Dr. Pascal Welke
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I am interested in Data Mining, Applied Graph Theory, Machine Learning, and Human-computer Interaction. I wrote my PhD thesis on 'Probabilistic Frequent Subtree Mining'.
I also teach several courses that are offered by our group in the Bachelors program and Masters program in Computer Science and I supervise BA and MA theses.
Contact
- University of Bonn:
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Phone: +49 228 73 4514
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Room 1.027
Friedrich-Hirzebruch-Allee 8Please send snail mail to
Friedrich-Hirzebruch-Allee 5
53115 Bonn
- email:
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- Other:
- I have an account on ResearchGate. My publications are indexed by dblp and google scholar.
You can give me anonymous feedback (for example on my teaching performance).
Publications
- Janis Kalofolias, Pascal Welke, Jilles Vreeken:
SUSAN: The Structural Similarity Random Walk Kernel.
SIAM International Conference on Data Mining, SDM (accepted), 2021.[preprint] [slides] [video] [conference]
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Pascal Welke, Fouad Alkhoury, Christian Bauckhage, Stefan Wrobel:
Decision Snippet Features.
International Conference on Pattern Recognition, ICPR, 2021.[preprint] [video] [slides] [doi] [conference]
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Till Hendrik Schulz, Tamas Horvath, Pascal Welke, Stefan Wrobel:
A Generalized Weisfeiler-Lehman Graph Kernel.
CoRR abs/2101.08104, 2021.
- Dario Antweiler, Pascal Welke:
Temporal Graph Analysis for Outbreak Pattern Detection in COVID-19 Contact Tracing Networks.
Machine Learning in Public Health Workshop, MLPH@NeurIPS, 2020.
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Pascal Welke:
Efficient Frequent Subgraph Mining in Transactional Databases.
IEEE International Conference on Data Science and Advanced Analytics, DSAA, 2020.
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Pascal Welke, Florian Seiffarth, Michael Kamp, Stefan Wrobel:
HOPS: Probabilistic Subtree Mining for Small and Large Graphs.
ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD, 2020.[preprint] [short video] [slides] [video] [doi] [dblp] [conference]
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Alexander Mehler, Wahed Hemati, Pascal Welke, Maxim Konca, Tolga Uslu:
Multiple Texts as a Limiting Factor in Online Learning: Quantifying (Dis-)similarities of Knowledge Networks across Languages.
Frontiers in Education | Digital Education, 2020.
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Till Schulz, Pascal Welke:
On the Necessity of Graph Kernel Baselines.
Graph Embedding and Mining Workshop, GEM@ECMLPKDD, 2019.
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Pascal Welke:
Frequent Subtree Mining Beyond Forests.
Dissertations in Artificial Intelligence Vol. 348, IOS Press, 2019.[pdf] [slides] [urn] [official publication venue] [dblp] [book]
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Pascal Welke, Tamas Horvath, Stefan Wrobel:
Probabilistic and Exact Frequent Subtree Mining in Graphs Beyond Forests.
Machine Learning, Volume 108, Issue 7, 2019[preprint] [doi] [read-only free official version] [dblp] [journal]
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Pascal Welke, Tamas Horvath, Stefan Wrobel:
Probabilistic Frequent Subtrees for Efficient Graph Classification and Retrieval.
Machine Learning, Volume 107, Issue 11, Springer, 2018.[preprint] [dblp] [doi] [read-only free official version] [journal]
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Till Hendrik Schulz, Tamas Horvath, Pascal Welke, Stefan Wrobel:
Mining Tree Patterns with Partially Injective Homomorphisms.
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML/PKDD, Springer LNCS 11052, 2018.[preprint] [slides] [dblp] [doi] [conference]
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Pascal Welke:
Simple Necessary Conditions for the Existence of a Hamiltonian Path with Applications to Cactus Graphs.
CoRR abs/1709.01367, 2017.
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Pascal Welke, Alexander Markowetz, Torsten Suel, Maria Christoforaki:
3-Hop Distance Estimation in Social Graphs.
IEEE International Conference on Big Data, BigData, IEEE, 2016.[preprint] [slides] [dblp] [doi] [conference]
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Pascal Welke, Tamas Horvath, Stefan Wrobel:
Min-Hashing for Probabilistic Frequent Subtree Feature Spaces.
Proceedings of Discovery Science - 18th International Conference, DS, Springer LNAI 9956, 2016.
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Katrin Ullrich, Jennifer Mack, Pascal Welke:
Ligand Affinity Prediction with Multi-Pattern Kernels.
Proceedings of Discovery Science - 18th International Conference, DS, Springer LNAI 9956, 2016.[preprint] [slides] [dblp] [doi] [conference]
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Pascal Welke, Ionut Andone, Konrad Blaskiewicz, Alexander Markowetz:
Differentiating Smartphone Users by App Usage.
Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp, ACM, 2016.[preprint] [slides] [dblp] [doi] [conference]
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Pascal Welke, Tamas Horvath, Stefan Wrobel:
Probabilistic Frequent Subtree Kernels.
Proceedings of the Fourth Workshop on New Frontiers in Mining Complex Patterns, nfMCP@ECMLPKDD, Selected Extended Papers, Springer LNCS 9607, 2015. -
Pascal Welke, Tamas Horvath, Stefan Wrobel:
On the Complexity of Frequent Subtree Mining in Very Simple Structures.
Proceedings of the Inductive Logic Programming Conference, ILP, Springer LNCS 9046, 2014.[preprint] [slides] [dblp] [doi] [conference]
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Anne-Kathrin Mahlein, Till Rumpf, Pascal Welke, Heinz-Wilhelm Dehne, Lutz Plümer, Ulrike Steiner, Erich-Christian Oerke:
Development of spectral indices for detecting and identifying plant diseases.
Remote Sensing of Environment Volume 128, Elsevier, 2013.
Lecture Notes and Coding Nuggets
- Pascal Welke and Christian Bauckhage
Solving Linear Programming Problems
- Pascal Welke and Christian Bauckhage
Linear Programming for Robust Regression
Community Activities
- We are organizing GEM'21, the third Workshop on Graph Embedding and Mining, collocated with ECMLPKDD'21! I hope to see you there! Consider submitting your paper on learning on or with graphs!
- I have co-organized GEM'20, Workshop on Graph Embedding and Mining, collocated with ECMLPKDD'20.
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I was program chair (with Nico Piatkowski) of the KDML track at LWDA 2020. It has been a pleasure. Here are the proceedings.
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Member of the program committee of PDFL'20, the Workshop on Parallel, Distributed, and Federated Learning, collocated with ECMLPKDD'20.
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Program committee member of ICML'21, AISTATS'21, SDM'21, ICDM'20, ECMLPKDD'20 '21, and NeurIPS'20 '21.
- Member of the program committee of GEM'19, the Workshop on Graph Embedding and Mining, collocated with ECMLPKDD'19.
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Member of the program committee of DMLE'19, the Second Workshop on Distributed Machine Learning at the Edge, collocated with ECMLPKDD'19.
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Reviews for several journals and conferences, e.g. Machine Learning, AMAI, ACM SIGKDD 2016, AISTATS 2020