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Graph Neural Networks (GNNs) extend basic Neural Networks (NNs) by using the graph structures based on the relational inductive bias (homophily assumption).
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Gradient-based learning applied to document recognition
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Birds of a feather: Homophily in social networks
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The graph neural network model
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Imagenet classification with deep convolutional neural networks
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Speech recognition with deep recurrent neural networks
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Neural machine translation by jointly learning to align and translate
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Graph structured data viewed through a fourier lens
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Adam: A method for stochastic optimization
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Deep learning
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Geometric deep learning: going beyond euclidean data
M. M. Bronstein, J. Bruna, Y. LeCun, A. Szlam, and P. Vandergheynst · 2016
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Convolutional neural networks on graphs with fast localized spectral filtering
M. Defferrard, X. Bresson, and P. Vandergheynst · 2016
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Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2016
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Inductive representation learning on large graphs
W. L. Hamilton, R. Ying, and J. Leskovec · 2017
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Decoupled weight decay regularization
I. Loshchilov and F. Hutter · 2017
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P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio · 2017
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Relational inductive biases, deep learning, and graph networks
P. W. Battaglia, J. B. Hamrick, V. Bapst, A. Sanchez-Gonzalez, V. Zambaldi, M. Malinowski, A. Tacchetti, D. Raposo, A. Santoro, R. Faulkner, et al · 2018
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Predict then propagate: Graph neural networks meet personalized pagerank
J. Klicpera, A. Bojchevski, and S. Günnemann · 2018
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Deeper insights into graph convolutional networks for semi-supervised learning
Simple and deep graph convolutional networks
M. Chen, Z. Wei, Z. Huang, B. Ding, and Y. Li · 2020
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Graph representation learning
W. L. Hamilton · 2020
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Non-local graph neural networks
M. Liu, Z. Wang, and S. Ji · 2020
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S. Luan, M. Zhao, C. Hua, X.-W. Chang, and D. Precup · 2020
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Scattering gcn: Overcoming oversmoothness in graph convolutional networks
Y. Min, F. Wenkel, and G. Wolf · 2020
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Q. Li, Z. Han, and X. Wu · 2018
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Representation learning on graphs with jumping knowledge networks
K. Xu, C. Li, Y. Tian, T. Sonobe, K.-i. Kawarabayashi, and S. Jegelka · 2018
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Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing
S. Abu-El-Haija, B. Perozzi, A. Kapoor, N. Alipourfard, K. Lerman, H. Harutyunyan, G. Ver Steeg, and A. Galstyan · 2019
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Fast graph representation learning with pytorch geometric
M. Fey and J. E. Lenssen · 2019
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Geometric scattering for graph data analysis
F. Gao, G. Wolf, and M. Hirn · 2019
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Measuring and improving the use of graph information in graph neural networks
Y. Hou, J. Zhang, J. Cheng, K. Ma, R. T. Ma, H. Chen, and M.-C. Yang · 2019
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Break the ceiling: Stronger multi-scale deep graph convolutional networks
S. Luan, M. Zhao, X.-W. Chang, and D. Precup · 2019
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Revisiting graph neural networks: All we have is low-pass filters
T. Maehara · 2019
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H. Pei, B. Wei, K. C.-C. Chang, Y. Lei, and B. Yang · 2020
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Graph neural networks with heterophily
J. Zhu, R. A. Rossi, A. Rao, T. Mai, N. Lipka, N. K. Ahmed, and D. Koutra · 2020
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Beyond homophily in graph neural networks: Current limitations and effective designs
J. Zhu, Y. Yan, L. Zhao, M. Heimann, L. Akoglu, and D. Koutra · 2020
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Beyond low-frequency information in graph convolutional networks
D. Bo, X. Wang, C. Shi, and H. Shen · 2021
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Adaptive universal generalized pagerank graph neural network
E. Chien, J. Peng, P. Li, and O. Milenkovic · 2021
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New benchmarks for learning on non-homophilous graphs
D. Lim, X. Li, F. Hohne, and S.-N. Lim · 2021
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Simple truncated svd based model for node classification on heterophilic graphs
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Two sides of the same coin: Heterophily and oversmoothing in graph convolutional neural networks
Y. Yan, M. Hashemi, K. Swersky, Y. Yang, and D. Koutra · 2021
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