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The design of deep graph models still remains to be investigated and the crucial part is how to explore and exploit the knowledge from different hops of neighbors in an efficient way.
A short introduction to boosting
Yoav Freund, Robert Schapire, and Naoki Abe · 1999
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Automating the construction of internet portals with machine learning
Andrew Kachites McCallum, Kamal Nigam, Jason Rennie, and Kristie Seymore · 2000
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On the bayes-risk consistency of boosting methods
Gábor Lugosi and Nicolas Vayatis · 2001
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Boosting with the l 2 loss: regression and classification
Peter Bühlmann and Bin Yu · 2003
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Greedy algorithms for classification–consistency, convergence rates, and adaptivity
Shie Mannor, Ron Meir, and Tong Zhang · 2003
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Process consistency for adaboost
Wenxin Jiang et al · 2004
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A new model for learning in graph domains
Marco Gori, Gabriele Monfardini, and Franco Scarselli · 2005
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Boosting with early stopping: Convergence and consistency
Tong Zhang, Bin Yu, et al · 2005
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Collective classification in network data
Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad · 2008
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Multi-class adaboost
Trevor Hastie, Saharon Rosset, Ji Zhu, and Hui Zou · 2009
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Community detection in graphs
Santo Fortunato · 2010
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Convergence and consistency of regularized boosting with weakly dependent observations
Aurelie C Lozano, Sanjeev R Kulkarni, and Robert E Schapire · 2013
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Spectral networks and locally connected networks on graphs
Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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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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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Inductive representation learning on large graphs
Will Hamilton, Zhitao 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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Adagan: Boosting generative models
Ilya O Tolstikhin, Sylvain Gelly, Olivier Bousquet, Carl-Johann Simon-Gabriel, and Bernhard Schölkopf · 2017
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Max-margin nonparametric latent feature models for link prediction
Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2018
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Representation learning on graphs with jumping knowledge networks
Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, and Stefanie Jegelka · 2018
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Mixhop: Higher-order graph convolution architectures via sparsified neighborhood mixing
Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Hrayr Harutyunyan, Nazanin Alipourfard, Kristina Lerman, Greg Ver Steeg, and Aram Galstyan · 2019
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Power up! robust graph convolutional network against evasion attacks based on graph powering
Ming Jin, Heng Chang, Wenwu Zhu, and Somayeh Sojoudi · 2019
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Can gcns go as deep as cnns?
Guohao Li, Matthias Müller, Ali Thabet, and Bernard Ghanem · 2019
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Jun Zhu, Jiaming Song, and Bei Chen · 2017
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Greedy layerwise learning can scale to imagenet
Eugene Belilovsky, Michael Eickenberg, and Edouard Oyallon · 2018
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Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking
Aleksandar Bojchevski and Stephan Günnemann · 2018
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Bootstrapped graph diffusions: Exposing the power of nonlinearity
Eliav Buchnik and Edith Cohen · 2018
Cited alongside, same era.
Fastgcn: fast learning with graph convolutional networks via importance sampling
Jie Chen, Tengfei Ma, and Cao Xiao · 2018
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Learning deep resnet blocks sequentially using boosting theory
Furong Huang, Jordan Ash, John Langford, and Robert Schapire · 2018
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Predict then propagate: Graph neural networks meet personalized pagerank
Johannes Klicpera, Aleksandar Bojchevski, and Stephan Günnemann · 2018
Cited alongside, same era.
Renjie Liao, Zhizhen Zhao, Raquel Urtasun, and Richard S Zemel · 2019
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Break the ceiling: Stronger multi-scale deep graph convolutional networks
Sitao Luan, Mingde Zhao, Xiao-Wen Chang, and Doina Precup · 2019
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Multi-stage self-supervised learning for graph convolutional networks
Ke Sun, Zhanxing Zhu, and Zhouchen Lin · 2019
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Simplifying graph convolutional networks
Felix Wu, Tianyi Zhang, Amauri Holanda de Souza Jr, Christopher Fifty, Tao Yu, and Kilian Q Weinberger · 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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Towards deeper graph neural networks
Meng Liu, Hongyang Gao, and Shuiwang Ji · 2020
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Optimization and generalization analysis of transduction through gradient boosting and application to multi-scale graph neural networks
Kenta Oono and Taiji Suzuki · 2020
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From graph low-rank global attention to 2-fwl approximation
Omri Puny, Heli Ben-Hamu, and Yaron Lipman · 2020
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