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This technical report presents GPS++, the first-place solution to the Open Graph Benchmark Large-Scale Challenge (OGB-LSC 2022) for the PCQM4Mv2 molecular property prediction task.
Self-consistent equations including exchange and correlation effects
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Neural message passing for quantum chemistry
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Attention is all you need
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Relational inductive biases, deep learning, and graph networks
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Fast graph representation learning with PyTorch Geometric
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Open Graph Benchmark: Datasets for Machine Learning on Graphs
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Graph neural networks with learnable structural and positional representations
Vijay Prakash Dwivedi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, and Xavier Bresson · 2022
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Benchmarking graphormer on large-scale molecular modeling datasets
Yu Shi, Shuxin Zheng, Guolin Ke, Yifei Shen, Jiacheng You, Jiyan He, Shengjie Luo, Chang Liu, Di He, and Tie-Yan Liu · 2022
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One transformer can understand both 2d & 3d molecular data
Shengjie Luo, Tianlang Chen, Yixian Xu, Shuxin Zheng, Tie-Yan Liu, Liwei Wang, and Di He · 2022
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Reducing down(stream)time: Pretraining molecular gnns using heterogeneous ai accelerators
Jenna A. Bilbrey, Kristina M. Herman, Henry Sprueill, Soritis S. Xantheas, Payel Das, Manuel Lopez Roldan, Mike Kraus, Hatem Helal, and Sutanay Choudhury · 2022
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Simple GNN regularisation for 3D molecular property prediction and beyond
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Vijay Prakash Dwivedi and Xavier Bresson · 2020
Cited alongside, same era.
OGB-LSC: A large-scale challenge for machine learning on graphs
Weihua Hu, Matthias Fey, Hongyu Ren, Maho Nakata, Yuxiao Dong, and Jure Leskovec · 2021
Cited alongside, same era.
Geometric deep learning: Grids, groups, graphs, geodesics, and gauges
Michael M Bronstein, Joan Bruna, Taco Cohen, and Petar Veličković · 2021
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First place solution of KDD Cup 2021 & OGB large-scale challenge graph prediction track
Chengxuan Ying, Mingqi Yang, Shuxin Zheng, Guolin Ke, Shengjie Luo, Tianle Cai, Chenglin Wu, Yuxin Wang, Yanming Shen, and Di He · 2021
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Rethinking graph transformers with spectral attention
Devin Kreuzer, Dominique Beaini, William L. Hamilton, Vincent Létourneau, and Prudencio Tossou · 2021
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Mario Michael Krell, Matej Kosec, Sergio P. Perez, and Andrew Fitzgibbon · 2021
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Recipe for a General, Powerful, Scalable Graph Transformer
Ladislav Rampášek, Mikhail Galkin, Vijay Prakash Dwivedi, Anh Tuan Luu, Guy Wolf, and Dominique Beaini · 2022
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Do transformers really perform badly for graph representation?
Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, and Tie-Yan Liu
Cited in the paper.
Jonathan Godwin, Michael Schaarschmidt, Alexander L Gaunt, Alvaro Sanchez-Gonzalez, Yulia Rubanova, Petar Veličković, James Kirkpatrick, and Peter Battaglia · 2022
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Pre-training via denoising for molecular property prediction
Sheheryar Zaidi, Michael Schaarschmidt, James Martens, Hyunjik Kim, Yee Whye Teh, Alvaro Sanchez-Gonzalez, Peter Battaglia, Razvan Pascanu, and Jonathan Godwin · 2022
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GRPE: Relative positional encoding for graph transformer
Wonpyo Park, Woonggi Chang, Donggeon Lee, Juntae Kim, and Seung won Hwang · 2022
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Global self-attention as a replacement for graph convolution
Md Shamim Hussain, Mohammed J Zaki, and Dharmashankar Subramanian · 2022
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Lihang Liu, Donglong He, Xiaomin Fang, Shanzhuo Zhang, Fan Wang, Jingzhou He, and Hua Wu · 2022
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How to better introduce geometric information in equivariant message passing?
Yusong Wang, Shaoning Li, Tong Wang, Zun Wang, Xinheng He, Bin Shao, and Tie-Yan Liu · 2022
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