WebConf, formerly WWW). 2022, pp. 1570–1578.
  14. Monika Henzinger, Stefan Neumann, Harald Räcke, and Stefan Schmid. “Tight Bounds for Online Graph Partitioning”. In: ACM-SIAM Symposium on Discrete Algorithms (SODA). Authors ordered alphabetically. 2021, pp. 2799–2818.
  15. Monika Henzinger, Stefan Neumann, and Andreas Wiese. “Dynamic Approximate Maximum Independent Set of Intervals, Hypercubes and Hyperrectangles”. In: Symposium on Computational Geometry (SoCG). Authors ordered alphabetically. 2020, 51:1–51:14.
  16. Monika Henzinger, Stefan Neumann, and Stefan Schmid. “Efficient Distributed Workload (Re-)Embedding”. In: ACM SIGMETRICS. Authors ordered alphabetically. 2019, pp. 43–44.
  17. Stefan Neumann. “Bipartite Stochastic Block Models with Tiny Clusters”. In: Neural Information Processing Systems (NeurIPS). 2018, pp. 3871–3881.
  18. Monika Henzinger, Andrea Lincoln, Stefan Neumann, and Virginia Vassilevska Williams. “Conditional Hardness for Sensitivity Problems”. In: Innovations in Theoretical Computer Science (ITCS). Authors ordered alphabetically. 2017, 26:1–26:31.
  19. Stefan Neumann and Pauli Miettinen. “Reductions for Frequency-Based Data Mining Problems”. In: IEEE International Conference on Data Mining (ICDM). 2017, pp. 997–1002.
  20. Monika Henzinger and Stefan Neumann. “Incremental and Fully Dynamic Subgraph Connectivity for Emergency Planning”. In: European Symposium on Algorithms (ESA). Authors ordered alphabetically. 2016, 48:1–48:11.
  21. Stefan Neumann, Rainer Gemulla, and Pauli Miettinen. “What You Will Gain By Rounding: Theory and Algorithms for Rounding Rank”. In: IEEE International Conference on Data Mining (ICDM). Best paper candidate. 2016, pp. 380–389.
  22. Stefan Neumann and Andreas Wiese. “This House Proves That Debating is Harder Than Soccer”. In: Fun with Algorithms (FUN). Authors ordered alphabetically. 2016, 25:1–25:14.

Journal Publications

  1. Stefan Neumann and Pauli Miettinen. “Biclustering and Boolean Matrix Factorization in Data Streams”. In: Proc. VLDB Endow. 13.10 (2020), pp. 1709–1722.
  2. Sayan Bhattacharya, Monika Henzinger, and Stefan Neumann. “New amortized cell-probe lower bounds for dynamic problems”. In: Theor. Comput. Sci. (TCS) 779 (2019). Authors ordered alphabetically., pp. 72–87.
  3. Monika Henzinger, Stefan Neumann, and Stefan Schmid. “Efficient Distributed Workload (Re-)Embedding”. In: Proc. of the ACM on Measurement and Analysis of Computing Systems (POMACS) 3.1 (2019). Authors ordered alphabetically. Conference version in SIGMETRICS’19. 13:1–13:38.

Publications at Workshops and National Conferences

  1. Thibault Marette and Stefan Neumann. “Drawing Clusterings of Bipartite Graphs”. In: International Symposium on Graph Drawing and Network Visualization (GD). Poster presentation. 2021.
  2. Stefan Neumann. “Beweisbar Gesetzmäßigkeiten in Daten finden und ausnutzen”. In: Ausgezeichnete Informatikdissertationen 2020. Vol. D-21. LNI. Gesellschaft für Informatik (GI), 2020, pp. 239–248.
  3. Stefan Neumann. “Finding Tiny Clusters in Bipartite Graphs”. In: INFORMATIK. Session Best of Data Science Made in Germany, Austria and Switzerland. 2019, pp. 253–254.
  4. Stefan Neumann, Julian Ritter, and Kailash Budhathoki. “Ranking the Teams in European Football Leagues with Agony”. In: Machine Learning and Data Mining for Sports Analytics (MLSA@PKDD/ECML). 2018, pp. 55–66.

Tutorials and Surveys

  1. Aristides Gionis, Stefan Neumann, and Bruno Ordozgoiti. “Opinion Formation in Social Networks: Models and Computational Problems”. In: ACM Web Conference (WebConf, formerly WWW). Authors ordered alphabetically. 2022, pp. 391–399.
  2. Aristides Gionis, Stefan Neumann, and Bruno Ordozgoiti. “Opinion Formation in Social Networks: Models and Computational Problems”. In: International Joint Conferences on Artificial Intelligence (IJCAI). Authors ordered alphabetically. 2022.
  3. Pauli Miettinen and Stefan Neumann. “Recent Developments in Boolean Matrix Factorization”. In: International Joint Conferences on Artificial Intelligence (IJCAI). Survey Article. 2020, pp. 4922–4928.

Manuscripts

  1. Pablo Barceló, Fabian Jogl, Alexander Kozachinskiy, Matthias Lanzinger, Stefan Neumann, and Cristóbal Rojas. “Message Passing on the Edge: Towards Scalable and Expressive GNNs”. In: CoRR abs/2510.13615 (2025). Authors ordered alphabetically. Manuscript.
  2. Monika Henzinger, Stefan Neumann, and Andreas Wiese. “Explicit and Implicit Dynamic Coloring of Graphs with Bounded Arboricity”. In: CoRR abs/2002.10142 (2020). Authors ordered alphabetically. Manuscript.