Matthias Fey
Matthias Fey
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Cited by
Cited by
Fast graph representation learning with PyTorch Geometric
M Fey, JE Lenssen
arXiv preprint arXiv:1903.02428, 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
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
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
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
Deep graph matching consensus
M Fey, JE Lenssen, C Morris, J Masci, NM Kriege
arXiv preprint arXiv:2001.09621, 2020
Group equivariant capsule networks
JE Lenssen, M Fey, P Libuschewski
Advances in neural information processing systems 31, 2018
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
Weisfeiler and leman go machine learning: The story so far
C Morris, Y Lipman, H Maron, B Rieck, NM Kriege, M Grohe, M Fey, ...
December, 2021
Fast graph representation learning with pytorch geometric, 2019
M Fey, JE Lenssen
arXiv preprint arXiv:1903.02428, 1903
Hierarchical inter-message passing for learning on molecular graphs
M Fey, JG Yuen, F Weichert
arXiv preprint arXiv:2006.12179, 2020
Adversarial generation of continuous implicit shape representations
M Kleineberg, M Fey, F Weichert
arXiv preprint arXiv:2002.00349, 2020
Just jump: Dynamic neighborhood aggregation in graph neural networks
M Fey
arXiv preprint arXiv:1904.04849, 2019
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, 2024
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
The power of the weisfeiler-leman algorithm for machine learning with graphs
C Morris, M Fey, NM Kriege
arXiv preprint arXiv:2105.05911, 2021
CP-and OCF-networks–a comparison
C Eichhorn, M Fey, G Kern-Isberner
Fuzzy sets and Systems 298, 109-127, 2016
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
From Similarity to Superiority: Channel Clustering for Time Series Forecasting
J Chen, JE Lenssen, A Feng, W Hu, M Fey, L Tassiulas, J Leskovec, ...
arXiv preprint arXiv:2404.01340, 2024
On the power of message passing for learning on graph-structured data
M Fey
Dissertation, Dortmund, Technische Universität, 2022, 2022
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