I am a Lecturer (Assistant Professor) in Algorithms and Computing Systems at the University of Liverpool, UK.
My research is in algorithmic data analysis, with a particular focus on graphs and networks. I develop efficient algorithms and learning methods for extracting structure and knowledge from complex and evolving data, combining mathematical and computational foundations with scalable methods for real-world problems.
My current research interests include temporal and dynamic networks, graph and network mining, graph representation learning, and adaptive and resource-aware algorithms. I am particularly interested in algorithmic problems where information, computation, or interactions are limited or costly, as well as in multilayer and higher-order network data.
I received my PhD from the University of Bonn, where I worked with Petra Mutzel and subsequently continued as a postdoctoral researcher. I later joined KTH Royal Institute of Technology in Stockholm as a postdoctoral researcher, working with Aristides Gionis.
Profiles: Google Scholar · DBLP
My Erdős number is at most 3 (via Giuseppe F. Italiano → Craig A. Tovey → Paul Erdős).
You can find my CV here.
Linguistic Monoculture in LLM-Assisted Language Use
Suhas Thejaswi, Juhi Kulshreshta, Lutz Oettershagen
arxiv:2607.27134, 2026
Query-Limited Community Recovery in Stochastic Block Models
Sabyasachi Basu, Manuj Mukherjee, Lutz Oettershagen, Suhas Thejaswi
arxiv:2606.02055, 2026
Top-k on a Budget: Adaptive Ranking with Weak and Strong Oracles
Lutz Oettershagen
arXiv:2601.20989, 2026
A Hierarchical Quantized Tokenization Framework for Task-Adaptive Graph Representation Learning
Yang Xiang, Li Fan, Chenke Yin, Lutz Oettershagen, Chengtao Ji
arxiv:2510.12369, 2026
Unsupervised Multi-Scale Gromov-Wasserstein Hypergraph Alignment
Lutz Oettershagen, Honglian Wang, Aristides Gionis
IEEE International Conference on Data Mining (ICDM), 2026
arxiv:2608.29635
Consistent Tie-Strength Labeling for Multilayer Strong Triadic Closure
Lutz Oettershagen, Athanasios L. Konstantinidis, Fariba Ranjbar, Giuseppe F. Italiano
ECML PKDD Journal Track - Data Mining and Knowledge Discovery, 2026
Paper (code)
Efficient Banzhaf-Based Data Valuation for k-Nearest Neighbors Classification
Guangyi Zhang, Lutz Oettershagen, Lixu Wang, Aristides Gionis
International Conference on Very Large Data Bases (VLDB), 2026
arxiv:2605.21033
Fair Minimum Labeling: Efficient Temporal Network Activations for Reachability and Equity
Lutz Oettershagen, Othon Michail
Conference on Neural Information Processing Systems (NeurIPS), 2025
arXiv:2510.03899
Streaming Stochastic Submodular Maximization with On-Demand User Requests
Honglian Wang, Sijing Tu, Lutz Oettershagen, Aristides Gionis
Conference on Neural Information Processing Systems (NeurIPS), 2025
arXiv:2601.10901
An Edge-Based Decomposition Framework for Temporal Networks
Lutz Oettershagen, Athanasios L. Konstantinidis, Giuseppe F. Italiano
ACM International Conference on Web Search and Data Mining (WSDM), 2025
arXiv:2309.11843 (code)
Finding Densest Subgraphs with Edge-Color Constraints
Lutz Oettershagen, Honglian Wang, Aristides Gionis
The ACM Web Conference (WWW), 2024
arXiv:2402.09124 (code)
A Higher-Order Temporal H-Index for Evolving Networks
Lutz Oettershagen, Nils M. Kriege, Petra Mutzel
ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2023
arXiv:2305.16001 (slides) (code)
An Index For Temporal Closeness Computation in Evolving Graphs
