Title
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Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark Jan Tönshoff Martin Ritzert Eran Rosenbluth Martin Grohe 63 50 0 01 Sep 2023
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A Generalist Neural Algorithmic Learner Borja Ibarz Vitaly Kurin George Papamakarios Kyriacos Nikiforou Mehdi Abbana Bennani ... Andreea Deac Beatrice Bevilacqua Yaroslav Ganin Charles Blundell Petar Velivcković OOD 65 53 0 22 Sep 2022
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GraphWorld: Fake Graphs Bring Real Insights for GNNs John Palowitch Anton Tsitsulin Brandon Mayer Bryan Perozzi GNN 222 68 0 28 Feb 2022
Top-N: Equivariant set and graph generation without exchangeability Clément Vignac P. Frossard BDL 81 34 0 05 Oct 2021
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Equivariant message passing for the prediction of tensorial properties and molecular spectra Kristof T. Schütt Oliver T. Unke M. Gastegger 67 522 0 05 Feb 2021
Design Space for Graph Neural Networks Jiaxuan You Rex Ying J. Leskovec GNN AI4CE 109 317 0 17 Nov 2020
Graph Neural Networks in Recommender Systems: A Survey Shiwen Wu Fei Sun Wentao Zhang Xu Xie Tengjiao Wang GNN 114 1,202 0 04 Nov 2020
Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning Muhan Zhang Pan Li Yinglong Xia Kai Wang Long Jin 43 188 0 30 Oct 2020
TUDataset: A collection of benchmark datasets for learning with graphs Christopher Morris Nils M. Kriege Franka Bause Kristian Kersting Petra Mutzel Marion Neumann 137 802 0 16 Jul 2020
Open Graph Benchmark: Datasets for Machine Learning on Graphs Weihua Hu Matthias Fey Marinka Zitnik Yuxiao Dong Hongyu Ren Bowen Liu Michele Catasta J. Leskovec 175 2,687 0 02 May 2020
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