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Popular graph neural networks are shallow models, despite the success of very deep architectures in other application domains of deep learning.
Nonnegative matrices in the mathematical sciences
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Social network analysis: a powerful strategy, also for the information sciences
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On graph kernels: Hardness results and efficient alternatives
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A new model for learning in graph domains
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Using graph theory to analyze biological networks
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Wavelets on graphs via spectral graph theory
David K Hammond, Pierre Vandergheynst, and Rémi Gribonval · 2011
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Weisfeiler-lehman graph kernels
Nino Shervashidze, Pascal Schweitzer, Erik Jan Van Leeuwen, Kurt Mehlhorn, and Karsten M Borgwardt · 2011
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Training recurrent neural networks
Ilya Sutskever · 2013
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Defining and evaluating network communities based on ground-truth
Jaewon Yang and Jure Leskovec · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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node2vec: Scalable feature learning for networks
Aditya Grover and Jure Leskovec · 2016
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Tianqi Chen, Bing Xu, Chiyuan Zhang, and Carlos Guestrin · 2016
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Attention is all you need
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Inductive representation learning on large graphs
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Neural message passing for quantum chemistry
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Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, and Cho-Jui Hsieh · 2019
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Deep equilibrium models
Shaojie Bai, J Zico Kolter, and Vladlen Koltun · 2019
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Diffusion improves graph learning
Johannes Klicpera, Stefan Weißenberger, and Stephan Günnemann · 2019
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GraphSAINT: Graph sampling based inductive learning method
Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor Prasanna · 2020
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Dropedge: Towards deep graph convolutional networks on node classification
Yu Rong, Wenbing Huang, Tingyang Xu, and Junzhou Huang · 2020
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Implicit graph neural networks
Fangda Gu, Heng Chang, Wenwu Zhu, Somayeh Sojoudi, and Laurent El Ghaoui · 2020
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struc2vec: Learning node representations from structural identity
Leonardo FR Ribeiro, Pedro HP Saverese, and Daniel R Figueiredo · 2017
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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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Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 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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Predict then propagate: Graph neural networks meet personalized pagerank
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Watch your step: Learning node embeddings via graph attention
Sami Abu-El-Haija, Bryan Perozzi, Rami Al-Rfou, and Alexander A Alemi · 2018
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A comprehensive survey on graph neural networks
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Beyond homophily in graph neural networks: Current limitations and effective designs
Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, and Danai Koutra · 2020
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Simple and deep graph convolutional networks
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Pairnorm: Tackling oversmoothing in gnns
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Graph neural networks exponentially lose expressive power for node classification
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Fast and deep graph neural networks
Claudio Gallicchio and Alessio Micheli · 2020
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Eta prediction with graph neural networks in google maps
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Implicit deep learning
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