计算机技术 |
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基于无负样本损失和自适应增强的图对比学习 |
周天琪( ),杨艳*( ),张继杰,殷少伟,郭增强 |
黑龙江大学 计算机科学技术学院,黑龙江 哈尔滨 150000 |
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Graph contrastive learning based on negative-sample-free loss and adaptive augmentation |
Tian-qi ZHOU( ),Yan YANG*( ),Ji-jie ZHANG,Shao-wei YIN,Zeng-qiang GUO |
College of Computer Science and Technology, Heilongjiang University, Harbin 150000, China |
引用本文:
周天琪,杨艳,张继杰,殷少伟,郭增强. 基于无负样本损失和自适应增强的图对比学习[J]. 浙江大学学报(工学版), 2023, 57(2): 259-266.
Tian-qi ZHOU,Yan YANG,Ji-jie ZHANG,Shao-wei YIN,Zeng-qiang GUO. Graph contrastive learning based on negative-sample-free loss and adaptive augmentation. Journal of ZheJiang University (Engineering Science), 2023, 57(2): 259-266.
链接本文:
https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2023.02.006
或
https://www.zjujournals.com/eng/CN/Y2023/V57/I2/259
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1 |
XU K, HU W, LESKOVEC J, et al. How powerful are graph neural networks [C]// Proceedings of the 7th International Conference on Learning Representations. New Orleans: [s.n.], 2019: 1-17.
|
2 |
ABU-EL-HAIJA S, PEROZZI B, KAPOOR A, et al. Mixhop: higher-order graph convolutional architectures via sparsified neighborhood mixing [C]// Proceedings of the 36th International Conference on Machine Learning. Long Beach: PMLR , 2019: 21-29.
|
3 |
YOU J, YING R, LESKOVEC J. Position-aware graph neural networks [C]// Proceedings of the 36th International Conference on Machine Learning. Long Beach: PMLR, 2019: 7134-7143.
|
4 |
张雁操, 赵宇海, 史岚 融合图注意力的多特征链接预测算法[J]. 计算机科学与探索, 2022, 16 (5): 1096- 1106 ZHANG Yan-cao, ZHAO Yu-hai, SHI Lan Multi-feature based link prediction algorithm fusing graph attention[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16 (5): 1096- 1106
doi: 10.3778/j.issn.1673-9418.2012092
|
5 |
KIPF T N, WELLING M. Semi-supervised classification with graph convolutional networks [C]// Proceedings of the 5th International Conference on Learning Representations. Toulon: [s. n. ], 2017: 1-14.
|
6 |
VELICKOVIC P, FEDUS W, HAMILTON W L, et al. Deep graph Infomax [C]// Proceedings of the 7th International Conference on Learning Representations. New Orleans: [s.n.], 2019: 1-17.
|
7 |
YOU Y, CHEN T L, SUI Y D, et al. Graph contrastive learning with augmentations [C]// Advances in Neural Information Processing Systems. [s.l.]: MIT Press, 2020: 1-12.
|
8 |
HASSANI K, AHMADI A H K. Contrastive multi-view representation learning on graphs [C]// Proceedings of the 37th International Conference on Machine Learning. [s.l.]: PMLR, 2020: 4116-4126.
|
9 |
ZHU Y Q, XU Y C, LIU Q, et al. Graph contrastive learning with adaptive augmentation [C]// Proceedings of the 2021 World Wide Web Conference. [s.l.]: ACM, 2021: 2069-2080.
|
10 |
ZHU Y Q, XU Y C, YU F, et al. Deep graph contrastive representation learning [EB/OL]. [2022-03-21]. https://arxiv.org/abs/2006.04131.
|
11 |
THAKOOR S, TALLEC C, AZAR M G, et al. Bootstrapped representation learning on graphs [EB/OL]. [2021-02-18]. https://arxiv.org/abs/2102.06514.
|
12 |
BIELAK P, KAJDANOWICZ T, CHAWLA N V. Graph Barlow Twins: a self-supervised representation learning framework for graphs [EB/OL]. [2021-06-10]. https://arxiv.org/abs/2106.02466.
|
13 |
PEROZZI B, AL-RFOU R, SKIENA S. Deepwalk: online learning of social representations [C]// Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. NewYork: ACM, 2014: 701-710.
|
14 |
GROVER A, LESKOVEC J. Node2vec: scalable feature learning for networks [C]// Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco: ACM, 2016: 855-864.
|
15 |
GRILL J B, ALTCHE F, TALLEC C, et al. Bootstrap Your Own Latent: a new approach to self-supervised learning [C]// Advances in Neural Information Proceedings Systems. [s.l.]: MIT Press, 2020: 1-35.
|
16 |
HJELM R, FEDOROV A, LAVOIE-MARCHILDON S, et al. Learning deep representations by mutual information estimation and maximization [C]// Proceedings of the 7th International Conference on Learning Representations. New Orleans: [s.n.], 2019: 1-24.
|
17 |
VELICKOVIC P, CUCURULL G, CASANOVA A, et al. Graph attention networks [C]// Proceedings of the 6th International Conference on Learning Representations. Vancouver: [s.n.], 2018: 1-12.
|
18 |
TSAI Y H, BAI S J, MORENCY L P, et al. A note on connecting Barlow Twins with negative-sample-free contrastive learning [EB/OL]. [2021-05-04]. https://arxiv.org/abs/2104.13712.
|
19 |
HAMILTON W L, YING Z, LESKOVEC J. Inductive representation learning on large graphs [C]// Advances in Neural Information Processing Systems. Long Beach: MIT Press, 2017: 1024-1034.
|
20 |
PENG Z, HUANG W, LUO M, et al. Graph representation learning via graphical mutual information maximization [C]// Proceedings of the 2020 World Wide Web Conference. Taipei: ACM, 2020: 259-270.
|
21 |
WAN S, PAN S, YANG J, et al. Contrastive and generative graph convolutional networks for graph-based semi-supervised learning [C]// Proceedings of the AAAI Conference on Artificial Intelligence. [s.l.]: AAAI, 2021: 10049-10057.
|
22 |
孙学全, 冯英浚 多层感知器的灵敏度分析[J]. 计算机学报, 2001, 24 (9): 951- 958 SUN Xue-quan, FENG Ying-jun Sensitivity analysis of multilayer perception[J]. Chinese Journal of Computers, 2001, 24 (9): 951- 958
doi: 10.3321/j.issn:0254-4164.2001.09.009
|
23 |
徐冰冰, 岑科廷, 黄俊杰, 等 图卷积神经网络综述[J]. 计算机学报, 2020, 43 (5): 755- 780 XU Bing-bing, CEN Ke-ting, HUANG Jun-jie, et al A survey on graph convolutional neural network[J]. Chinese Journal of Computers, 2020, 43 (5): 755- 780
doi: 10.11897/SP.J.1016.2020.00755
|
24 |
YANG Z W, COHEN W, SALAKHUTDINOV R. Revisiting semi-supervised learning with graph embeddings [C]// Proceedings of 33nd International Conference on Machine Learning. New York: [s.n.], 2016: 40-48.
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