Machine Learning
with Graphs
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Research
Graphs and networks are ubiquitous in various domains, from chem- and bioinformatics to computer vision and social network analysis. Machine learning with graphs aims to exploit the potential of the growing amount of structured data in all these areas to automate, accelerate, and improve decision-making. Analyzing graph data requires solving problems at the boundaries of machine learning, graph theory, and algorithmics.
Our ambition is to develop methods that are useful for solving concrete problems in real-world applications, especially in drug discovery. We focus on the development of new machine learning and data mining methods for structured data related to the following broad topics:
Graph Embedding
Graph embedding techniques map graphs into a vector space, such that similar graphs are represented by close vectors. We investigate graph kernels and graph neural networks (GNNs) for embedding graphs to solve learning problems. While graph kernels allow operating implicitly in high-dimensional feature spaces, graph neural networks optimize the feature representation for the learning task. We design, theoretically analyze, implement, and empirically evaluate graph embedding methods.
Graph Matching
The term graph matching refers to methods for finding a correspondence between the nodes of two graphs that optimally preserves their node/edge features and adjacency structure. We study exact polynomial-time algorithms for restricted graph classes, heuristics for general large graphs, and learning-based graph matching techniques.
Graph Search
Information systems with graph-structured data require custom methods for specifying, identifying, and retrieving the desired information. We develop efficient methods and index structures for searching large databases of graphs, e.g., regarding similarity or subgraph containment.
Team
Nils
Kriege
Group Leader
Tabea Reichmann
PhD Candidate
Anatol
Ehrlich
External PhD Candidate
Tinatini Buturishvili
External PhD Candidate
Daniel
Kovacic
Research Assistant
Gunda
Nitzsche
Administration
Alumni
| Name | Position | Current Position |
|---|---|---|
| Dr. Lorenz Kummer | PhD Candidate (2022 – 2026) | AI Expert at A1 Telekom Austria AG |
| Dr. Franka Bause | PhD Candidate (2021 – 2026) | Postdoc at CISPA Helmholtz Center |
| Albert Dinstl | Research Assistant (2025 – 2026) | |
| Ole Schlüter | Research Assistant (2024 – 2025) |
Recent News
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Two PhD positions in Graph Neural Networks
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The positions are part of the FWF-funded research project EXOSPHERE – Efficient, Expressive and Robust GNNs. One PhD student will focus on algorithmic and numerical aspects of efficiency and robustness of GNNs, while the other will focus on the theoretical foundations of efficient GNNs, balancing efficiency and expressivity. Application deadline: November 20, 2026 All details…
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Welcome Tabea!
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We are happy to welcome Tabea Reichmann as a new PhD candidate in our group. Tabea started today, on October 15, 2026. She holds a bachelor’s degree in computer science from the University of Vienna and recently completed her master’s studies in Logic and Computation at TU Wien with honors. During her studies, she already…
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Nils gives a talk at the Simons Science School in Mathematics 2026
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Nils gave a talk on “Deep learning on sets and graphs” at the Simons Science School in Mathematics 2026. The Simons Science School in Mathematics is a one-week program for Ukrainian high school students who have excelled at science competitions in mathematics. The goal is to foster the students’ interest in science, to introduce them…
Join Us!
We are a collaborative and ambitious research team advancing machine learning with graphs through both theoretical analysis and methodological innovation for applications.
PhD Students
We regularly have open positions for doctoral researchers. We are open to working with outstanding students from different areas, including computer science and mathematics. If you are interested, please send your CV and up-to-date transcript of records to Nils Kriege.
Master’s and Bachelor’s Students
We are happy to supervise students at the University of Vienna who are interested in our research. An overview of possible thesis topics offered by our group is available in the teaching section or on the Univie Open Topics page. Please get in touch with the specified contact persons and send an up-to-date transcript of records.