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Graph Neural Networks (GNNs) typically operate by message-passing, where the state of a node is updated based on the information received from its neighbours.
Harmonic mappings of riemannian manifolds
James Eells and Joseph H Sampson · 1964
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Scale-space and edge detection using anisotropic diffusion
Pietro Perona and Jitendra Malik · 1990
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Encoding labeled graphs by labeling raam
Alessandro Sperduti · 1993
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Learning task-dependent distributed representations by backpropagation through structure
Christoph Goller and Andreas Kuchler · 1996
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Behavioral analysis of anisotropic diffusion in image processing
Yu-Li You, Wenyuan Xu, Allen Tannenbaum, and Mostafa Kaveh · 1996
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Spectral graph theory
Fan RK Chung and Fan Chung Graham · 1997
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From high energy physics to low level vision
Ron Kimmel, Nir Sochen, and Ravi Malladi · 1997
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A new model for learning in graph domains
Marco Gori, Gabriele Monfardini, and Franco Scarselli · 2005
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Regularization on discrete spaces
Dengyong Zhou and Bernhard Schölkopf · 2005
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The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2008
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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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Spectral networks and locally connected networks on graphs
Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun · 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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Xavier Bresson and Thomas Laurent · 2017
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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
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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Neural ordinary differential equations
Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud · 2018
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Stable architectures for deep neural networks
E. Haber and L. Ruthotto · 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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Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
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Fast graph representation learning with PyTorch Geometric
Matthias Fey and Jan E. Lenssen · 2019
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The spectral graph wavelet transform: Fundamental theory and fast computation
David K Hammond, Pierre Vandergheynst, and Rémi Gribonval · 2019
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Exploring weight symmetry in deep neural networks
Shell Xu Hu, Sergey Zagoruyko, and Nikos Komodakis · 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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Revisiting graph neural networks: All we have is low-pass filters
Hoang Nt and Takanori Maehara · 2019
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Pytorch: An imperative style, high-performance deep learning library
Geometric deep learning: Grids, groups, graphs, geodesics, and gauges
Michael M Bronstein, Joan Bruna, Taco Cohen, and Petar Veličković · 2021
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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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Pde-gcn: Novel architectures for graph neural networks motivated by partial differential equations
Moshe Eliasof, Eldad Haber, and Eran Treister · 2021
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Bernnet: Learning arbitrary graph spectral filters via bernstein approximation
Mingguo He, Zhewei Wei, Hongteng Xu, et al · 2021
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Large scale learning on non-homophilous graphs: New benchmarks and strong simple methods
Derek Lim, Felix Hohne, Xiuyu Li, Sijia Linda Huang, Vaishnavi Gupta, Omkar Bhalerao, and Ser Nam Lim · 2021
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Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Simplifying graph convolutional networks
Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger · 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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Pairnorm: Tackling oversmoothing in gnns
Lingxiao Zhao and Leman Akoglu · 2019
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Analyzing the expressive power of graph neural networks in a spectral perspective
Muhammet Balcilar, Guillaume Renton, Pierre Héroux, Benoit Gaüzère, Sébastien Adam, and Paul Honeine · 2020
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Experiment tracking with weights and biases, 2020
Lukas Biewald · 2020
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A note on over-smoothing for graph neural networks
Chen Cai and Yusu Wang · 2020
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Sitao Luan, Chenqing Hua, Qincheng Lu, Jiaqi Zhu, Mingde Zhao, Shuyuan Zhang, Xiao-Wen Chang, and Doina Precup · 2021
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Improving graph neural networks with simple architecture design
Sunil Kumar Maurya, Xin Liu, and Tsuyoshi Murata · 2021
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Multi-scale attributed node embedding
Benedek Rozemberczki, Carl Allen, and Rik Sarkar · 2021
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Two sides of the same coin: Heterophily and oversmoothing in graph convolutional neural networks
Yujun Yan, Milad Hashemi, Kevin Swersky, Yaoqing Yang, and Danai Koutra · 2021
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Magnet: A neural network for directed graphs
Xitong Zhang, Yixuan He, Nathan Brugnone, Michael Perlmutter, and Matthew Hirn · 2021
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Dirichlet energy constrained learning for deep graph neural networks
Kaixiong Zhou, Xiao Huang, Daochen Zha, Rui Chen, Li Li, Soo-Hyun Choi, and Xia Hu · 2021
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Neural sheaf diffusion: A topological perspective on heterophily and oversmoothing in gnns
Cristian Bodnar, Francesco Di Giovanni, Benjamin Paul Chamberlain, Pietro Liò, and Michael M Bronstein · 2022
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Revisiting heterophily for graph neural networks
Sitao Luan, Chenqing Hua, Qincheng Lu, Jiaqi Zhu, Mingde Zhao, Shuyuan Zhang, Xiao-Wen Chang, and Doina Precup · 2022
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Graph-coupled oscillator networks
T Konstantin Rusch, Benjamin P Chamberlain, James Rowbottom, Siddhartha Mishra, and Michael M Bronstein · 2022
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Understanding over-squashing and bottlenecks on graphs via curvature
Jake Topping, Francesco Di Giovanni, Benjamin Paul Chamberlain, Xiaowen Dong, and Michael M Bronstein · 2022
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Acmp: Allen-cahn message passing for graph neural networks with particle phase transition
Yuelin Wang, Kai Yi, Xinliang Liu, Yu Guang Wang, and Shi Jin · 2022
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On over-squashing in message passing neural networks: The impact of width, depth, and topology
Francesco Di Giovanni, Lorenzo Giusti, Federico Barbero, Giulia Luise, Pietro Lio, and Michael Bronstein · 2023
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Improving graph neural networks with learnable propagation operators
Moshe Eliasof, Lars Ruthotto, and Eran Treister · 2023
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A critical look at the evaluation of gnns under heterophily: are we really making progress?
Oleg Platonov, Denis Kuznedelev, Michael Diskin, Artem Babenko, and Liudmila Prokhorenkova · 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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