Pascal Welke

I am an Assistant Professor in Data Science at Lancaster University Leipzig and I am affiliated with the Machine Learning research unit at TU Wien. I received my PhD from the University of Bonn. After a PostDoc there I moved to TU Wien before joining LU Leipzig in early 2025. In my research, I investigate learning on and with graphs. In particular, I combine neural networks, graph mining, and graph theory to develop and analyze expressive graph representations, as well as efficient similarity based learning on graphs. I regularly publish and participate in top ML conferences such as NeurIPS, ICML, or AAAI and organize workshops and regular seminars for the graph learning community.

I also teach undergraduate and graduate courses in Computer Science and I supervise BA and MA theses on a regular basis.

My Erdős number is at most 3 (via Torsten Suel and Endre Szemerédi).

1. Contact
2. News
3. Current Preprints
4. Publications
5. Books
6. Non-Archival Venues
7. Lecture Notes and Coding Nuggets
8. Community Activities

If you like pdfs (or hate trees made out of wood), you can download my publication list in pdf format.

Contact

I have accounts on ResearchGate and LinkedIn. My publications are indexed by dblp and google scholar. Some of the code that I write is on github and I may shamelessly advertise my work and activities on BlueSky.

News

1. The organizers of the GLOW reading group wrote a blog post summarizing an intriguing discussion: Community Takes on the Future of Graph Learning. (July 2025)
2. Expressive Pooling for Graph Neural Networks, which started as a small idea at the MLG workshop 2023 in Torino, was now published in TMLR. (July 2025)
3. I was invited to give a talk at the Department of Mathematics of TU Chemnitz. (June 2025)
4. We'll be organizing the 22nd International Workshop on Mining and Learning with Graphs at this year's ECML/PKDD conference in Porto, Portugal! Submit your papers! (June 2025)
5. I gave a talk in the CS Katha Bartha lecture series at NISER, India. (May 2025)
6. I started a new job as Assistant Professor in Data Science at Lancaster University Leipzig in February 2025. (February 2025)
7. Our paper Maximally Expressive GNNs for Outerplanar Graphs will soon be published in the journal Transactions on Machine Learning Research. (January 2025)
8. I gave a keynote talk on Expressive Graph Embeddings via Homomorphism Counting at the LoG Meetup Paris. Here are the slides. (December 2024)
9. I was invited to give a talk at TU Wien's CAIML Seminar. (November 2024)
10. We started the GLOW seminar series. Graph Learning On Wednesdays aims to create an inclusive and accessible environment that encourages interaction, especially for junior researchers and those outside major research labs. Once or twice a month, we discuss recent graph learning papers with the authors. (October 2024)

Current Preprints

1. Maria Bresich, Günther Raidl, Caspian Coleman, Pascal Welke, Steffen Limmer (2025):
   Search Space Reduction Through Machine Learning for the Electric Autonomous Dial-A-Ride Problem.
   International Conference on Machine Learning, Optimization, and Data Science (LOD)
   (accepted for presentation)
   

   [pdf] [conference]

Publications

1. Masahiro Negishi, Thomas Gärtner, Pascal Welke (2025):
   WILTing Trees: Interpreting the Distance Between MPNN Embeddings.
   International Conference on Machine Learning (ICML)
   

   [pdf] [poster] [code] [reviews] [arXiv] [conference]

2. Fouad Alkhoury, Sebastian Buschjäger, Pascal Welke (2025):
   Splitting Stump Forests: Tree Ensemble Compression for Edge Devices (Extended Version).
   Machine Learning (114)
   

   [pdf] [code] [doi] [official free version] [journal]

3. Veronica Lachi*, Alice Moallemy-Oureh*, Andreas Roth*, Pascal Welke* (2025):
   Expressive Pooling for Graph Neural Networks.
   Transactions on Machine Learning Research (TMLR)
   

   [pdf] [video] [code] [reviews] [journal]

4. Dario Antweiler, Jan Pablo Burgard, Marc Harmening, Nicole Marheineke, Andre Schmeißer, Raimund Wegener, Pascal Welke (2025):
   A Regression-Based Predictive Model Hierarchy for Nonwoven Tensile Strength Inference.
   Informed Machine Learning
   

   [pdf] [code] [doi] [book]

5. Franka Bause*, Fabian Jogl*, Patrick Indri, Tamara Drucks, David Penz, Nils Morten Kriege, Thomas Gärtner, Pascal Welke, Maximilian Thiessen (2025):
   Maximally Expressive GNNs for Outerplanar Graphs.
   Transactions on Machine Learning Research (TMLR)
   

   [pdf] [poster] [slides] [video] [code] [reviews] [journal]

