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Graph convolutional networks (GCNs) have achieved promising performance on various graph-based tasks.
Graph neural networks exponentially lose expressive power for node classification
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Continuous Graph Neural Networks
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The PageRank citation ranking: Bringing order to the web
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Revisiting” Over-smoothing” in Deep GCNs
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Open graph benchmark: Datasets for machine learning on graphs
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A Note on Over-Smoothing for Graph Neural Networks
Cai, C.; and Wang, Y. 2020 · 2006
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DeeperGCN: All You Need to Train Deeper GCNs
Li, G.; Xiong, C.; Thabet, A.; and Ghanem, B. 2020 · 2006
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Simple and Deep Graph Convolutional Networks
Chen, M.; Wei, Z.; Huang, Z.; Ding, B.; and Li, Y. 2020 · 2007
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Collective classification in network data
Sen, P.; Namata, G.; Bilgic, M.; Getoor, L.; Galligher, B.; and Eliassi-Rad, T. 2008 · 2008
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Stochastic Graph Recurrent Neural Network
Yan, T.; Zhang, H.; Li, Z.; and Xia, Y. 2020 · 2009
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Predicting multicellular function through multi-layer tissue networks
Zitnik, M.; and Leskovec, J. 2017 · 2009
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Spectral networks and locally connected networks on graphs
Bruna, J.; Zaremba, W.; Szlam, A.; and LeCun, Y. 2013 · 2013
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Adam: A method for stochastic optimization
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Deepwalk: Online learning of social representations
Perozzi, B.; Al-Rfou, R.; and Skiena, S. 2014 · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
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Gated graph sequence neural networks
Li, Y.; Tarlow, D.; Brockschmidt, M.; and Zemel, R. 2015 · 2015
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Line: Large-scale information network embedding
Tang, J.; Qu, M.; Wang, M.; Zhang, M.; Yan, J.; and Mei, Q. 2015 · 2015
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Convolutional neural networks on graphs with fast localized spectral filtering
Defferrard, M.; Bresson, X.; and Vandergheynst, P. 2016 · 2016
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node2vec: Scalable feature learning for networks
Grover, A.; and Leskovec, J. 2016 · 2016
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Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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Revisiting semi-supervised learning with graph embeddings
Yang, Z.; Cohen, W.; and Salakhudinov, R. 2016 · 2016
Link prediction based on graph neural networks
Zhang, M.; and Chen, Y. 2018 · 2018
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An end-to-end deep learning architecture for graph classification
Zhang, M.; Cui, Z.; Neumann, M.; and Chen, Y. 2018 · 2018
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Fast Graph Representation Learning with PyTorch Geometric
Fey, M.; and Lenssen, J. E. 2019 · 2019
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Attention based spatial-temporal graph convolutional networks for traffic flow forecasting
Guo, S.; Lin, Y.; Feng, N.; Song, C.; and Wan, H. 2019 · 2019
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Variational graph recurrent neural networks
Hajiramezanali, E.; Hasanzadeh, A.; Narayanan, K.; Duffield, N.; Zhou, M.; and Qian, X. 2019 · 2019
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Predict then propagate: Graph neural networks meet personalized pagerank
Klicpera, J.; Bojchevski, A.; and Günnemann, S. 2019 · 2019
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Neural message passing for quantum chemistry
Gilmer, J.; Schoenholz, S. S.; Riley, P. F.; Vinyals, O.; and Dahl, G. E. 2017 · 2017
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Inductive representation learning on large graphs
Hamilton, W.; Ying, Z.; and Leskovec, J. 2017 · 2017
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Geometric matrix completion with recurrent multi-graph neural networks
Monti, F.; Bronstein, M.; and Bresson, X. 2017 · 2017
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Weisfeiler-lehman neural machine for link prediction
Zhang, M.; and Chen, Y. 2017 · 2017
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Learning steady-states of iterative algorithms over graphs
Dai, H.; Kozareva, Z.; Dai, B.; Smola, A.; and Song, L. 2018 · 2018
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Large-Scale Learnable Graph Convolutional Networks 1416–1424
Gao, H.; Wang, Z.; and Ji, S. 2018 · 2018
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Self-Attention Graph Pooling
Lee, J.; Lee, I.; and Kang, J. 2019 · 2019
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Encoding social information with graph convolutional networks forPolitical perspective detection in news media
Li, C.; and Goldwasser, D. 2019 · 2019
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Geniepath: Graph neural networks with adaptive receptive paths
Liu, Z.; Chen, C.; Li, L.; Zhou, J.; Li, X.; Song, L.; and Qi, Y. 2019 · 2019
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Graph convolutional networks with eigenpooling
Ma, Y.; Wang, S.; Aggarwal, C. C.; and Tang, J. 2019 · 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.; et al. 2019 · 2019
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Dropedge: Towards deep graph convolutional networks on node classification
Rong, Y.; Huang, W.; Xu, T.; and Huang, J. 2019 · 2019
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Simplifying Graph Convolutional Networks
Wu, F.; Souza, A.; Zhang, T.; Fifty, C.; Yu, T.; and Weinberger, K. 2019 · 2019
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A Multi-Scale Approach for Graph Link Prediction
Cai, L.; and Ji, S. 2020 · 2020
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Towards Deeper Graph Neural Networks
Liu, M.; Gao, H.; and Ji, S. 2020 · 2020
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