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We study self-supervised learning on graphs using contrastive methods.
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C. Wang, S. Pan, G. Long, X. Zhu, and J. Jiang, “Mgae: Marginalized graph autoencoder for graph clustering,” in Proceedings of the 2017 ACM on Conference on Information and Knowledge Management , 2017, pp. 889–898
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P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio, “Graph attention networks,” in International Conference on Learning Representations , 2018
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H. Gao, Z. Wang, and S. Ji, “Large-scale learnable graph convolutional networks,” in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , 2018, pp. 1416–1424
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M. Opitz, G. Waltner, H. Possegger, and H. Bischof, “Deep metric learning with bier: Boosting independent embeddings robustly,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 42, no. 2, pp. 276–290, 2018
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W. Kim, B. Goyal, K. Chawla, J. Lee, and K. Kwon, “Attention-based ensemble for deep metric learning,” in Proceedings of the European Conference on Computer Vision , 2018, pp. 736–751
2018
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B. Adhikari, Y. Zhang, N. Ramakrishnan, and B. A. Prakash, “Sub2vec: Feature learning for subgraphs,” in Pacific-Asia Conference on Knowledge Discovery and Data Mining . Springer, 2018, pp. 170–182
2018
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2018
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H. Gao and S. Ji, “Graph representation learning via hard and channel-wise attention networks,” in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , 2019, pp. 741–749
2019
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J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” in North American Chapter of the Association for Computational Linguistics , 2019
2019
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Y. Xie, Z. Wang, and S. Ji, “Noise2same: Optimizing a self-supervised bound for image denoising,” in Advances in Neural Information Processing Systems , 2020
2020
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Y. You, T. Chen, Y. Sui, T. Chen, Z. Wang, and Y. Shen, “Graph contrastive learning with augmentations,” in Advances in Neural Information Processing Systems , vol. 33, 2020
2020
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T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, “A simple framework for contrastive learning of visual representations,” in International Conference on Machine Learning , 2020, pp. 1597–1607
2020
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F.-Y. Sun, J. Hoffmann, V. Verma, and J. Tang, “Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization,” in International Conference on Learning Representations , 2020
2020
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H. Wang, X. Wang, W. Xiong, M. Yu, X. Guo, S. Chang, and W. Y. Wang, “Self-supervised learning for contextualized extractive summarization,” in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics , 2019
2019
Cited alongside, same era.
Z. Yang, Z. Dai, Y. Yang, J. Carbonell, R. R. Salakhutdinov, and Q. V. Le, “Xlnet: Generalized autoregressive pretraining for language understanding,” in Advances in Neural Information Processing Systems , vol. 32, 2019
2019
Cited alongside, same era.
P. Veličković, W. Fedus, W. L. Hamilton, P. Liò, Y. Bengio, and R. D. Hjelm, “Deep graph infomax,” in International Conference on Learning Representations , 2019
2019
Cited alongside, same era.
R. D. Hjelm, A. Fedorov, S. Lavoie-Marchildon, K. Grewal, P. Bachman, A. Trischler, and Y. Bengio, “Learning deep representations by mutual information estimation and maximization,” in International Conference on Learning Representations , 2019
2019
Cited alongside, same era.
B. Chen and W. Deng, “Hybrid-attention based decoupled metric learning for zero-shot image retrieval,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 2750–2759
2019
Cited alongside, same era.
K. Xu, W. Hu, J. Leskovec, and S. Jegelka, “How powerful are graph neural networks?” in International Conference on Learning Representations , 2019
2019
Cited alongside, same era.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “Pytorch: An imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems , 2019, pp. 8024–8035
2019
Cited alongside, same era.
M. Fey and J. E. Lenssen, “Fast graph representation learning with PyTorch Geometric,” in International Conference on Learning Representations Workshop , 2019
2019
Cited alongside, same era.
Y. Zhu, Y. Xu, F. Yu, Q. Liu, S. Wu, and L. Wang, “Deep graph contrastive representation learning,” in International Conference on Machine Learning Workshops , 2020
2020
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Y. Jiao, Y. Xiong, J. Zhang, Y. Zhang, T. Zhang, and Y. Zhu, “Sub-graph contrast for scalable self-supervised graph representation learning,” in IEEE International Conference on Data Mining , 2020, pp. 222–231
2020
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Z. Peng, W. Huang, M. Luo, Q. Zheng, Y. Rong, T. Xu, and J. Huang, “Graph representation learning via graphical mutual information maximization,” in Proceedings of the Web Conference , 2020, pp. 259–270
2020
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W. L. Hamilton, “Graph representation learning,” Synthesis Lectures on Artifical Intelligence and Machine Learning , vol. 14, no. 3, pp. 1–159, 2020
2020
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K. Hassani and A. H. Khasahmadi, “Contrastive multi-view representation learning on graphs,” in International Conference on Machine Learning , 2020, pp. 4116–4126
2020
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M. Tschannen, J. Djolonga, P. K. Rubenstein, S. Gelly, and M. Lucic, “On mutual information maximization for representation learning,” in International Conference on Learning Representations , 2020
2020
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2020
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P. Cheng, W. Hao, S. Dai, J. Liu, Z. Gan, and L. Carin, “Club: A contrastive log-ratio upper bound of mutual information,” in International Conference on Machine Learning , 2020, pp. 1779–1788
2020
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2020
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2021
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2021
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