6. Raffaele Paolino*, Sohir Maskey*, Pascal Welke, Gitta Kutyniok (2024):
   Weisfeiler and Leman Go Loopy: A New Hierarchy for Graph Representational Learning.
   Advances in Neural Information Processing Systems (NeurIPS)
   (accepted as oral presentation)
   

   [pdf] [poster] [slides] [video] [code] [reviews] [arXiv] [conference]

7. Alexander Pluska, Pascal Welke, Thomas Gärtner, Sagar Malhotra (2024):
   Logical Distillation of Graph Neural Networks.
   International Conference on Knowledge Representation and Reasoning (KR)
   (honorable mention award at the Special Track on Reasoning, Learning, and Decision Making)
   

   [pdf] [poster] [slides] [code] [doi] [arXiv] [conference]

8. Fouad Alkhoury, Pascal Welke (2024):
   Splitting Stump Forests: Tree Ensemble Compression for Edge Devices.
   International Conference on Discovery Science (DS)
   (Best Student Paper Award)
   

   [pdf] [slides] [code] [doi] [conference]

9. Sebastian Müller, Vanessa Toborek, Katharina Beckh, Matthias Jakobs, Christian Bauckhage, Pascal Welke (2023):
   An Empirical Evaluation of the Rashomon Effect in Explainable Machine Learning.
   European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD)
   

   [pdf] [code] [doi] [arXiv] [conference]

10. Pascal Welke*, Maximilian Thiessen*, Fabian Jogl, Thomas Gärtner (2023):
    Expectation-Complete Graph Representations with Homomorphisms.
    International Conference on Machine Learning (ICML)
    

    [pdf] [poster] [slides] [video] [code] [reviews] [arXiv] [conference]

11. Ramsés J. Sánchez, Lukas Conrads, Pascal Welke, Kostadin Cvejoski, César Ojeda (2023):
    Hidden Schema Networks.
    Annual Meeting of the Association for Computational Linguistics (ACL)
    

    [pdf] [poster] [slides] [code] [doi] [arXiv] [bibtex] [conference]

12. Vanessa Toborek, Moritz Busch, Malte Boßert, Christian Bauckhage, Pascal Welke (2023):
    A New Aligned Simple German Corpus.
    Annual Meeting of the Association for Computational Linguistics (ACL)
    

    [pdf] [poster] [code] [doi] [arXiv] [bibtex] [conference]

13. Karishma Mohiuddin, Mirza Ariful Alam, Mirza Mohtashim Alam, Pascal Welke, Michael Martin, Jens Lehmann, Sahar Vahdati (2023):
    Retention Is All You Need.
    International Conference on Information and Knowledge Management (CIKM)
    

    [doi] [arXiv] [bibtex] [conference]

14. Katharina Beckh, Sebastian Müller, Matthias Jakobs, Vanessa Toborek, Hanxiao Tan, Raphael Fischer, Pascal Welke, Sebastian Houben, Laura von Rüden (2023):
    Harnessing Prior Knowledge for Explainable Machine Learning: An Overview.
    IEEE Conference on Secure and Trustworthy Machine Learning (SatML)
    

    [pdf] [video] [doi] [reviews] [arXiv] [bibtex] [conference]

15. Till Hendrik Schulz, Tamás Horváth, Pascal Welke, Stefan Wrobel (2022):
    A generalized Weisfeiler-Lehman graph kernel.
    Machine Learning (111)
    

    [pdf] [code] [doi] [arXiv] [bibtex] [journal]

16. Dario Antweiler, Marc Harmening, Nicole Marheineke, Andre Schmeißer, Raimund Wegener, Pascal Welke (2022):
    Machine learning framework to predict nonwoven material properties from fiber graph representations.
    Software Impacts (14)
    

    [pdf] [code] [reproducible run] [doi] [bibtex] [journal]

17. Dario Antweiler, Marc Harmening, Nicole Marheineke, Andre Schmeißer, Raimund Wegener, Pascal Welke (2022):
    Graph-Based Tensile Strength Approximation of Random Nonwoven Materials by Interpretable Regression.
    Machine Learning with Applications (8)
    

    [pdf] [code] [reproducible run] [doi] [journal]

18. Till Hendrik Schulz, Pascal Welke, Stefan Wrobel (2022):
    Graph Filtration Kernels.
    AAAI Conference on Artificial Intelligence (AAAI)
    

    [pdf] [poster] [slides] [code] [doi] [arXiv] [bibtex] [conference]

19. Richard Palme, Pascal Welke (2022):
    Frequent Generalized Subgraph Mining via Graph Edit Distances.
    IoT Streams for Predictive Maintenance (IoTStreams@ECMLPKDD)
    

