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This paper studies Dropout Graph Neural Networks (DropGNNs), a new approach that aims to overcome the limitations of standard GNN frameworks.
A survey of the reconstruction conjecture
F. Harary · 1974
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The graph neural network model
F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini · 2008
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Weisfeiler-lehman graph kernels
N. Shervashidze, P. Schweitzer, E. J. Van Leeuwen, K. Mehlhorn, and K. M. Borgwardt · 2011
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Spectral networks and locally connected networks on graphs
J. Bruna, W. Zaremba, A. Szlam, and Y. LeCun · 2014
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Quantum chemistry structures and properties of 134 kilo molecules
R. Ramakrishnan, P. O. Dral, M. Rupp, and O. A. Von Lilienfeld · 2014
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Dropout: a simple way to prevent neural networks from overfitting
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
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Deep graph kernels
P. Yanardag and S. Vishwanathan · 2015
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Diffusion-convolutional neural networks
J. Atwood and D. Towsley · 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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Learning convolutional neural networks for graphs
M. Niepert, M. Ahmed, and K. Kutzkov · 2016
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Neural message passing for quantum chemistry
J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl · 2017
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Inductive representation learning on large graphs
W. Hamilton, Z. Ying, and J. Leskovec · 2017
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Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 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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Graph attention networks
P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio · 2018
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Moleculenet: a benchmark for molecular machine learning
Z. Wu, B. Ramsundar, E. N. Feinberg, J. Gomes, C. Geniesse, A. S. Pappu, K. Leswing, and V. Pande · 2018
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Representation learning on graphs with jumping knowledge networks
Pytorch: An imperative style, high-performance deep learning library
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, et al · 2019
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Approximation ratios of graph neural networks for combinatorial problems
R. Sato, M. Yamada, and H. Kashima · 2019
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How powerful are graph neural networks?
K. Xu, W. Hu, J. Leskovec, and S. Jegelka · 2019
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The surprising power of graph neural networks with random node initialization
R. Abboud, İ. İ. Ceylan, M. Grohe, and T. Lukasiewicz · 2020
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Efficient robustness certificates for discrete data: Sparsity-aware randomized smoothing for graphs, images and more
A. Bojchevski, J. Klicpera, and S. Günnemann · 2020
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K. Xu, C. Li, Y. Tian, T. Sonobe, K.-i. Kawarabayashi, and S. Jegelka · 2018
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An end-to-end deep learning architecture for graph classification
M. Zhang, Z. Cui, M. Neumann, and Y. Chen · 2018
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On the equivalence between graph isomorphism testing and function approximation with gnns
Z. Chen, L. Chen, S. Villar, and J. Bruna · 2019
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Certified adversarial robustness via randomized smoothing
J. Cohen, E. Rosenfeld, and Z. Kolter · 2019
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Fast graph representation learning with pytorch geometric
M. Fey and J. E. Lenssen · 2019
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Strategies for pre-training graph neural networks
W. Hu, B. Liu, J. Gomes, M. Zitnik, P. Liang, V. Pande, and J. Leskovec · 2019
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Provably powerful graph networks
H. Maron, H. Ben-Hamu, H. Serviansky, and Y. Lipman · 2019
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Graph random neural networks for semi-supervised learning on graphs
W. Feng, J. Zhang, Y. Dong, Y. Han, H. Luan, Q. Xu, Q. Yang, E. Kharlamov, and J. Tang · 2020
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Generalization and representational limits of graph neural networks
V. Garg, S. Jegelka, and T. Jaakkola · 2020
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Directional message passing for molecular graphs
J. Klicpera, J. Groß, and S. Günnemann · 2020
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What graph neural networks cannot learn: depth vs width
A. Loukas · 2020
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Dropedge: Towards deep graph convolutional networks on node classification
Y. Rong, W. Huang, T. Xu, and J. Huang · 2020
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Learning to simulate complex physics with graph networks
A. Sanchez-Gonzalez, J. Godwin, T. Pfaff, R. Ying, J. Leskovec, and P. Battaglia · 2020
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A comprehensive survey on graph neural networks
Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and S. Y. Philip · 2020
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Random features strengthen graph neural networks
R. Sato, M. Yamada, and H. Kashima · 2021
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