Fabian Jogl
Univ.Ass. Dipl.-Ing. / BSc
Roles
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PreDoc Researcher
Databases and Artificial Intelligence, E192-02 -
PreDoc Researcher
Machine Learning, E194-06
Courses
2024W
- Introduction to Machine Learning / 194.025 / VU
- Machine Learning Algorithms and Applications / 194.101 / PR
- Project in Computer Science 1 / 194.145 / PR
- Seminar in Artificial Intelligence - Theoretical Aspects of Machine Learning / 194.118 / SE
- Theoretical Foundations and Research Topics in Machine Learning / 194.100 / VU
2025S
- Bachelor Thesis in Computer Science / 194.112 / PR
Publications
- The Expressive Power of Path-Based Graph Neural Networks / Graziani, C., Drucks, T., Jogl, F., Bianchini, M., Scarselli, F., & Gärtner, T. (2024). The Expressive Power of Path-Based Graph Neural Networks. In Z. K. Ruslan Salakhutdinov Katherine Heller, Adrian Weller, Nuria Oliver, Jonathan Scarlett, Felix Berkenkamp (Ed.), Proceedings of the 41st International Conference on Machine Learning. PMLR.
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Maximally Expressive GNNs for Outerplanar Graphs
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Bause, F., Jogl, F., Indri, P., Drucks, T., Penz, D., Kriege, N., Gärtner, T., Welke, P., & Thiessen, M. (2023). Maximally Expressive GNNs for Outerplanar Graphs. In NeurIPS 2023 Workshop: New Frontiers in Graph Learning. NeurIPS 2023 Workshop: New Frontiers in Graph Learning, New Orleans, LA, United States of America (the). OpenReview.net. https://doi.org/10.34726/5433
Download: PDF (880 KB)
Project: StruDL (2023–2027) -
Maximally Expressive GNNs for Outerplanar Graphs
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Bause, F., Jogl, F., Indri, P., Drucks, T., Penz, D., Kriege, N., Gärtner, T., Welke, P., & Thiessen, M. (2023, December 1). Maximally Expressive GNNs for Outerplanar Graphs [Poster Presentation]. Learning-on-Graphs Conference 2023: Local Meetup, München, Germany. https://doi.org/10.34726/5344
Downloads: Paper (880 KB) / Poster (422 KB)
Project: StruDL (2023–2027) -
Extending Graph Neural Networks with Global Features
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Brasoveanu, A. D., Jogl, F., Welke, P., & Thiessen, M. (2023, December 1). Extending Graph Neural Networks with Global Features [Poster Presentation]. Learning-on-Graphs Conference 2023: Local Meetup, München, Germany. https://doi.org/10.34726/5343
Downloads: Paper (365 KB) / Poster (289 KB) -
Extending Graph Neural Networks with Global Features
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Brasoveanu, A. D., Jogl, F., Welke, P., & Thiessen, M. (2023, November 27). Extending Graph Neural Networks with Global Features [Poster Presentation]. Learning on Graphs Conference 2023, Austria. https://doi.org/10.34726/5281
Download: Camera-ready full paper (365 KB) -
Maximally Expressive GNNs for Outerplanar Graphs
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Bause, F., Jogl, F., Welke, P., & Thiessen, M. (2023). Maximally Expressive GNNs for Outerplanar Graphs. In The Second Learning on Graphs Conference (LoG 2023). Second Learning on Graphs Conference (LoG 2023), Austria. OpenReview.net. https://doi.org/10.34726/5434
Download: PDF (541 KB)
Project: StruDL (2023–2027) - Expressivity-Preserving GNN Simulation / Jogl, F., Thiessen, M., & Gärtner, T. (2023). Expressivity-Preserving GNN Simulation. In Advances in Neural Information Processing Systems. 37th Annual Conference on Neural Information Processing Systems (NeurIPS 2023), New Orleans, United States of America (the).
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Extending Graph Neural Networks with Global Features
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Brasoveanu, A. D., Jogl, F., Welke, P., & Thiessen, M. (2023). Extending Graph Neural Networks with Global Features. In The Second Learning on Graphs Conference (LoG 2023). The Second Learning on Graphs Conference (LoG 2023), online, Austria. OpenReview.net. https://doi.org/10.34726/5423
Downloads: PDF (365 KB) / Poster (289 KB) -
Expectation-Complete Graph Representations with Homomorphisms
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Welke, P., Thiessen, M., Jogl, F., & Gärtner, T. (2023). Expectation-Complete Graph Representations with Homomorphisms. In A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato, & J. Scarlett (Eds.), Proceedings of the 40th International Conference on Machine Learning (pp. 36910–36925). Proceedings of Machine Learning Research.
Project: StruDL (2023–2027) -
Weisfeiler and Leman Return with Graph Transformations
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Jogl, F., Thiessen, M., & Gärtner, T. (2022). Weisfeiler and Leman Return with Graph Transformations. In 18th International Workshop on Mining and Learning with Graphs - Accepted Papers. 18th International Workshop on Mining and Learning with Graphs, Grenoble, France. https://doi.org/10.34726/3829
Download: Full paper as PDF (439 KB) -
Reducing Learning on Cell Complexes to Graphs
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Jogl, F., Thiessen, M., & Gärtner, T. (2022). Reducing Learning on Cell Complexes to Graphs. In ICLR 2022 Workshop on Geometrical and Topological Representation Learning. ICLR 2022 Workshop on Geometrical and Topological Representation Learning, Unknown. https://doi.org/10.34726/3421
Download: Paper as PDF (263 KB) -
Historian: A Large-Scale Historical Film Dataset with Cinematographic Annotation
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Helm, D., Jogl, F., & Kampel, M. (2022). Historian: A Large-Scale Historical Film Dataset with Cinematographic Annotation. In 2022 IEEE International Conference on Image Processing (ICIP) (pp. 2087–2091). https://doi.org/10.1109/ICIP46576.2022.9897300
Project: VHH (2019–2023) -
Do we need to Improve message passing? Improving graph neural networks with graph transformations
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Jogl, F. (2022). Do we need to Improve message passing? Improving graph neural networks with graph transformations [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2022.103141
Download: PDF (897 KB) - On (Coalitional) Exchange-Stable Matching / Chen, J., Chmurovic, A., Jogl, F., & Sorge, M. (2021). On (Coalitional) Exchange-Stable Matching. In Algorithmic Game Theory (pp. 205–220). LNCS / Springer. https://doi.org/10.1007/978-3-030-85947-3_14