    [pdf] [slides] [code] [doi] [bibtex] [workshop]

20. Janis Kalofolias, Pascal Welke, Jilles Vreeken (2021):
    SUSAN: The Structural Similarity Random Walk Kernel.
    SIAM International Conference on Data Mining (SDM)
    

    [pdf] [slides] [video] [code] [doi] [bibtex] [conference]

21. Pascal Welke (2020):
    Efficient Frequent Subgraph Mining in Transactional Databases.
    International Conference on Data Science and Advanced Analytics (DSAA)
    

    [pdf] [slides] [video] [doi] [bibtex] [conference]

22. Pascal Welke, Fouad Alkhoury, Christian Bauckhage, Stefan Wrobel (2020):
    Decision Snippet Features.
    International Conference on Pattern Recognition (ICPR)
    

    [pdf] [slides] [video] [code] [doi] [bibtex] [conference]

23. Pascal Welke, Florian Seiffarth, Michael Kamp, Stefan Wrobel (2020):
    HOPS: Probabilistic Subtree Mining for Small and Large Graphs.
    SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
    

    [pdf] [slides] [video] [code] [doi] [bibtex] [conference]

24. Alexander Mehler, Wahed Hemati, Pascal Welke, Maxim Konca, Tolga Uslu (2020):
    Multiple Texts as a Limiting Factor in Online Learning: Quantifying (Dis-)similarities of Knowledge Networks across Languages.
    Frontiers in Education | Digital Education
    

    [pdf] [doi] [arXiv] [bibtex] [journal]

25. Pascal Welke, Tamás Horváth, Stefan Wrobel (2019):
    Probabilistic and Exact Frequent Subtree Mining in Graphs Beyond Forests.
    Machine Learning (108)
    

    [pdf] [doi] [bibtex] [journal]

26. Pascal Welke, Tamás Horváth, Stefan Wrobel (2018):
    Probabilistic Frequent Subtrees for Efficient Graph Classification and retrieval.
    Machine Learning (107)
    

    [pdf] [doi] [bibtex] [journal]

27. Till Hendrik Schulz, Tamás Horváth, Pascal Welke, Stefan Wrobel (2018):
    Mining Tree Patterns with Partially Injective Homomorphisms.
    European Conference on Machine Learning and Knowledge Discovery in Databases (ECMLPKDD)
    

    [pdf] [slides] [doi] [bibtex] [conference]

28. Pascal Welke, Alexander Markowetz, Torsten Suel, Maria Christoforaki (2016):
    Three-hop Distance Estimation in Social Graphs.
    IEEE International Conference on Big Data (BigData)
    

    [pdf] [slides] [doi] [bibtex] [conference]

29. Pascal Welke, Tamás Horváth, Stefan Wrobel (2016):
    Min-Hashing for Probabilistic Frequent Subtree Feature Spaces.
    International Conference on Discovery Science (DS)
    

    [pdf] [poster] [slides] [doi] [bibtex] [conference]

30. Katrin Ullrich, Jennifer Mack, Pascal Welke (2016):
    Ligand Affinity Prediction with Multi-pattern Kernels.
    International Conference on Discovery Science (DS)
    

    [pdf] [slides] [doi] [bibtex] [conference]

31. Pascal Welke, Ionut Andone, Konrad Blaszkiewicz, Alexander Markowetz (2016):
    Differentiating Smartphone Users by App Usage.
    International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp)
    

    [pdf] [slides] [doi] [bibtex] [conference]

32. Pascal Welke, Tamás Horváth, Stefan Wrobel (2015):
    Probabilistic Frequent Subtree Kernels.
    New Frontiers in Mining Complex Patterns (NFMCP@ECMLPKDD)
    

    [pdf] [slides] [doi] [bibtex] [workshop]

33. Pascal Welke, Tamás Horváth, Stefan Wrobel (2014):
    On the Complexity of Frequent Subtree Mining in Very Simple Structures.
    International Conference on Inductive Logic Programming (ILP)
    

    [pdf] [slides] [doi] [bibtex] [conference]

34. Anne-Kathrin Mahlein, Till Rumpf, Pascal Welke, Heinz-Wilhelm Dehne, Ulrike Steiner, Erich-Christian Oerke (2013):
    Development of Spectral Indices for Detecting and Identifying Plant Diseases.
    Remote Sensing of Environment (128)
    

    [doi] [journal]

Books

1. Michael Kamp et al. (2021):
   Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2021, Virtual Event, September 13-17, 2021, Proceedings, Part I.
   

