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Contrastive learning has been widely applied to graph representation learning, where the view generators play a vital role in generating effective contrastive samples.
Are powerful graph neural nets necessary? a dissection on graph classification
Chen, T.; Bian, S.; and Sun, Y. 2019 · 1905
Earlier work this paper cites.
Strategies for pre-training graph neural networks
Hu, W.; Liu, B.; Gomes, J.; Zitnik, M.; Liang, P.; Pande, V.; and Leskovec, J. 2019 · 1905
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Graphnvp: An invertible flow model for generating molecular graphs
Madhawa, K.; Ishiguro, K.; Nakago, K.; and Abe, M. 2019 · 1905
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Tian, Y.; Krishnan, D.; and Isola, P. 2019 · 1906
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Xlnet: Generalized autoregressive pretraining for language understanding
Yang, Z.; Dai, Z.; Yang, Y.; Carbonell, J.; Salakhutdinov, R.; and Le, Q. V. 2019 · 1906
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Sun, F.-Y.; Hoffmann, J.; Verma, V.; and Tang, J. 2019 · 1908
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Gradient-based learning applied to document recognition
LeCun, Y.; Bottou, L.; Bengio, Y.; and Haffner, P. 1998 · 1998
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Improved baselines with momentum contrastive learning
Chen, X.; Fan, H.; Girshick, R.; and He, K. 2020b · 2003
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To transfer or not to transfer
Rosenstein, M. T.; Marx, Z.; Kaelbling, L. P.; and Dietterich, T. G. 2005 · 2005
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What makes for good views for contrastive learning
Tian, Y.; Sun, C.; Poole, B.; Krishnan, D.; Schmid, C.; and Isola, P. 2020 · 2005
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Bootstrap your own latent: A new approach to self-supervised learning
Grill, J.-B.; Strub, F.; Altché, F.; Tallec, C.; Richemond, P. H.; Buchatskaya, E.; Doersch, C.; Pires, B. A.; Guo, Z. D.; Azar, M. G.; et al. 2020 · 2006
Earlier work this paper cites.
Dimensionality reduction by learning an invariant mapping
Hadsell, R.; Chopra, S.; and LeCun, Y. 2006 · 2006
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Deep graph contrastive representation learning
Zhu, Y.; Xu, Y.; Yu, F.; Liu, Q.; Wu, S.; and Wang, L. 2020a · 2006
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Visualizing data using t-SNE
Van der Maaten, L.; and Hinton, G. 2008 · 2008
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Efficient graphlet kernels for large graph comparison
Shervashidze, N.; Vishwanathan, S.; Petri, T.; Mehlhorn, K.; and Borgwardt, K. 2009 · 2009
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Graph Contrastive Learning with Adaptive Augmentation
Zhu, Y.; Xu, Y.; Yu, F.; Liu, Q.; Wu, S.; and Wang, L. 2020b · 2010
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Weisfeiler-lehman graph kernels
Shervashidze, N.; Schweitzer, P.; Van Leeuwen, E. J.; Mehlhorn, K.; and Borgwardt, K. M. 2011 · 2011
Cited alongside, same era.
ChEMBL: a large-scale bioactivity database for drug discovery
Gaulton, A.; Bellis, L. J.; Bento, A. P.; Chambers, J.; Davies, M.; Hersey, A.; Light, Y.; McGlinchey, S.; Michalovich, D.; Al-Lazikani, B.; et al. 2012 · 2012
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A.; Sutskever, I.; and Hinton, G. E. 2012 · 2012
Cited alongside, same era.
The Network Data Repository with Interactive Graph Analytics and Visualization
Rossi, R. A.; and Ahmed, N. K. 2015 · 2015
Cited alongside, same era.
Deep graph kernels
Yanardag, P.; and Vishwanathan, S. 2015 · 2015
Cited alongside, same era.
node2vec: Scalable feature learning for networks
Grover, A.; and Leskovec, J. 2016 · 2016
Cited alongside, same era.
Large-scale comparison of machine learning methods for drug target prediction on ChEMBL
Mayr, A.; Klambauer, G.; Unterthiner, T.; Steijaert, M.; Wegner, J. K.; Ceulemans, H.; Clevert, D.-A.; and Hochreiter, S. 2018 · 2018
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Veličković, P.; Fedus, W.; Hamilton, W. L.; Liò, P.; Bengio, Y.; and Hjelm, R. D. 2018 · 2018
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How powerful are graph neural networks?
Xu, K.; Hu, W.; Leskovec, J.; and Jegelka, S. 2018 · 2018
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Autoaugment: Learning augmentation strategies from data
Cubuk, E. D.; Zoph, B.; Mane, D.; Vasudevan, V.; and Le, Q. V. 2019 · 2019
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Graph neural networks for social recommendation
Fan, W.; Ma, Y.; Li, Q.; He, Y.; Zhao, E.; Tang, J.; and Yin, D. 2019 · 2019
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Categorical reparameterization with gumbel-softmax
Jang, E.; Gu, S.; and Poole, B. 2016 · 2016
Cited alongside, same era.
Improved deep metric learning with multi-class n-pair loss objective
Sohn, K. 2016 · 2016
Cited alongside, same era.
Inductive representation learning on large graphs
Hamilton, W. L.; Ying, R.; and Leskovec, J. 2017 · 2017
Cited alongside, same era.
Geometric deep learning on graphs and manifolds using mixture model cnns
Monti, F.; Boscaini, D.; Masci, J.; Rodola, E.; Svoboda, J.; and Bronstein, M. M. 2017 · 2017
Cited alongside, same era.
graph2vec: Learning distributed representations of graphs
Narayanan, A.; Chandramohan, M.; Venkatesan, R.; Chen, L.; Liu, Y.; and Jaiswal, S. 2017 · 2017
Cited alongside, same era.
Veličković, P.; Cucurull, G.; Casanova, A.; Romero, A.; Lio, P.; and Bengio, Y. 2017 · 2017
Cited alongside, same era.
Contrastive multi-view representation learning on graphs
Hassani, K.; and Khasahmadi, A. H. 2020 · 2020
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Faster autoaugment: Learning augmentation strategies using backpropagation
Hataya, R.; Zdenek, J.; Yoshizoe, K.; and Nakayama, H. 2020 · 2020
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Momentum contrast for unsupervised visual representation learning
He, K.; Fan, H.; Wu, Y.; Xie, S.; and Girshick, R. 2020 · 2020
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Differentiable Automatic Data Augmentation
Li, Y.; Hu, G.; Wang, Y.; Hospedales, T.; Robertson, N. M.; and Yang, Y. 2020 · 2020
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TUDataset: A collection of benchmark datasets for learning with graphs
Morris, C.; Kriege, N. M.; Bause, F.; Kersting, K.; Mutzel, P.; and Neumann, M. 2020 · 2020
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Point-gnn: Graph neural network for 3d object detection in a point cloud
Shi, W.; and Rajkumar, R. 2020 · 2020
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Adversarial Graph Augmentation to Improve Graph Contrastive Learning
Suresh, S.; Li, P.; Hao, C.; and Neville, J. 2021 · 2021
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Molecular Graph Contrastive Learning with Parameterized Explainable Augmentations
Wang, Y.; Min, Y.; Shao, E.; and Wu, J. 2021 · 2021
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Graph Contrastive Learning Automated
You, Y.; Chen, T.; Shen, Y.; and Wang, Z. 2021 · 2021
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Barlow twins: Self-supervised learning via redundancy reduction
Zbontar, J.; Jing, L.; Misra, I.; LeCun, Y.; and Deny, S. 2021 · 2021
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