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Graph neural networks (GNNs) are able to leverage the structure of graph data by passing messages along the edges of the graph.
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Inductive representation learning on large graphs
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Semi-supervised classification with graph convolutional networks
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Probability on trees and networks , volume 42
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Attention is all you need
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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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Deeper insights into graph convolutional networks for semi-supervised learning
Q. Li, Z. Han, and X.-M. Wu · 2018
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Modeling relational data with graph convolutional networks
M. Schlichtkrull, T. N. Kipf, P. Bloem, R. v. d. Berg, I. Titov, and M. Welling · 2018
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Graph attention networks
P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio · 2018
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Representation learning on graphs with jumping knowledge networks
Tudataset: A collection of benchmark datasets for learning with graphs
C. Morris, N. M. Kriege, F. Bause, K. Kersting, P. Mutzel, and M. Neumann · 2020
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Graph neural networks exponentially lose expressive power for node classification
K. Oono and T. Suzuki · 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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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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Pairnorm: Tackling oversmoothing in gnns
L. Zhao and L. Akoglu · 2020
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Graph neural networks: A review of methods and applications
J. Zhou, G. Cui, S. Hu, Z. Zhang, C. Yang, Z. Liu, L. Wang, C. Li, and M. Sun · 2020
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K. Xu, C. Li, Y. Tian, T. Sonobe, K.-i. Kawarabayashi, and S. Jegelka · 2018
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Fast graph representation learning with Pytorch Geometric
M. Fey and J. E. Lenssen · 2019
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Diffusion improves graph learning
J. Klicpera, S. Weißenberger, and S. Günnemann · 2019
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Weisfeiler and Leman go neural: Higher-order graph neural networks
C. Morris, M. Ritzert, M. Fey, W. L. Hamilton, J. E. Lenssen, G. Rattan, and M. Grohe · 2019
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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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How powerful are graph neural networks?
K. Xu, W. Hu, J. Leskovec, and S. Jegelka · 2019
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The logical expressiveness of graph neural networks
P. Barceló, E. Kostylev, M. Monet, J. Pérez, J. Reutter, and J.-P. Silva · 2020
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On the bottleneck of graph neural networks and its practical implications
U. Alon and E. Yahav · 2021
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A generalization of transformer networks to graphs
V. P. Dwivedi and X. Bresson · 2021
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Rethinking graph transformers with spectral attention
D. Kreuzer, D. Beaini, W. Hamilton, V. Létourneau, and P. Tossou · 2021
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DiffWire: Inductive graph rewiring via the Lovász bound
A. Arnaiz-Rodríguez, A. Begga, F. Escolano, and N. Oliver · 2022
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Oversquashing in GNNs through the lens of information contraction and graph expansion
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Rewiring networks for graph neural network training using discrete geometry, 2022
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Rewiring with positional encodings for graph neural networks, 2022
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Graph neural networks as gradient flows
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Understanding over-squashing and bottlenecks on graphs via curvature
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