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Graphs are the natural data structure to represent relational and structural information in many domains.
Multi-graph transformer for free-hand sketch recognition
Peng Xu, Chaitanya K Joshi, and Xavier Bresson · 1912
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A new model for learning in graph domains
Marco Gori, Gabriele Monfardini, and Franco Scarselli · 2005
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The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2008
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Enumeration of 166 billion organic small molecules in the chemical universe database gdb-17
Lars Ruddigkeit, Ruud van Deursen, Lorenz C. Blum, and Jean-Louis Reymond · 2012
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Patent reaction extraction: downloads; https://bitbucket.org/dan2097/patent-reaction-extraction/downloads
D. M. Lowe · 2014
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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
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Convolutional networks on graphs for learning molecular fingerprints
David K Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P Adams · 2015
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dandelion Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2015
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Gated graph sequence neural networks
Yujia Li, Richard Zemel, Marc Brockschmidt, and Daniel Tarlow · 2016
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Interaction networks for learning about objects, relations and physics
Peter Battaglia, Razvan Pascanu, Matthew Lai, Danilo Jimenez Rezende, et al · 2016
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Variational graph auto-encoders
Thomas N Kipf and Max Welling · 2016
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Knowledge transfer for out-of-knowledge-base entities: A graph neural network approach
Takuo Hamaguchi, Hidekazu Oiwa, Masashi Shimbo, and Yuji Matsumoto · 2017
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Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, and I. Polosukhin · 2017
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
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Predicting organic reaction outcomes with weisfeiler-lehman network
W. Jin, C. Coley, R. Barzilay, and T. Jaakkola · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2017
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Graph convolutional matrix completion
Rianne van den Berg, Thomas N Kipf, and Max Welling · 2017
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Protein interface prediction using graph convolutional networks
Alex Fout, Jonathon Byrd, Basir Shariat, and Asa Ben-Hur · 2017
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Graph neural networks: A review of methods and applications
Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, and Maosong Sun · 2018
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Machine learning for molecular and materials science
Keith T. Butler, Daniel W. Davies, Hugh Cartwright, Olexandr Isayev, and Aron Walsh · 2018
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Feature-wise transformations
V. Dumoulin, E. Perez, N. Schucher, F. Strub, H.d. Vries, A. Courville, and Y. Bengio · 2018
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Moleculenet: a benchmark for molecular machine learning
Zhenqin Wu, Bharath Ramsundar, Evan N. Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S. Pappu, Karl Leswing, and Vijay Pande · 2018
Cited alongside, same era.
Found in translation: Predicting outcomes of complex organic chemistry reactions using neural sequence-to-sequence models
P. Schwaller, T. Gaudin, D. Lanyi, C. Bekas, and T. Laino · 2018
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Circuit-GNN: Graph neural networks for distributed circuit design
Guo Zhang, Hao He, and Dina Katabi · 2019
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Graph neural networks for social recommendation
Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, and Dawei Yin · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Graph transformation policy network for chemical reaction prediction
K. Do, T. Tran, and S. Venkatesh · 2019
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A graph-convolutional neural network model for the prediction of chemical reactivity
C.W. Coley, W. Jin, L. Rogers, T.F. Jamison, T.S. Jaakkola, W.H. Green, R. Barzilay, and K.F. Jensen · 2019
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Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction
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Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2018
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Diffusion convolutional recurrent neural network: Data-driven traffic forecasting
Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu · 2018
Cited alongside, same era.
Graph convolutional neural networks for web-scale recommender systems
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec · 2018
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Hybrid approach of relation network and localized graph convolutional filtering for breast cancer subtype classification
Sungmin Rhee, Seokjun Seo, and Sun Kim · 2018
Cited alongside, same era.
Modeling polypharmacy side effects with graph convolutional networks
Marinka Zitnik, Monica Agrawal, and Jure Leskovec · 2018
Cited alongside, same era.
Cross-lingual knowledge graph alignment via graph convolutional networks
Zhichun Wang, Qingsong Lv, Xiaohan Lan, and Yu Zhang · 2018
Cited alongside, same era.
Iterative visual reasoning beyond convolutions
Xinlei Chen, Li-Jia Li, Li Fei-Fei, and Abhinav Gupta · 2018
Cited alongside, same era.
P. Schwaller, T. Laino, T. Gaudin, P. Bolgar, C.A. Hunter, C. Bekas, and A.A. Lee · 2019
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Text generation from knowledge graphs with graph transformers
Yi Luan Mirella Lapata Rik Koncel-Kedziorski, Dhanush Bekal and Hannaneh Hajishirzi · 2019
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Graph transformer networks
Seongjun Yun, Minbyul Jeong, Raehyun Kim, Jaewoo Kang, and Hyunwoo J Kim · 2019
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Traffic graph convolutional recurrent neural network: A deep learning framework for network-scale traffic learning and forecasting
Zhiyong Cui, Kristian Henrickson, Ruimin Ke, and Yinhai Wang · 2019
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Dynamic graph cnn for learning on point clouds
Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E Sarma, Michael M Bronstein, and Justin M Solomon · 2019
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Efficient graph generation with graph recurrent attention networks
Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, Will Hamilton, David K Duvenaud, Raquel Urtasun, and Richard Zemel · 2019
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Hierarchical graph-to-graph translation for molecules
W Jin, R Barzilay, and T Jaakkola · 2019
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Graph-to-graph transformer for transition-based dependency parsing
Alireza Mohammadshahi and James Henderson · 2019
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Molecule attention transformer
Łukasz Maziarka, Tomasz Danel, Sławomir Mucha, Krzysztof Rataj, Jacek Tabor, and Stanisław Jastrzębski · 2020
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Strategies for pre-training graph neural networks
Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay S. Pande, and Jure Leskovec · 2020
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Directional message passing for molecular graphs
Johannes Klicpera, Janek Groß, and Stephan Günnemann · 2020
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Learning the graphical structure of electronic health records with graph convolutional transformer
Edward Choi, Zhen Xu, Yujia Li, Michael W Dusenberry, Gerardo Flores, Emily Xue, and Andrew M Dai · 2020
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Amr-to-text generation with graph transformer
Tianming Wang, Xiaojun Wan, and Hanqi Jin · 2020
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