| Fast graph representation learning with PyTorch Geometric M Fey, JE Lenssen arXiv preprint arXiv:1903.02428, 2019 | 2365 | 2019 |
| Open graph benchmark: Datasets for machine learning on graphs W Hu, M Fey, M Zitnik, Y Dong, H Ren, B Liu, M Catasta, J Leskovec Advances in neural information processing systems 33, 22118-22133, 2020 | 1179 | 2020 |
| Weisfeiler and leman go neural: Higher-order graph neural networks C Morris, M Ritzert, M Fey, WL Hamilton, JE Lenssen, G Rattan, M Grohe Proceedings of the AAAI Conference on Artificial Intelligence 33, 4602-4609, 2019 | 930 | 2019 |
| Splinecnn: Fast geometric deep learning with continuous b-spline kernels M Fey, JE Lenssen, F Weichert, H Müller Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 394 | 2018 |
| Deep graph matching consensus M Fey, JE Lenssen, C Morris, J Masci, NM Kriege arXiv preprint arXiv:2001.09621, 2020 | 160 | 2020 |
| Ogb-lsc: A large-scale challenge for machine learning on graphs W Hu, M Fey, H Ren, M Nakata, Y Dong, J Leskovec arXiv preprint arXiv:2103.09430, 2021 | 139 | 2021 |
| Group equivariant capsule networks JE Lenssen, M Fey, P Libuschewski Advances in neural information processing systems 31, 2018 | 114 | 2018 |
| Fast graph representation learning with PyTorch Geometric. arXiv 2019 M Fey, JE Lenssen arXiv preprint arXiv:1903.02428, 1903 | 62 | 1903 |
| Gnnautoscale: Scalable and expressive graph neural networks via historical embeddings M Fey, JE Lenssen, F Weichert, J Leskovec International Conference on Machine Learning, 3294-3304, 2021 | 55 | 2021 |
| Hierarchical inter-message passing for learning on molecular graphs M Fey, JG Yuen, F Weichert arXiv preprint arXiv:2006.12179, 2020 | 40 | 2020 |
| Adversarial generation of continuous implicit shape representations M Kleineberg, M Fey, F Weichert arXiv preprint arXiv:2002.00349, 2020 | 40 | 2020 |
| Weisfeiler and leman go machine learning: The story so far C Morris, Y Lipman, H Maron, B Rieck, NM Kriege, M Grohe, M Fey, ... arXiv preprint arXiv:2112.09992, 2021 | 39 | 2021 |
| Just jump: Dynamic neighborhood aggregation in graph neural networks M Fey arXiv preprint arXiv:1904.04849, 2019 | 33 | 2019 |
| Recognizing cuneiform signs using graph based methods NM Kriege, M Fey, D Fisseler, P Mutzel, F Weichert International Workshop on Cost-Sensitive Learning, 31-44, 2018 | 23 | 2018 |
| The power of the weisfeiler-leman algorithm for machine learning with graphs C Morris, M Fey, NM Kriege arXiv preprint arXiv:2105.05911, 2021 | 12 | 2021 |
| CP-and OCF-networks–a comparison C Eichhorn, M Fey, G Kern-Isberner Fuzzy sets and Systems 298, 109-127, 2016 | 7 | 2016 |
| 4 Structured Data N Piatkowski, K Morik, N Kriege, C Morris, M Fey, F Weichert, N Bertram, ... Volume 1 Fundamentals, 99-178, 2022 | | 2022 |
| On the Power of Message Passing for Learning on Graph-Structured Data M Fey | | |
Jan Eric LenssenMax Planck Institute for InformaticsVerified email at mpi-inf.mpg.de
