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Although graph contrastive learning (GCL) has been widely investigated, it is still a challenge to generate effective and stable graph augmentations.
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Deepwalk: Online learning of social representations
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Dropout: a simple way to prevent neural networks from overfitting
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Semi-supervised classification with graph convolutional networks, 2016
Kipf, T. N. and Welling, M · 2016
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Graph attention networks, 2017
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y · 2017
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van den Oord, A., Li, Y., and Vinyals, O · 2018
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Deep graph infomax, 2018
Veličković, P., Fedus, W., Hamilton, W. L., Liò, P., Bengio, Y., and Hjelm, R. D · 2018
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Xu, K., Hu, W., Leskovec, J., and Jegelka, S · 2018
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Learning representations by maximizing mutual information across views
Bachman, P., Hjelm, R. D., and Buchwalter, W · 2019
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Hierarchical graph convolutional networks for semi-supervised node classification
Hu, F., Zhu, Y., Wu, S., Wang, L., and Tan, T · 2019
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Pitfalls of graph neural network evaluation, 2019
Shchur, O., Mumme, M., Bojchevski, A., and Günnemann, S · 2019
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Simplifying graph convolutional networks
Wu, F., Souza, A., Zhang, T., Fifty, C., Yu, T., and Weinberger, K · 2019
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A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G · 2020
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A framework for contrastive self-supervised learning and designing a new approach
Falcon, W. and Cho, K · 2020
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Feng, W., Zhang, J., Dong, Y., Han, Y., Luan, H., Xu, Q., Yang, Q., Kharlamov, E., and Tang, J · 2020
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He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R · 2020
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Hu, W., Fey, M., Zitnik, M., Dong, Y., Ren, H., Liu, B., Catasta, M., and Leskovec, J · 2020
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Towards domain-agnostic contrastive learning
Verma, V., Luong, T., Kawaguchi, K., Pham, H., and Le, Q · 2021
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Graph adversarial self-supervised learning
Yang, L., Zhang, L., and Yang, W · 2021
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Graph contrastive learning with adaptive augmentation
Zhu, Y., Xu, Y., Yu, F., Liu, Q., Wu, S., and Wang, L · 2021
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Positive-incentive noise
Li, X · 2022
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Graph structure learning with variational information bottleneck, 2022
Sun, Q., Li, J., Peng, H., Wu, J., Fu, X., Ji, C., and Philip, S. Y · 2022
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Graph contrastive learning with stable and scalable spectral encoding
Bo, D., Fang, Y., Liu, Y., and Shi, C · 2023
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Morris, C., Kriege, N. M., Bause, F., Kersting, K., Mutzel, P., and Neumann, M · 2020
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Pei, H., Wei, B., Chang, K. C.-C., Lei, Y., and Yang, B · 2020
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Graph representation learning via graphical mutual information maximization, 2020
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Dropedge: Towards deep graph convolutional networks on node classification, 2020
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Graph contrastive learning with augmentations
You, Y., Chen, T., Sui, Y., Chen, T., Wang, Z., and Shen, Y · 2020
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Lin, X., Dong, R., Zhao, Y., and Wang, R · 2023
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Beyond smoothing: Unsupervised graph representation learning with edge heterophily discriminating
Liu, Y., Zheng, Y., Zhang, D., Lee, V., and Pan, S · 2023
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Neighbor contrastive learning on learnable graph augmentation
Shen, X., Sun, D., Pan, S., Zhou, X., and Yang, L. T · 2023
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Variational positive-incentive noise: How noise benefits models
Zhang, H., Huang, S., and Li, X · 2023
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Exploitation of a latent mechanism in graph contrastive learning: Representation scattering
He, D., Shan, L., Zhao, J., Zhang, H., Wang, Z., and Zhang, W · 2024
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Perfect alignment may be poisonous to graph contrastive learning
Liu, J., Tang, H., and Liu, Y · 2024
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Data augmentation of contrastive learning is estimating positive-incentive noise
Zhang, H., Xu, Y., Huang, S., and Li, X · 2024
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Unified graph augmentations for generalized contrastive learning on graphs
Zhuo, J., Lu, Y., Ning, H., Fu, K., He, D., Wang, C., Guo, Y., Wang, Z., Cao, X., Yang, L., et al · 2024
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Enhance vision-language alignment with noise
Huang, S., Zhang, H., and Li, X · 2025
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Why does dropping edges usually outperform adding edges in graph contrastive learning?
Xu, Y., Huang, S., Zhang, H., and Li, X · 2025
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