   [doi] [bibtex] [workshop proceedings]

2. Michael Kamp et al. (2021):
   Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2021, Virtual Event, September 13-17, 2021, Proceedings, Part II.
   

   [doi] [bibtex] [workshop proceedings]

3. Daniel Trabold, Pascal Welke, Nico Piatkowski (2020):
   Proceedings of the Conference "Lernen, Wissen, Daten, Analysen", Online, September 9-11, 2020.
   

   [bibtex] [proceedings]

4. Pascal Welke (2019):
   Efficient Frequent Subtree Mining Beyond Forests.
   Dissertations in Artificial Intelligence (348)
   

   [pdf] [slides] [code] [bibtex] [book]

Non-Archival Venues

1. Franka Bause*, Fabian Jogl*, Patrick Indri, Tamara Drucks, David Penz, Nils Morten Kriege, Thomas Gärtner, Pascal Welke, Maximilian Thiessen (2025):
   Maximally Expressive GNNs for Outerplanar Graphs.
   European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD) Nectar Track
   

   [pdf] [code] [conference]

2. Masahiro Negishi, Thomas Gärtner, Pascal Welke (2025):
   WILTing Trees: Interpreting the Distance Between MPNN Embeddings.
   International School and Conference on Network Science (NetSci)
   (extended abstract)
   

   [pdf] [poster] [code] [conference]

3. Fabian Jogl, Pascal Welke, Thomas Gärtner (2024):
   Is Expressivity Essential for the Predictive Performance of Graph Neural Networks?.
   Workshop on Scientific Methods for Understanding Deep Learning (SciForDL@NeurIPS)
   (accepted as poster presentation)
   

   [pdf] [poster] [code] [reviews] [workshop]

4. Raffaele Paolino*, Sohir Maskey*, Pascal Welke, Gitta Kutyniok (2024):
   Weisfeiler and Leman Go Loopy: A New Hierarchy for Graph Representational Learning.
   Bridging the Gap Between Practice and Theory in Deep Learning (BGPT@ICLR)
   

   [pdf] [poster] [code] [reviews] [arXiv] [workshop]

5. Alexander Pluska, Pascal Welke, Thomas Gärtner, Sagar Malhotra (2024):
   Logical Distillation of Graph Neural Networks.
   Mechanistic Interpretability Workshop (MI@ICML)
   

   [pdf] [poster] [code] [arXiv] [workshop]

6. Veronica Lachi*, Alice Moallemy-Oureh*, Andreas Roth*, Pascal Welke* (2023):
   Graph Pooling Provably Improves Expressivity.
   New Frontiers in Graph Learning (GLFrontiers@NeurIPS)
   

   [pdf] [poster] [reviews] [workshop]

7. Franka Bause*, Fabian Jogl*, Patrick Indri, Tamara Drucks, David Penz, Nils Morten Kriege, Thomas Gärtner, Pascal Welke, Maximilian Thiessen (2023):
   Maximally Expressive GNNs for Outerplanar Graphs.
   New Frontiers in Graph Learning (GLFrontiers@NeurIPS)
   

   [pdf] [poster] [code] [reviews] [workshop]

8. Franka Bause*, Fabian Jogl*, Pascal Welke, Maximilian Thiessen (2023):
   Maximally Expressive GNNs for Outerplanar Graphs.
   Learning on Graphs Conference (LoG)
   (Extended Abstract)
   

   [pdf] [poster] [code] [reviews] [conference]

9. Andrei Dragos Brasoveanu, Fabian Jogl, Pascal Welke, Maximilian Thiessen (2023):
   Extending Graph Neural Networks with Global Features.
   Learning on Graphs Conference (LoG)
   (Extended Abstract)
   

   [pdf] [poster] [code] [reviews] [conference]

10. Maximilian Thiessen*, Pascal Welke*, Thomas Gärtner (2022):
    Expectation Complete Graph Representations using Graph Homomorphisms.
    New Frontiers in Graph Learning Workshop (GLFrontiers@NeurIPS)
    

    [pdf] [poster] [code] [reviews] [workshop]

11. Pascal Welke*, Maximilian Thiessen*, Thomas Gärtner (2022):
    Expectation Complete Graph Representations using Graph Homomorphisms.
    Learning on Graphs Conference (LoG)
    

    [pdf] [poster] [code] [reviews] [conference]

12. Dario Antweiler, Pascal Welke (2020):
    Temporal Graph Analysis for Outbreak Pattern Detection in COVID-19 Contact Tracing Networks.
    Machine Learning in Public Health Workshop (MLPH@NeurIPS)
    

    [pdf] [slides] [workshop]

