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Graph neural networks (GNNs) integrate deep architectures and topological structure modeling in an effective way.
On the evolution of random graphs
Paul Erdős and Alfréd Rényi · 1960
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean · 2013
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Gated graph sequence neural networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel · 2015
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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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A simple way to initialize recurrent networks of rectified linear units
Quoc V Le, Navdeep Jaitly, and Geoffrey E Hinton · 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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Revisiting semi-supervised learning with graph embeddings
Zhilin Yang, William W Cohen, and Ruslan Salakhutdinov · 2016
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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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Predicting multicellular function through multi-layer tissue networks
Marinka Zitnik and Jure Leskovec · 2017
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Geometric matrix completion with recurrent multi-graph neural networks
Federico Monti, Michael M Bronstein, and Xavier Bresson · 2017
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Graph attention networks
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2017
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On the effects of batch and weight normalization in generative adversarial networks
Sitao Xiang and Hao Li · 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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Large-scale learnable graph convolutional networks
Hongyang Gao, Zhengyang Wang, and Shuiwang Ji · 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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Deeper insights into graph convolutional networks for semi-supervised learning
Qimai Li, Zhichao Han, and Xiao-Ming Wu · 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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Link prediction based on graph neural networks
Muhan Zhang and Yixin Chen · 2018
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Predict then propagate: Graph neural networks meet personalized pagerank
Johannes Klicpera, Aleksandar Bojchevski, and Stephan Günnemann · 2018
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Graph neural networks exponentially lose expressive power for node classification
Kenta Oono and Taiji Suzuki · 2020
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On the bottleneck of graph neural networks and its practical implications
Uri Alon and Eran Yahav · 2020
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Tackling over-smoothing for general graph convolutional networks
Wenbing Huang, Yu Rong, Tingyang Xu, Fuchun Sun, and Junzhou Huang · 2020
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Towards deeper graph neural networks with differentiable group normalization
Kaixiong Zhou, Xiao Huang, Yuening Li, Daochen Zha, Rui Chen, and Xia Hu · 2020
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Simple and deep graph convolutional networks
Ming Chen, Zhewei Wei, Zengfeng Huang, Bolin Ding, and Yaliang Li · 2020
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Pitfalls of graph neural network evaluation
Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, and Stephan Günnemann · 2018
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Graph recurrent networks with attributed random walks
Xiao Huang, Qingquan Song, Yuening Li, and Xia Hu · 2019
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Graph neural networks for social recommendation
Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, and Dawei Yin · 2019
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Graph u-nets
Hongyang Gao and Shuiwang Ji · 2019
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Auto-gnn: Neural architecture search of graph neural networks
Kaixiong Zhou, Qingquan Song, Xiao Huang, and Xia Hu · 2019
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Revisiting graph neural networks: All we have is low-pass filters
Hoang NT and Takanori Maehara · 2019
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Deli Chen, Yankai Lin, Wei Li, Peng Li, Jie Zhou, and Xu Sun · 2019
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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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Graph random neural networks for semi-supervised learning on graphs
Wenzheng Feng, Jie Zhang, Yuxiao Dong, Yu Han, Huanbo Luan, Qian Xu, Qiang Yang, Evgeny Kharlamov, and Jie Tang · 2020
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Bayesian graph neural networks with adaptive connection sampling
Arman Hasanzadeh, Ehsan Hajiramezanali, Shahin Boluki, Mingyuan Zhou, Nick Duffield, Krishna Narayanan, and Xiaoning Qian · 2020
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A note on over-smoothing for graph neural networks
Chen Cai and Yusu Wang · 2020
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Deepergcn: All you need to train deeper gcns
Guohao Li, Chenxin Xiong, Ali Thabet, and Bernard Ghanem · 2020
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Deep isometric learning for visual recognition
Haozhi Qi, Chong You, Xiaolong Wang, Yi Ma, and Jitendra Malik · 2020
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Revisiting graph based collaborative filtering: A linear residual graph convolutional network approach
Lei Chen, Le Wu, Richang Hong, Kun Zhang, and Meng Wang · 2020
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Understanding and resolving performance degradation in graph convolutional networks, 2020
Kuangqi Zhou, Yanfei Dong, Kaixin Wang, Wee Sun Lee, Bryan Hooi, Huan Xu, and Jiashi Feng · 2020
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Towards deeper graph neural networks
Meng Liu, Hongyang Gao, and Shuiwang Ji · 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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Adaptive universal generalized pagerank graph neural network
Eli Chien, Jianhao Peng, Pan Li, and Olgica Milenkovic · 2021
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