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
Franka
Bause
PhD Candidate
Lorenz Kummer
PhD Candidate
Anatol
Ehrlich
PhD Candidate
Tinatini Buturishvili
External PhD Candidate
Ole
Schlüter
Research Assistant
Charlotte
Zott
Administration
Recent News
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Group Excursion to Mödling
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Last week, the Kriegegroup went on an excursion to the beautiful hilltops of Mödling in Lower Austria. After arriving at the central station, Charlotte led us to the Bergruine Mödling, followed by a steep ascent to the Husar Temple, where we took the image below. Finally we ended up at the excellent Heuriger called Pferschy-Seper…
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MLG Workshop at ECML/PKDD
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We are happy to announce, that the 22nd Mining and Learning with Graphs workshop will be held at ECML/PKDD on September 15th 2025 in Porto! We will have exciting keynotes by Rebekka Burgholz and Matthias Fey, as well as plenty of time for socializing. Submit your work until 14th of June at http://mlg-europe.github.io! The European…
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Paper accepted at ICML 2025
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We’re thrilled to share that our paper “Weisfeiler and Leman Go Gambling: Why Expressive Lottery Tickets Win” by Lorenz Kummer, Samir Moustafa, Anatol Ehrlich, Franka Bause, Nikolaus Suess, Wilfried Gansterer and Nils Kriege has been accpted at the 42nd International Conference on Machine Learning, July 13th-19th 2025, Vancouver, Canada! This work bridges two important areas…
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 topics for theses offered by our group can be found in the teaching section or on the Univie Open Topics page. Please get in touch with Nils Kriege or the specified contact persons and send an up-to-date transcript of records.