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Modern graph neural networks (GNNs) use a message passing scheme and have achieved great success in many fields.
The pagerank citation ranking: Bringing order to the web
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Spectral networks and locally connected networks on graphs
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Neural machine translation by jointly learning to align and translate
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Convolutional neural networks on graphs with fast localized spectral filtering
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node2vec: Scalable feature learning for networks
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Gated graph sequence neural networks
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Hierarchical attention networks for document classification
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Geometric deep learning: going beyond euclidean data
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Neural message passing for quantum chemistry
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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
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Automatic differentiation in pytorch
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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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Fastgcn: Fast learning with graph convolutional networks via importance sampling
J. Chen, T. Ma, and C. Xiao · 2018
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Stochastic training of graph convolutional networks with variance reduction
J. Chen, J. Zhu, and L. Song · 2018
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Large-scale learnable graph convolutional networks
H. Gao, Z. Wang, and S. Ji · 2018
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Adaptive sampling towards fast graph representation learning
W. Huang, T. Zhang, Y. Rong, and J. Huang · 2018
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Anonymous walk embeddings
Simplifying graph convolutional networks
F. Wu, A. Souza, T. Zhang, C. Fifty, T. Yu, and K. Weinberger · 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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Layer-dependent importance sampling for training deep and large graph convolutional networks
D. Zou, Z. Hu, Y. Wang, S. Jiang, Y. Sun, and Q. Gu · 2019
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On the bottleneck of graph neural networks and its practical implications
U. Alon and E. Yahav · 2020
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Scaling graph neural networks with approximate pagerank
A. Bojchevski, J. Klicpera, B. Perozzi, A. Kapoor, M. Blais, B. Rózemberczki, M. Lukasik, and S. Günnemann · 2020
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Contrastive multi-view representation learning on graphs
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S. Ivanov and E. Burnaev · 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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Graph convolutional neural networks for web-scale recommender systems
R. Ying, R. He, K. Chen, P. Eksombatchai, W. L. Hamilton, and J. Leskovec · 2018
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Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks
W.-L. Chiang, X. Liu, S. Si, Y. Li, S. Bengio, and C.-J. Hsieh · 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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Predict then propagate: Graph neural networks meet personalized pagerank
J. Klicpera, A. Bojchevski, and S. Günnemann · 2019
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K. Hassani and A. H. Khasahmadi · 2020
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Open graph benchmark: Datasets for machine learning on graphs
W. Hu, M. Fey, M. Zitnik, Y. Dong, H. Ren, B. Liu, M. Catasta, and J. Leskovec · 2020
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Gpt-gnn: Generative pre-training of graph neural networks
Z. Hu, Y. Dong, K. Wang, K.-W. Chang, and Y. Sun · 2020
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Self-supervised learning on graphs: Deep insights and new direction
W. Jin, T. Derr, H. Liu, Y. Wang, S. Wang, Z. Liu, and J. Tang · 2020
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Gcc: Graph contrastive coding for graph neural network pre-training
J. Qiu, Q. Chen, Y. Dong, J. Zhang, H. Yang, M. Ding, K. Wang, and J. Tang · 2020
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Sign: Scalable inception graph neural networks
E. Rossi, F. Frasca, B. Chamberlain, D. Eynard, M. Bronstein, and F. Monti · 2020
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A deep learning approach to antibiotic discovery
J. M. Stokes, K. Yang, K. Swanson, W. Jin, A. Cubillos-Ruiz, N. M. Donghia, C. R. MacNair, S. French, L. A. Carfrae, Z. Bloom-Ackerman, et al · 2020
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Advanced graph and sequence neural networks for molecular property prediction and drug discovery
Z. Wang, M. Liu, Y. Luo, Z. Xu, Y. Xie, L. Wang, L. Cai, and S. Ji · 2020
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When does self-supervision help graph convolutional networks?
Y. You, T. Chen, Z. Wang, and Y. Shen · 2020
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Graphsaint: Graph sampling based inductive learning method
H. Zeng, H. Zhou, A. Srivastava, R. Kannan, and V. Prasanna · 2020
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