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Graph Neural Networks (GNNs) have become mainstream methods for solving the semi-supervised node classification problem.
Improving predictive inference under covariate shift by weighting the log-likelihood function
Shimodaira, H. 2000 · 2000
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On the bottleneck of graph neural networks and its practical implications
Alon, U.; and Yahav, E. 2020 · 2006
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Pattern recognition and machine learning , volume 4
Bishop, C. M.; and Nasrabadi, N. M. 2006 · 2006
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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Lee, D.-H.; et al. 2013 · 2013
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Adam: A method for stochastic optimization
Kingma, D.; and Ba, J. 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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Revisiting semi-supervised learning with graph embeddings
Yang, Z.; Cohen, W.; and Salakhudinov, R. 2016 · 2016
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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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Semi-Supervised Classification with Graph Convolutional Networks
Kipf, N. T.; and Welling, M. 2017 · 2017
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mixup: Beyond empirical risk minimization
Zhang, H.; Cisse, M.; Dauphin, Y. N.; and Lopez-Paz, D. 2017 · 2017
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Bootstrapped graph diffusions: Exposing the power of nonlinearity
Buchnik, E.; and Cohen, E. 2018 · 2018
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Deeper insights into graph convolutional networks for semi-supervised learning
Li, Q.; Han, Z.; and Wu, X.-M. 2018 · 2018
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Pitfalls of graph neural network evaluation
Shchur, O.; Mumme, M.; Bojchevski, A.; and Günnemann, S. 2018 · 2018
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Graph Attention Networks
Velickovic, P.; Cucurull, G.; Casanova, A.; Romero, A.; Liò, P.; and Bengio, Y. 2018 · 2018
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Representation learning on graphs with jumping knowledge networks
Xu, K.; Li, C.; Tian, Y.; Sonobe, T.; Kawarabayashi, K.-i.; and Jegelka, S. 2018 · 2018
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Mixmatch: A holistic approach to semi-supervised learning
Berthelot, D.; Carlini, N.; Goodfellow, I.; Papernot, N.; Oliver, A.; and Raffel, C. A. 2019 · 2019
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Fast Graph Representation Learning with PyTorch Geometric
Fey, M.; and Lenssen, J. E. 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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Similarity of neural network representations revisited
Kornblith, S.; Norouzi, M.; Lee, H.; and Hinton, G. 2019 · 2019
ifmixup: Towards intrusion-free graph mixup for graph classification
Guo, H.; and Mao, Y. 2021 · 2021
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SkipNode: On Alleviating Over-smoothing for Deep Graph Convolutional Networks
Lu, W.; Zhan, Y.; Guan, Z.; Liu, L.; Yu, B.; Zhao, W.; Yang, Y.; and Tao, D. 2021 · 2021
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Rizve, M. N.; Duarte, K.; Rawat, Y. S.; and Shah, M. 2021 · 2021
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Graphmix: Improved training of gnns for semi-supervised learning
Verma, V.; Qu, M.; Kawaguchi, K.; Lamb, A.; Bengio, Y.; Kannala, J.; and Tang, J. 2021 · 2021
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Mixup for node and graph classification
Wang, Y.; Wang, W.; Liang, Y.; Cai, Y.; and Hooi, B. 2021 · 2021
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Cited alongside, same era.
Simplifying graph convolutional networks
Wu, F.; Souza, A.; Zhang, T.; Fifty, C.; Yu, T.; and Weinberger, K. 2019 · 2019
Cited alongside, same era.
How Powerful are Graph Neural Networks?
Xu, K.; Hu, W.; Leskovec, J.; and Jegelka, S. 2019 · 2019
Cited alongside, same era.
The logical expressiveness of graph neural networks
Barceló, P.; Kostylev, E. V.; Monet, M.; Pérez, J.; Reutter, J.; and Silva, J.-P. 2020 · 2020
Cited alongside, same era.
Simple and deep graph convolutional networks
Chen, M.; Wei, Z.; Huang, Z.; Ding, B.; and Li, Y. 2020 · 2020
Cited alongside, same era.
Open graph benchmark: Datasets for machine learning on graphs
Hu, W.; Fey, M.; Zitnik, M.; Dong, Y.; Ren, H.; Liu, B.; Catasta, M.; and Leskovec, J. 2020 · 2020
Cited alongside, same era.
Graph Neural Networks Exponentially Lose Expressive Power for Node Classification
Oono, K.; and Suzuki, T. 2020 · 2020
Cited alongside, same era.
Wu, L.; Lin, H.; Gao, Z.; Tan, C.; Li, S.; et al. 2021 · 2021
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Rewiring with positional encodings for graph neural networks
Brüel-Gabrielsson, R.; Yurochkin, M.; and Solomon, J. 2022 · 2022
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Metric Based Few-Shot Graph Classification
Crisostomi, D.; Antonelli, S.; Maiorca, V.; Moschella, L.; Marin, R.; and Rodolà, E. 2022 · 2022
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GraphMAD: Graph Mixup for Data Augmentation using Data-Driven Convex Clustering
Navarro, M.; and Segarra, S. 2022 · 2022
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Graph transplant: Node saliency-guided graph mixup with local structure preservation
Park, J.; Shim, H.; and Yang, E. 2022 · 2022
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Position-aware structure learning for graph topology-imbalance by relieving under-reaching and over-squashing
Sun, Q.; Li, J.; Yuan, H.; Fu, X.; Peng, H.; Ji, C.; Li, Q.; and Yu, P. S. 2022 · 2022
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Understanding over-squashing and bottlenecks on graphs via curvature
Topping, J.; Giovanni, D. F.; Chamberlain, P. B.; Dong, X.; and Bronstein, M. M. 2021 · 2022
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On the distribution alignment of propagation in graph neural networks
Zheng, Q.; Xia, X.; Zhang, K.; Kharlamov, E.; and Dong, Y. 2022 · 2022
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On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology
Di Giovanni, F.; Giusti, L.; Barbero, F.; Luise, G.; Lio, P.; and Bronstein, M. 2023 · 2023
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