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Graph Neural Networks (GNNs) are popular for graph machine learning and have shown great results on wide node classification tasks.
Mincut pooling in graph neural networks
Filippo Maria Bianchi, Daniele Grattarola, and Cesare Alippi · 1907
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Kernel k-means: Spectral clustering and normalized cuts
Inderjit S. Dhillon, Yuqiang Guan, and Brian Kulis · 2004
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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 · 2005
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Paul Graham · 2012
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Spectral networks and locally connected networks on graphs, 2014
Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun · 2014
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Deep learning with limited numerical precision
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Learning both weights and connections for efficient neural networks
Song Han, Jeff Pool, John Tran, and William J. Dally · 2015
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Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Eyeriss: A spatial architecture for energy-efficient dataflow for convolutional neural networks
Yu-Hsin Chen, Joel Emer, and Vivienne Sze · 2016
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Proteus: Exploiting numerical precision variability in deep neural networks
Patrick Judd, Jorge Albericio, Tayler Hetherington, Tor M. Aamodt, Natalie Enright Jerger, and Andreas Moshovos · 2016
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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, 2017
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst · 2017
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Attention is all you need, 2017
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
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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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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2018
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Predict then propagate: Graph neural networks meet personalized pagerank, 2019
Johannes Klicpera, Aleksandar Bojchevski, and Stephan Günnemann · 2019
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Deepgcns: Can gcns go as deep as cnns?
Guohao Li, Matthias Muller, Ali Thabet, and Bernard Ghanem · 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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Learned low precision graph neural networks, 2020
Yiren Zhao, Duo Wang, Daniel Bates, Robert Mullins, Mateja Jamnik, and Pietro Lio · 2020
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On graph neural networks versus graph-augmented {mlp}s
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Graph-free knowledge distillation for graph neural networks, 2021
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Combining label propagation and simple models out-performs graph neural networks
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Simplifying graph convolutional networks, 2019
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Layer-dependent importance sampling for training deep and large graph convolutional networks
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Simple and deep graph convolutional networks
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Sign: Scalable inception graph neural networks, 2020
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Redundancy-free computation for graph neural networks
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Joint embedding of structure and features via graph convolutional networks
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