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Graph neural networks (GNNs) build on the success of deep learning models by extending them for use in graph spaces.
On information and sufficiency
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Birds of a feather: Homophily in social networks
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A survey on transfer learning
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ImageNet: A large-scale hierarchical image database
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Node classification in social networks. In Social Network Data Analytics
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Algorithms for graph similarity and subgraph matching
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Deep learning of representations for unsupervised and transfer learning
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A Bayesian approach to in silico blood-brain barrier penetration modeling
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How transferable are features in deep neural networks?
Yosinski, J.; Clune, J.; Bengio, Y.; Lipson, H · 2014
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Adam: A method for stochastic optimization
Kingma, D.P.; Ba, J · 2015
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An exact graph edit distance algorithm for solving pattern recognition problems
Abu-Aisheh, Z.; Raveaux, R.; Ramel, J.Y.; Martineau, P · 2015
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Network Science
Barabási, A.L.; others · 2016
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What makes ImageNet good for transfer learning?
Huh, M.; Agrawal, P.; Efros, A.A · 2016
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Towards a Definition of Knowledge Graphs
Ehrlinger, L.; Wöß, W · 2016
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Relational inductive biases, deep learning, and graph networks
Battaglia, P.W.; Hamrick, J.B.; Bapst, V.; Sanchez-Gonzalez, A.; Zambaldi, V.; Malinowski, M.; Tacchetti, A.; Raposo, D.; Santoro, A.; Faulkner, R.; others · 2018
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How Powerful are Graph Neural Networks?
Xu, K.; Hu, W.; Leskovec, J.; Jegelka, S · 2018
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Using pre-training can improve model robustness and uncertainty
Hendrycks, D.; Lee, K.; Mazeika, M · 2019
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Do better ImageNet models transfer better?
Kornblith, S.; Shlens, J.; Le, Q.V · 2019
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Deep Graph Infomax
Velickovic, P.; Fedus, W.; Hamilton, W.L.; Liò, P.; Bengio, Y.; Hjelm, R.D · 2019
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Strategies for Pre-training Graph Neural Networks
Hu, W.; Liu, B.; Gomes, J.; Zitnik, M.; Liang, P.; Pande, V.; Leskovec, J · 2019
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Morris, C.; Kriege, N.M.; Kersting, K.; Mutzel, P · 2016
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Geometric deep learning: going beyond Euclidean data
Bronstein, M.M.; Bruna, J.; LeCun, Y.; Szlam, A.; Vandergheynst, P · 2017
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Semi-supervised classification with graph convolutional networks
Kipf, T.N.; Welling, M · 2017
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Inductive representation learning on large graphs
Hamilton, W.; Ying, Z.; Leskovec, J · 2017
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Neural message passing for quantum chemistry
Gilmer, J.; Schoenholz, S.S.; Riley, P.F.; Vinyals, O.; Dahl, G.E · 2017
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Transfer learning for deep learning on graph-structured data
Lee, J.; Kim, H.; Lee, J.; Yoon, S · 2017
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Network Transfer Learning via Adversarial Domain Adaptation with Graph Convolution
Dai, Q.; Shen, X.; Wu, X.M.; Wang, D · 2019
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Fast Graph Representation Learning with PyTorch Geometric
Fey, M.; Lenssen, J.E · 2019
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PyTorch: An imperative style, high-performance deep learning library
Paszke, A.; Gross, S.; Massa, F.; Lerer, A.; Bradbury, J.; Chanan, G.; Killeen, T.; Lin, Z.; Gimelshein, N.; Antiga, L.; others · 2019
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A comprehensive survey on graph neural networks
Wu, Z.; Pan, S.; Chen, F.; Long, G.; Zhang, C.; Philip, S.Y · 2020
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Graph representation learning
Hamilton, W.L · 2020
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A fair comparison of graph neural networks for graph classification
Errica, F.; Podda, M.; Bacciu, D.; Micheli, A · 2020
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Benchmarking graph neural networks
Dwivedi, V.P.; Joshi, C.K.; Laurent, T.; Bengio, Y.; Bresson, X · 2020
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Open Graph Benchmark: Datasets for Machine Learning on Graphs
Hu, W.; Fey, M.; Zitnik, M.; Dong, Y.; Ren, H.; Liu, B.; Catasta, M.; Leskovec, J · 2020
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Microsoft academic graph: When experts are not enough
Wang, K.; Shen, Z.; Huang, C.; Wu, C.H.; Dong, Y.; Kanakia, A · 2020
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