Lutz Oettershagen, Petra Mutzel
SIAM International Conference on Data Mining (SDM), 2023
arXiv:2111.10095 (slides) (code)
A Temporal Graphlet Kernel For Classifying Dissemination in Evolving Networks
Lutz Oettershagen, Nils M. Kriege, Claude Jordan, Petra Mutzel
SIAM International Conference on Data Mining (SDM), 2023
arXiv:2209.07332 (slides) (code)
Inferring Tie Strength in Temporal Networks
Lutz Oettershagen, Athanasios L. Konstantinidis, Giuseppe F. Italiano
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML PKDD), 2022
arXiv:2206.11705 (slides) (code)
Temporal Walk Centrality: Ranking Nodes in Evolving Networks
Lutz Oettershagen, Petra Mutzel and Nils Kriege
The ACM Web Conference (WWW), 2022
arXiv:2202.03706 (slides) (code)
Spatio-Temporal Top-k Similarity Search for Trajectories in Graphs
Lutz Oettershagen, Anne Driemel, Petra Mutzel, 2021
arXiv:2009.06778 (code)
Efficient Top-k Temporal Closeness Calculation in Temporal Networks
Lutz Oettershagen, Petra Mutzel
IEEE International Conference on Data Mining (ICDM), 2020
(slides) (code)
Temporal Graph Kernels for Classifying Dissemination Processes
Lutz Oettershagen, Nils Kriege, Christopher Morris, Petra Mutzel
SIAM International Conference on Data Mining (SDM), 2020
arXiv:1911.05496 (code)
On the Enumeration of Bicriteria Temporal Paths
Petra Mutzel, Lutz Oettershagen
Theory and Applications of Models of Computation (TAMC), 2019
arXiv:1812.02507
The Crossing Number of Semi-Pair-Seq-Shellable Drawings of Complete Graphs
Petra Mutzel, Lutz Oettershagen
Canadian Conference on Computational Geometry (CCCG), 2018
arXiv:1805.06780
The Crossing Number of Seq-Shellable Drawings of Complete Graphs
Petra Mutzel, Lutz Oettershagen
International Workshop on Combinatorial Algorithms (IWOCA), 2018
arXiv:1803.07515
Consistent Tie-Strength Labeling for Multilayer Strong Triadic Closure
Lutz Oettershagen, Athanasios L. Konstantinidis, Fariba Ranjbar, Giuseppe F. Italiano
ECML PKDD Journal Track - Data Mining and Knowledge Discovery, Springer, 2026
Paper (code)
Inferring Tie Strength in Temporal Networks
Lutz Oettershagen, Athanasios L. Konstantinidis, Giuseppe F. Italiano
Data Mining and Knowledge Discovery, Springer, 2025
Paper
Computing Top-k Temporal Closeness in Temporal Networks
Lutz Oettershagen, Petra Mutzel
Knowledge and Information Systems, Springer, 2022
Invited article
Classifying Dissemination Processes in Temporal Graphs
Lutz Oettershagen, Nils Kriege, Christopher Morris, Petra Mutzel
Big Data, Mary Ann Liebert, 2020
Invited article
TGLib: An Open-Source Library for Temporal Graph Analysis
Lutz Oettershagen, Petra Mutzel
ICDM’22 Open Project Forum, 2022
arXiv:2209.12587
A Temporal Graphlet Kernel For Classifying Dissemination in Evolving Networks
Lutz Oettershagen, Nils M. Kriege, Claude Jordan, Petra Mutzel
18th International Workshop on Mining and Learning with Graphs, 2022
arXiv:2209.07332
Best paper award
I develop and maintain TGLib, an open-source C++ library for analyzing and processing temporal graphs.
Mining Cohesive Structures in Temporal Graphs invited tutorial at Dagstuhl seminar “Temporal Graphs: Structure, Algorithms, Applications”, 2026.
Mining Temporal Networks together with Aristides Gionis and Ilie Sarpe at SDM 2025.
Mining Temporal Networks together with Aristides Gionis and Ilie Sarpe presented at the WebConf’24.
Please refer to my CV for an overview of my previous teaching experiences.