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Graph neural networks (GNNs) have become the standard learning architectures for graphs.
The reduction of a graph to canonical form and the algebra which appears therein
Boris Weisfeiler and Andrei Leman · 1968
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What can be computed locally?
Moni Naor and Larry Stockmeyer · 1995
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Laplacian eigenmaps for dimensionality reduction and data representation
Mikhail Belkin and Partha Niyogi · 2003
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Spectral grouping using the nystrom method
Charless Fowlkes, Serge Belongie, Fan Chung, and Jitendra Malik · 2004
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Automatic multimedia cross-modal correlation discovery
Jia-Yu Pan, Hyung-Jeong Yang, Christos Faloutsos, and Pinar Duygulu · 2004
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Deepergcn: All you need to train deeper gcns
Guohao Li, Chenxin Xiong, Ali Thabet, and Bernard Ghanem · 2006
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Zinc: a free tool to discover chemistry for biology
John J Irwin, Teague Sterling, Michael M Mysinger, Erin S Bolstad, and Ryan G Coleman · 2012
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Pubchem’s bioassay database
Yanli Wang, Jewen Xiao, Tugba O Suzek, Jian Zhang, Jiyao Wang, Zhigang Zhou, Lianyi Han, Karen Karapetyan, Svetlana Dracheva, Benjamin A Shoemaker, et al · 2012
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A splitting method for orthogonality constrained problems
Rongjie Lai and Stanley Osher · 2014
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Tox21 challenge
Tox21 · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst · 2016
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Learning multiagent communication with backpropagation
Sainbayar Sukhbaatar, Rob Fergus, et al · 2016
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Xavier Bresson and Thomas Laurent · 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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Inductive representation learning on large graphs
William L Hamilton, Rex Ying, and Jure Leskovec · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
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Geometric deep learning on graphs and manifolds using mixture model cnns
Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodola, Jan Svoboda, and Michael M Bronstein · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Junction tree variational autoencoder for molecular graph generation
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2018
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Invariant and equivariant graph networks
Haggai Maron, Heli Ben-Hamu, Nadav Shamir, and Yaron Lipman · 2018
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Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
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Moleculenet: a benchmark for molecular machine learning. chem sci 9: 513–530, 2018
Z Wu, B Ramsundar, EN Feinberg, J Gomes, C Geniesse, AS Pappu, K Leswing, and V Pande · 2018
Cited alongside, same era.
On the equivalence between graph isomorphism testing and function approximation with gnns
Zhengdao Chen, Lei Chen, Soledad Villar, and Joan Bruna · 2019
Cited alongside, same era.
Learning symbolic physics with graph networks
Miles D Cranmer, Rui Xu, Peter Battaglia, and Shirley Ho · 2019
Cited alongside, same era.
Open graph benchmark: Datasets for machine learning on graphs
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec · 2020
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How much position information do convolutional neural networks encode?
Md Amirul Islam, Sen Jia, and Neil D. B. Bruce · 2020
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Transformers are graph neural networks
Chaitanya Joshi · 2020
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Memory-based graph networks
Amir Hosein Khasahmadi, Kaveh Hassani, Parsa Moradi, Leo Lee, and Quaid Morris · 2020
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What graph neural networks cannot learn: depth vs width
Andreas Loukas · 2020
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Tudataset: A collection of benchmark datasets for learning with graphs
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Deepgcns: Can gcns go as deep as cnns?
Guohao Li, Matthias Muller, Ali Thabet, and Bernard Ghanem · 2019
Cited alongside, same era.
Provably powerful graph networks
Haggai Maron, Heli Ben-Hamu, Hadar Serviansky, and Yaron Lipman · 2019
Cited alongside, same era.
Fake news detection on social media using geometric deep learning
Federico Monti, Fabrizio Frasca, Davide Eynard, Damon Mannion, and Michael M Bronstein · 2019
Cited alongside, same era.
Weisfeiler and leman go neural: Higher-order graph neural networks
Christopher Morris, Martin Ritzert, Matthias Fey, William L Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe · 2019
Cited alongside, same era.
Relational pooling for graph representations
Ryan Murphy, Balasubramaniam Srinivasan, Vinayak Rao, and Bruno Ribeiro · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
Cited alongside, same era.
Approximation ratios of graph neural networks for combinatorial problems
Ryoma Sato, Makoto Yamada, and Hisashi Kashima · 2019
Cited alongside, same era.
Christopher Morris, Nils M Kriege, Franka Bause, Kristian Kersting, Petra Mutzel, and Marion Neumann · 2020
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Pinnersage: Multi-modal user embedding framework for recommendations at pinterest
Aditya Pal, Chantat Eksombatchai, Yitong Zhou, Bo Zhao, Charles Rosenberg, and Jure Leskovec · 2020
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A survey on the expressive power of graph neural networks
Ryoma Sato · 2020
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A deep learning approach to antibiotic discovery
Jonathan M Stokes, Kevin Yang, Kyle Swanson, Wengong Jin, Andres Cubillos-Ruiz, Nina M Donghia, Craig R MacNair, Shawn French, Lindsey A Carfrae, Zohar Bloom-Ackermann, et al · 2020
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Graph neural networks: A review of methods and applications
Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun · 2020
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Directional graph networks
Dominique Beani, Saro Passaro, Vincent Létourneau, Will Hamilton, Gabriele Corso, and Pietro Liò · 2021
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Machine learning for combinatorial optimization: a methodological tour d’horizon
Yoshua Bengio, Andrea Lodi, and Antoine Prouvost · 2021
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Weisfeiler and lehman go cellular: Cw networks
Cristian Bodnar, Fabrizio Frasca, Nina Otter, Yu Guang Wang, Pietro Liò, Guido Montúfar, and Michael Bronstein · 2021
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Combinatorial optimization and reasoning with graph neural networks
Quentin Cappart, Didier Chételat, Elias Khalil, Andrea Lodi, Christopher Morris, and Petar Veličković · 2021
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On positional and structural node features for graph neural networks on non-attributed graphs
Hejie Cui, Zijie Lu, Pan Li, and Carl Yang · 2021
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Traffic Prediction with Graph Neural Networks in Google Maps
Austin Derrow-Pinion, Jennifer She, David Wong, Oliver Lange, Todd Hester, Luis Perez, Marc Nunkesser, Seongjae Lee, Xueying Guo, Peter W Battaglia, Vishal Gupta, Ang Li, Zhongwen Xu, Alvaro Sanchez-Gonzalez, Yujia Li, and Petar Veličković · 2021
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Position information in transformers: An overview
Philipp Dufter, Martin Schmitt, and Hinrich Schütze · 2021
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A generalization of transformer networks to graphs
Vijay Prakash Dwivedi and Xavier Bresson · 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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Parameterized hypercomplex graph neural networks for graph classification
Tuan Le, Marco Bertolini, Frank Noé, and Djork-Arné Clevert · 2021
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Graphit: Encoding graph structure in transformers
Grégoire Mialon, Dexiong Chen, Margot Selosse, and Julien Mairal · 2021
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Do transformers really perform bad for graph representation?
Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, and Tie-Yan Liu · 2021
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