| Structure-Aware Transformer for Graph Representation Learning D Chen, L O'Bray, K Borgwardt International Conference on Machine Learning (ICML), 2022 | 184 | 2022 |
| Biological network analysis with deep learning G Muzio, L O’Bray, K Borgwardt Briefings in bioinformatics 22 (2), 1515-1530, 2021 | 146 | 2021 |
| Graph Kernels: State-of-the-Art and Future Challenges K Borgwardt, E Ghisu, F Llinares-López, L O’Bray, B Rieck Foundations and Trends in Machine Learning 13 (5-6), 531-712, 2020 | 108 | 2020 |
| Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutions L O'Bray, M Horn, B Rieck, K Borgwardt International Conference on Learning Representations (ICLR), 2021 | 33 | 2021 |
| Filtration curves for graph representation L O'Bray, B Rieck, K Borgwardt Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data …, 2021 | 24 | 2021 |
| Taxonomy of benchmarks in graph representation learning R Liu, S Cantürk, F Wenkel, S McGuire, X Wang, A Little, L O’Bray, ... Learning on Graphs Conference, 6: 1-6: 25, 2022 | 10 | 2022 |
| networkGWAS: a network-based approach to discover genetic associations G Muzio, L O’Bray, L Meng-Papaxanthos, J Klatt, K Fischer, K Borgwardt Bioinformatics 39 (6), btad370, 2023 | 4* | 2023 |
| The magnitude vector of images MF Adamer, E De Brouwer, L O'Bray, B Rieck arXiv preprint arXiv:2110.15188, 2021 | 4 | 2021 |
| Towards a taxonomy of graph learning datasets R Liu, S Cantürk, F Wenkel, D Sandfelder, D Kreuzer, A Little, S McGuire, ... arXiv preprint arXiv:2110.14809, 2021 | 2 | 2021 |
| Leveraging global information for machine learning on graphs L O’Bray ETH Zurich, 2023 | | 2023 |
Karsten BorgwardtDirector, Max Planck Institute of BiochemistryBestätigte E-Mail-Adresse bei biochem.mpg.de
