| Fast graph representation learning with PyTorch Geometric M Fey, JE Lenssen arXiv preprint arXiv:1903.02428, 2019 | 5371 | 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 | 3168 | 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 | 1997 | 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 | 585 | 2018 |
| 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 | 466 | 2021 |
| Deep graph matching consensus M Fey, JE Lenssen, C Morris, J Masci, NM Kriege arXiv preprint arXiv:2001.09621, 2020 | 255 | 2020 |
| 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 | 189 | 2021 |
| Group equivariant capsule networks JE Lenssen, M Fey, P Libuschewski Advances in neural information processing systems 31, 2018 | 153 | 2018 |
| Weisfeiler and leman go machine learning: The story so far C Morris, Y Lipman, H Maron, B Rieck, NM Kriege, M Grohe, M Fey, ... Journal of Machine Learning Research 24 (333), 1-59, 2023 | 147 | 2023 |
| Fast graph representation learning with pytorch geometric, 2019 M Fey, JE Lenssen arXiv preprint arXiv:1903.02428, 1903 | 120 | 1903 |
| Temporal graph benchmark for machine learning on temporal graphs S Huang, F Poursafaei, J Danovitch, M Fey, W Hu, E Rossi, J Leskovec, ... Advances in Neural Information Processing Systems 36, 2056-2073, 2023 | 102 | 2023 |
| Hierarchical inter-message passing for learning on molecular graphs M Fey, JG Yuen, F Weichert arXiv preprint arXiv:2006.12179, 2020 | 90 | 2020 |
| Adversarial generation of continuous implicit shape representations M Kleineberg, M Fey, F Weichert arXiv preprint arXiv:2002.00349, 2020 | 72 | 2020 |
| Just jump: Dynamic neighborhood aggregation in graph neural networks M Fey arXiv preprint arXiv:1904.04849, 2019 | 60 | 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 | 39 | 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 | 33 | 2021 |
| Relational deep learning: Graph representation learning on relational databases M Fey, W Hu, K Huang, JE Lenssen, R Ranjan, J Robinson, R Ying, J You, ... arXiv preprint arXiv:2312.04615, 2023 | 22* | 2023 |
| Pytorch frame: A modular framework for multi-modal tabular learning W Hu, Y Yuan, Z Zhang, A Nitta, K Cao, V Kocijan, J Sunil, J Leskovec, ... arXiv preprint arXiv:2404.00776, 2024 | 15 | 2024 |
| CP-and OCF-networks–a comparison C Eichhorn, M Fey, G Kern-Isberner Fuzzy sets and Systems 298, 109-127, 2016 | 11 | 2016 |
| Relbench: A benchmark for deep learning on relational databases J Robinson, R Ranjan, W Hu, K Huang, J Han, A Dobles, M Fey, ... Advances in Neural Information Processing Systems 37, 21330-21341, 2025 | 9 | 2025 |
Jan Eric LenssenMax Planck Institute for Informatics; Kumo.aiPreverjeni e-poštni naslov na mpi-inf.mpg.de