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There has been a recent surge in transformer-based architectures for learning on graphs, mainly motivated by attention as an effective learning mechanism and the desire to supersede handcrafted operators characteristic of message passing schemes.
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“Quantum chemistry structures and properties of 134 kilo molecules”
Raghunathan Ramakrishnan, Pavlo. Dral, Matthias Rupp and O. von Lilienfeld · 2014
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“Quantum chemistry structures and properties of 134 kilo molecules”
Raghunathan Ramakrishnan, Pavlo. Dral, Matthias Rupp and O. von Lilienfeld · 2014
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“Quantum chemistry structures and properties of 134 kilo molecules”
Raghunathan Ramakrishnan, Pavlo. Dral, Matthias Rupp and O. von Lilienfeld · 2014
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“Attention is All you Need”
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Wengong Jin, Regina Barzilay and Tommi Jaakkola · 2018
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“A simple yet effective baseline for non-attribute graph classification”
Chen Cai and Yusu Wang · 2018
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“Deeper insights into graph convolutional networks for semi-supervised learning”
Qimai Li, Zhichao Han and Xiao-Ming Wu · 2018
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“Graph Attention Networks”
Petar Veličković et al · 2018
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“Junction Tree Variational Autoencoder for Molecular Graph Generation”
Wengong Jin, Regina Barzilay and Tommi Jaakkola · 2018
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“A simple yet effective baseline for non-attribute graph classification”
Chen Cai and Yusu Wang · 2018
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“On the Limitations of Representing Functions on Sets”
Edward Wagstaff et al · 2019
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“Triton: an intermediate language and compiler for tiled neural network computations”
Philippe Tillet, H.. Kung and David Cox · 2019
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“Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks”
Juho Lee et al · 2019
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“PyTorch: An Imperative Style, High-Performance Deep Learning Library”
Adam Paszke et al · 2019
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“PyTorch: An Imperative Style, High-Performance Deep Learning Library”
Adam Paszke et al · 2019
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“Decoupled Weight Decay Regularization”
Ilya Loshchilov and Frank Hutter · 2019
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“On the Limitations of Representing Functions on Sets”
Edward Wagstaff et al · 2019
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“Triton: an intermediate language and compiler for tiled neural network computations”
Philippe Tillet, H.. Kung and David Cox · 2019
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“Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks”
Juho Lee et al · 2019
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“PyTorch: An Imperative Style, High-Performance Deep Learning Library”
Adam Paszke et al · 2019
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“PyTorch: An Imperative Style, High-Performance Deep Learning Library”
Adam Paszke et al · 2019
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“Decoupled Weight Decay Regularization”
Ilya Loshchilov and Frank Hutter · 2019
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“A Deep Learning Approach to Antibiotic Discovery”
Jonathan. Stokes et al · 2020
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“Directional Message Passing for Molecular Graphs”
Johannes Gasteiger, Janek Groß and Stephan Günnemann · 2020
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“Principal Neighbourhood Aggregation for Graph Nets”
Gabriele Corso et al · 2020
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“PairNorm: Tackling Oversmoothing in GNNs”
Lingxiao Zhao and Leman Akoglu · 2020
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“Strategies for Pre-training Graph Neural Networks”
Weihua Hu et al · 2020
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“Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism” PMID: 31408336
Zhaoping Xiong et al · 2020
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“On Layer Normalization in the Transformer Architecture”
Ruibin Xiong et al · 2020
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“Transformers: State-of-the-Art Natural Language Processing”
Thomas Wolf et al · 2020
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“The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation”
Davide Chicco and Giuseppe Jurman · 2020
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“Directional Message Passing for Molecular Graphs”
Johannes Gasteiger, Janek Groß and Stephan Günnemann · 2020
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“A Deep Learning Approach to Antibiotic Discovery”
Jonathan. Stokes et al · 2020
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“Directional Message Passing for Molecular Graphs”
Johannes Gasteiger, Janek Groß and Stephan Günnemann · 2020
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“Principal Neighbourhood Aggregation for Graph Nets”
Gabriele Corso et al · 2020
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“PairNorm: Tackling Oversmoothing in GNNs”
Lingxiao Zhao and Leman Akoglu · 2020
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“Strategies for Pre-training Graph Neural Networks”
Weihua Hu et al · 2020
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“Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism” PMID: 31408336
Zhaoping Xiong et al · 2020
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“On Layer Normalization in the Transformer Architecture”
Ruibin Xiong et al · 2020
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“Transformers: State-of-the-Art Natural Language Processing”
Thomas Wolf et al · 2020
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“The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation”
Davide Chicco and Giuseppe Jurman · 2020
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“Directional Message Passing for Molecular Graphs”
Johannes Gasteiger, Janek Groß and Stephan Günnemann · 2020
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“Accurate prediction of protein structures and interactions using a three-track neural network”
Minkyung Baek et al · 2021
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“CellVGAE: an unsupervised scRNA-seq analysis workflow with graph attention networks”
David Buterez et al · 2021
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“On the Bottleneck of Graph Neural Networks and its Practical Implications”
Uri Alon and Eran Yahav · 2021
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“GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training”
Tianle Cai et al · 2021
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“Rethinking Graph Transformers with Spectral Attention”
Devin Kreuzer et al · 2021
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“Do Transformers Really Perform Badly for Graph Representation?”