13. Till Hendrik Schulz, Pascal Welke (2018):
    On the Necessity of Graph Kernel Baselines.
    Graph Embedding and Mining Workshop, (GEM@ECMLPKDD)
    

    [pdf] [poster] [workshop]

14. Pascal Welke (2017):
    Simple Necessary Conditions for the Existence of a Hamiltonian Path with Applications to Cactus Graphs.
    Computer Science Conference for University of Bonn Students (CSCUBS)
    

    [pdf] [arXiv] [bibtex] [workshop]

Lecture Notes and Coding Nuggets

1. Pascal Welke, Christian Bauckhage (2021):
   Linear Programming for Robust Regression.
   ML2R Coding Nugget
   

2. Christian Bauckhage, Pascal Welke (2021):
   Sorting as Linear Programming.
   ML2R Coding Nugget
   

3. Christian Bauckhage, Pascal Welke (2021):
   Sorting as Quadratic Unconstrained Binary Optimization Problem.
   ML2R Coding Nugget
   

4. Christian Bauckhage, Pascal Welke (2021):
   Centering Data- and Kernel Matrices.
   ML2R Theory Nugget
   

5. Pascal Welke, Till Hendrik Schulz, Christian Bauckhage (2021):
   Computational Complexity of Max-Sum Diversification.
   ML2R Theory Nugget
   

6. Christian Bauckhage, Pascal Welke (2021):
   Solving Least Squares Gradient Flows.
   ML2R Coding Nugget
   

7. Pascal Welke, Christian Bauckhage (2020):
   Solving Linear Programming Problems.
   ML2R Coding Nugget
   

Community Activities

1. I organize workshops and (social) gatherings for the Graph ML and broader ML community, e.g.,
   • MLG@ECMLPKDD 2025, the 22nd Workshop on Mining and Learning with Graphs in Porto, Portugal.
   • We started the GLOW seminar series. Graph Learning On Wednesdays aims to create an inclusive and accessible environment that encourages interaction, especially for junior researchers and those outside major research labs. Once or twice a month, we discuss recent graph learning papers with the authors.
   • A Graph ML social at ICML'2024 in beautiful Vienna. It was a great success with panel discussions, food, drinks, and over 200 participants.
   • MLG@ECMLPKDD 2024, the 21th Workshop on Mining and Learning with Graphs in Vilnius, Lithuania.
   • MLG@ECMLPKDD 2023, the 20th Workshop on Mining and Learning with Graphs in Torino, Italy. Here are the proceedings.
   • MLG@ECMLPKDD 2022, the 18th Workshop on Mining and Learning with Graphs in Grenoble, France.
   • GEM@ECMLPKDD 2021, the third Workshop on Graph Embedding and Mining which was a virtual event. Here are the proceedings.
   • GEM@ECMLPKDD 2020, the Workshop on Graph Embedding and Mining which was a virtual event.
   • I was program chair of the KDML track at LWDA 2022.
   • I was a program chair of the KDML track at LWDA 2020. Here are the proceedings.
2. I am a regular program committee member of conferences, e.g.,
   • NeurIPS'20'21'22'23
   • NeurIPS Datasets and Benchmarks'22'23'24'25
   • ICML'21'23'24
   • ICLR'21'22'23'25
   • AAAI'25
   • AISTATS'20'21'22'26
   • ACM SIGKDD'16
   • ECMLPKDD'20'21'22(Journal Track)'23(Journal Track)'24(Journal Track)'25(Journal Track)
   • ICDM'20
   • SDM'21
   • LOG'22'23'24'25
   • SISAP'21'22'24
   • IDA'23'24
   • LWDA'23'24
3. I peer-review for workshops, e.g.,
   • IWCIDM'25, the 13th International Workshop on Computational Intelligence and Data Mining in the Low Tatras, Slovakia.
   • MI@ICML'24, the Mechanistic Interpretability Workshop at ICML'24.
   • PDFL'20'21, the Workshop on Parallel, Distributed, and Federated Learning, collocated with ECMLPKDD'20'21.
   • DMLE'19, the Second Workshop on Distributed Machine Learning at the Edge, collocated with ECMLPKDD'19.
   • GEM'19, the Workshop on Graph Embedding and Mining, collocated with ECMLPKDD'19
4. I was awarded as one of the 20 top LOG'22 reviewers!
5. I was a top reviewer at NeurIPS'23.
6. I review for several journals and academic funding programs, e.g. Machine Learning, Data Mining and Knowledge Discovery, AMAI, KI-Starter NRW.

This page tries to be minimalistic in layout, bandwith, and used tools. It is hosted on github pages, using neat.css stylesheets, and bibtexparser to generate the lists of references.