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It is hard to directly implement Graph Neural Networks (GNNs) on large scaled graphs.
Learning from labeled and unlabeled data with label propagation
Zhu Xiaojin and Ghahramani Zoubin · 2002
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Label propagation through linear neighborhoods
Fei Wang and Changshui Zhang · 2007
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Algorithms for hyper-parameter optimization
James Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl · 2011
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Spectral networks and locally connected networks on graphs
Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun · 2013
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Translating embeddings for modeling multi-relational data
Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko · 2013
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Efficient label propagation
Yasuhiro Fujiwara and Go Irie · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Convolutional networks on graphs for learning molecular fingerprints
David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael Gómez-Bombarelli, Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P Adams · 2015
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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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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node2vec: Scalable feature learning for networks
Aditya Grover and Jure Leskovec · 2016
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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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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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Stochastic training of graph convolutional networks with variance reduction
Jianfei Chen, Jun Zhu, and Le Song · 2017
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
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Predicting multicellular function through multi-layer tissue networks
Marinka Zitnik and Jure Leskovec · 2017
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Modeling relational data with graph convolutional networks, 2017
Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling · 2017
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metapath2vec: Scalable representation learning for heterogeneous networks
Yuxiao Dong, Nitesh V Chawla, and Ananthram Swami · 2017
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Link prediction based on graph neural networks
Muhan Zhang and Yixin Chen · 2018
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Learning human-object interactions by graph parsing neural networks
Siyuan Qi, Wenguan Wang, Baoxiong Jia, Jianbing Shen, and Song-Chun Zhu · 2018
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Graph neural networks for icecube signal classification
Nicholas Choma, Federico Monti, Lisa Gerhardt, Tomasz Palczewski, Zahra Ronaghi, Prabhat Prabhat, Wahid Bhimji, Michael M Bronstein, Spencer R Klein, and Joan Bruna · 2018
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Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks
Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, and Cho-Jui Hsieh · 2019
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Graphsaint: Graph sampling based inductive learning method
Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor Prasanna · 2019
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Simplifying graph convolutional networks
Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger · 2019
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Transferability of spectral graph convolutional neural networks
Ron Levie, Wei Huang, Lorenzo Bucci, Michael M Bronstein, and Gitta Kutyniok · 2019
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Diffusion improves graph learning
Johannes Klicpera, Stefan Weißenberger, and Stephan Günnemann · 2019
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Disease prediction using graph convolutional networks: application to autism spectrum disorder and alzheimer’s disease
Sarah Parisot, Sofia Ira Ktena, Enzo Ferrante, Matthew Lee, Ricardo Guerrero, Ben Glocker, and Daniel Rueckert · 2018
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Modeling polypharmacy side effects with graph convolutional networks
Marinka Zitnik, Monica Agrawal, and Jure Leskovec · 2018
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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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Adaptive sampling towards fast graph representation learning
Wenbing Huang, Tong Zhang, Yu Rong, and Junzhou Huang · 2018
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Fastgcn: fast learning with graph convolutional networks via importance sampling
Jie Chen, Tengfei Ma, and Cao Xiao · 2018
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Deeper insights into graph convolutional networks for semi-supervised learning, 2018
Qimai Li, Zhichao Han, and Xiao-Ming Wu · 2018
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Cayleynets: Graph convolutional neural networks with complex rational spectral filters
Ron Levie, Federico Monti, Xavier Bresson, and Michael M Bronstein · 2018
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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
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Deep graph library: A graph-centric, highly-performant package for graph neural networks
Minjie Wang, Da Zheng, Zihao Ye, Quan Gan, Mufei Li, Xiang Song, Jinjing Zhou, Chao Ma, Lingfan Yu, Yu Gai, Tianjun Xiao, Tong He, George Karypis, Jinyang Li, and Zheng Zhang · 2019
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Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning
Pablo Gainza, Freyr Sverrisson, Frederico Monti, Emanuele Rodola, D Boscaini, MM Bronstein, and BE Correia · 2020
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Sign: Scalable inception graph neural networks
Emanuele Rossi, Fabrizio Frasca, Ben Chamberlain, Davide Eynard, Michael Bronstein, and Federico Monti · 2020
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Combining label propagation and simple models out-performs graph neural networks
Qian Huang, Horace He, Abhay Singh, Ser-Nam Lim, and Austin R Benson · 2020
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Multi-stage self-supervised learning for graph convolutional networks on graphs with few labels, 2020
Ke Sun, Zhouchen Lin, and Zhanxing Zhu · 2020
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Self-enhanced gnn: Improving graph neural networks using model outputs
Han Yang, Xiao Yan, Xinyan Dai, and James Cheng · 2020
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Unifying graph convolutional neural networks and label propagation
Hongwei Wang and Jure Leskovec · 2020
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Masked label prediction: Unified massage passing model for semi-supervised classification
Yunsheng Shi, Zhengjie Huang, Shikun Feng, and Yu Sun · 2020
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Adaptive graph diffusion networks with hop-wise attention
Chuxiong Sun and Guoshi Wu · 2020
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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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Scalable graph neural networks for heterogeneous graphs
Lingfan Yu, Jiajun Shen, Jinyang Li, and Adam Lerer · 2020
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Bag of tricks of semi-supervised classification with graph neural networks, 2021
Yangkun Wang · 2021
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