Chengxuan Ying et al · 2021
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“Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification” Main Track
Yunsheng Shi et al · 2021
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“A Generalization of Transformer Networks to Graphs”
Vijay Dwivedi and Xavier Bresson · 2021
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“Rethinking Attention with Performers”
Krzysztof Choromanski et al · 2021
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“OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs”
Weihua Hu et al · 2021
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“Open Catalyst 2020 (OC20) Dataset and Community Challenges”
Lowik Chanussot et al · 2021
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“Self-attention Does Not Need O(n 2 {}^{\mbox{2}} ) Memory”, 2021
Markus. Rabe and Charles Staats · 2021
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“Rethinking Graph Transformers with Spectral Attention”
Devin Kreuzer et al · 2021
“One Transformer Can Understand Both 2D & 3D Molecular Data”
Shengjie Luo et al · 2023
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“Generative model based on junction tree variational autoencoder for HOMO value prediction and molecular optimization”
Vladimir Kondratyev, Marian Dryzhakov, Timur Gimadiev and Dmitriy Slutskiy · 2023
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“Benchmarking Graphormer on Large-Scale Molecular Modeling Datasets”, 2023
Yu Shi et al · 2023
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“Accurate GW frontier orbital energies of 134 kilo molecules”
Artem Fediai et al · 2023
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“A critical look at the evaluation of GNNs under heterophily: Are we really making progress?”
Oleg Platonov et al · 2023
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“The Matthews Correlation Coefficient (MCC) is More Informative Than Cohen’s Kappa and Brier Score in Binary Classification Assessment”
Davide Chicco, Matthijs. Warrens and Giuseppe Jurman · 2021
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“The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation”
Davide Chicco, Niklas Tötsch and Giuseppe Jurman · 2021
Cited alongside, same era.
“The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation”
Davide Chicco, Matthijs Warrens and Giuseppe Jurman · 2021
Cited alongside, same era.
“Accurate prediction of protein structures and interactions using a three-track neural network”
Minkyung Baek et al · 2021
Cited alongside, same era.
“CellVGAE: an unsupervised scRNA-seq analysis workflow with graph attention networks”
David Buterez et al · 2021
Cited alongside, same era.
“On the Bottleneck of Graph Neural Networks and its Practical Implications”
Uri Alon and Eran Yahav · 2021
Cited alongside, same era.
Romain Menegaux, Emmanuel Jehanno, Margot Selosse and Julien Mairal · 2023
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“Accurate GW frontier orbital energies of 134 kilo molecules of the QM9 dataset.”, 2023
Artem Fediai et al · 2023
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“Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark”
Jan Tönshoff, Martin Ritzert, Eran Rosenbluth and Martin Grohe · 2023
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“Exphormer: Sparse Transformers for Graphs”
Hamed Shirzad et al · 2023
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“The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification”
Davide Chicco and Giuseppe Jurman · 2023
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“The Misuse of AUC: What High Impact Risk Assessment Gets Wrong”
Kweku Kwegyir-Aggrey et al · 2023
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“ 𝒩 {\mathscr{N}} -WL: A New Hierarchy of Expressivity for Graph Neural Networks”
Qing Wang et al · 2023
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“Benchmarking Graphormer on Large-Scale Molecular Modeling Datasets”, 2023
Yu Shi et al · 2023
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“Discovering small-molecule senolytics with deep neural networks”
Felix Wong et al · 2023
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Amil Merchant et al · 2023
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Remi Lam et al · 2023
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“Physics-inspired machine learning of localized intensive properties”
Ke Chen et al · 2023
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“Understanding convolution on graphs via energies”
Francesco Giovanni et al · 2023
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“Exphormer: Sparse Transformers for Graphs”
Hamed Shirzad et al · 2023
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“Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark”
Jan Tönshoff, Martin Ritzert, Eran Rosenbluth and Martin Grohe · 2023
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“Graph inductive biases in transformers without message passing”
Liheng Ma et al · 2023
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“MF-PCBA: Multifidelity High-Throughput Screening Benchmarks for Drug Discovery and Machine Learning” PMID: 37058588
David Buterez, Jon Janet, Steven. Kiddle and Pietro Liò · 2023
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“One Transformer Can Understand Both 2D & 3D Molecular Data”
Shengjie Luo et al · 2023
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“Generative model based on junction tree variational autoencoder for HOMO value prediction and molecular optimization”
Vladimir Kondratyev, Marian Dryzhakov, Timur Gimadiev and Dmitriy Slutskiy · 2023
Later among the works it cites.
“Benchmarking Graphormer on Large-Scale Molecular Modeling Datasets”, 2023
Yu Shi et al · 2023
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“Accurate GW frontier orbital energies of 134 kilo molecules”
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“Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark”
Jan Tönshoff, Martin Ritzert, Eran Rosenbluth and Martin Grohe · 2023
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“Exphormer: Sparse Transformers for Graphs”
Hamed Shirzad et al · 2023
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“The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification”
Davide Chicco and Giuseppe Jurman · 2023
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“The Misuse of AUC: What High Impact Risk Assessment Gets Wrong”
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“ 𝒩 {\mathscr{N}} -WL: A New Hierarchy of Expressivity for Graph Neural Networks”
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“Benchmarking Graphormer on Large-Scale Molecular Modeling Datasets”, 2023
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