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Residual connections and normalization layers have become standard design choices for graph neural networks (GNNs), and were proposed as solutions to the mitigate the oversmoothing problem in GNNs.
The reduction of a graph to canonical form and the algebra which appears therein
Boris Weisfeiler and Andrei Leman · 1968
Earlier work this paper cites.
Efficient backprop
Yann LeCun, Léon Bottou, Genevieve B Orr, and Klaus-Robert Müller · 2002
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A new model for learning in graph domains
M. Gori, G. Monfardini, and F. Scarselli · 2005
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The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2009
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Decentralised minimum-time consensus
Ye Yuan, G-B Stan, Ling Shi, Mauricio Barahona, and Jorge Goncalves · 2013
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Spectral networks and locally connected networks on graphs
Joan Bruna, Wojciech Zaremba, Arthur D. Szlam, and Yann LeCun · 2014
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Convolutional networks on graphs for learning molecular fingerprints
David Kristjanson Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael Gómez-Bombarelli, Timothy D. Hirzel, Alán Aspuru-Guzik, and Ryan P. Adams · 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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Jimmy Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton · 2016
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Interaction networks for learning about objects, relations and physics
Peter Battaglia, Razvan Pascanu, Matthew Lai, Danilo Jimenez Rezende, and koray kavukcuoglu · 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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Deep residual learning for image recognition
Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun · 2016
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Graph partitions and cluster synchronization in networks of oscillators
Michael T Schaub, Neave O’Clery, Yazan N Billeh, Jean-Charles Delvenne, Renaud Lambiotte, and Mauricio Barahona · 2016
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Instance normalization: The missing ingredient for fast stylization
Dmitry Ulyanov, Andrea Vedaldi, and Victor S. Lempitsky · 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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Inductive representation learning on large graphs
William L. Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Identity matters in deep learning
Moritz Hardt and Tengyu Ma · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2017
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Modeling relational data with graph convolutional networks
M. Schlichtkrull, Thomas Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling · 2017
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Theoretical analysis of auto rate-tuning by batch normalization
Sanjeev Arora, Zhiyuan Li, and Kaifeng Lyu · 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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Unsupervised inductive graph-level representation learning via graph-graph proximity
Yunsheng Bai, Haoyang Ding, Yang Qiao, Agustin Marinovic, Ken Gu, Tingting Chen, Yizhou Sun, and Wei Wang · 2019
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A fair comparison of graph neural networks for graph classification
Federico Errica, Marco Podda, Davide Bacciu, and Alessio Micheli · 2019
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Geom-gcn: Geometric graph convolutional networks
Hongbin Pei, Bingzhen Wei, Kevin Chen-Chuan Chang, Yu Lei, and Bo Yang · 2020
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Exploiting symmetry in network analysis
Rubén J Sánchez-García · 2020
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Graph neural networks in recommender systems: A survey
Shiwen Wu, Wentao Zhang, Fei Sun, and Bin Cui · 2020
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Pairnorm: Tackling oversmoothing in gnns
Lingxiao Zhao and Leman Akoglu · 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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Graphnorm: A principled approach to accelerating graph neural network training
Tianle Cai, Shengjie Luo, Keyulu Xu, Di He, Tie-yan Liu, and Liwei Wang · 2021
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Matthias Fey and Jan E. Lenssen · 2019
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Predict then propagate: Graph neural networks meet personalized pagerank
Johannes Klicpera, Aleksandar Bojchevski, and Stephan Günnemann · 2019
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Towards understanding the importance of shortcut connections in residual networks
Tianyi Liu, Minshuo Chen, Mo Zhou, Simon Shaolei Du, Enlu Zhou, and Tuo Zhao · 2019
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Weisfeiler and leman go neural: Higher-order graph neural networks
Christopher Morris, Martin Ritzert, Matthias Fey, William L Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe · 2019
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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A mean field theory of batch normalization
Greg Yang, Jeffrey Pennington, Vinay Rao, Jascha Narain Sohl-Dickstein, and Samuel S. Schoenholz · 2019
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A note on over-smoothing for graph neural networks
Chen Cai and Yusu Wang · 2020
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Combinatorial optimization and reasoning with graph neural networks
Quentin Cappart, Didier Chételat, Elias Boutros Khalil, Andrea Lodi, Christopher Morris, and Petar Velickovic · 2021
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Batch normalization orthogonalizes representations in deep random networks
Hadi Daneshmand, Amir Joudaki, and Francis R. Bach · 2021
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Attention is not all you need: Pure attention loses rank doubly exponentially with depth
Yihe Dong, Jean-Baptiste Cordonnier, and Andreas Loukas · 2021
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Graph neural networks with learnable structural and positional representations
Vijay Prakash Dwivedi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, and Xavier Bresson · 2021
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Understanding and resolving performance degradation in deep graph convolutional networks
Kuangqi Zhou, Yanfei Dong, Kaixin Wang, Wee Sun Lee, Bryan Hooi, Huan Xu, and Jiashi Feng · 2021
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From local to global: Spectral-inspired graph neural networks
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Prediction of protein–protein interaction using graph neural networks
Kanchan Jha, Sriparna Saha, and Hiteshi Singh · 2022
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Not too little, not too much: a theoretical analysis of graph (over)smoothing
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Benchmarking graph neural networks
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Contranorm: A contrastive learning perspective on oversmoothing and beyond
Xiaojun Guo, Yifei Wang, Tianqi Du, and Yisen Wang · 2023
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A survey on oversmoothing in graph neural networks
T.Konstantin Rusch, Michael M. Bronstein, and Siddhartha Mishra · 2023
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Neighborhood structure configuration models
Felix I. Stamm, Michael Scholkemper, Markus Strohmaier, and Michael T. Schaub · 2023
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