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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  • Paper Accepted at AISTATS 2025

    Paper Accepted at AISTATS 2025

    We are glad to announce our paper “On the Relationship Between Robustness and Expressivity of Graph Neural Networks” by Lorenz Kummer, Wilfried Gansterer and Nils Kriege has been accepted at the 28th International Conference on Artificial Intelligence and Statistics (AISTATS) , which will take place from 3rd – May 5th, 2025 in Mai Kaho, Thailand!…

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  • Paper Accepted at TMLR 2025

    Paper Accepted at TMLR 2025

    We are excited to announce that the paper Maximally Expressive GNNs for Outerplanar Graphs by Franka Bause, Fabian Jogl, Patrick Indri, Tamara Drucks, David Penz, Nils Kriege, Thomas Gärtner, Pascal Welke, and Maximilian Thiessen has been accepted to Transactions on Machine Learning Research (TMLR)! The paper proposes a linear time graph transformation that enables the…

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  • Paper Accepted at AAAI 2025

    Paper Accepted at AAAI 2025

    We are excited to announce the acceptance of our recent work “Crossfire: An Elastic Defense Framework for Graph Neural Networks under Bit Flip Attacks” by Lorenz Kummer, Samir Moustafa, Wilfried Gansterer and Nils Kriege at the 39th Annual AAAI Conference on Artificial Intelligence, which will take place in Philadelphia, Pennsylvania, USA from February 25 –…

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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.