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Self-supervised graph representation learning via bootstrapping | |
Che, Feihu1,2; Yang, Guohua1; Zhang, Dawei1; Tao, Jianhua1,2,3; Liu, Tong1 | |
发表期刊 | NEUROCOMPUTING |
ISSN | 0925-2312 |
2021-10-07 | |
卷号 | 456页码:88-96 |
摘要 | Graph neural networks (GNNs) apply deep learning techniques to graph-structured data and have achieved promising performance in graph representation learning. However, existing GNNs rely heavily on labeled data or well-designed negative samples. To address these issues, we propose a new self-supervised graph representation method: deep graph bootstrapping (DGB). DGB consists of two neural networks: online and target networks, and the input of them are different augmented views of the initial graph. The online network is trained to predict the target network while the target network is updated with a slow-moving average of the online network, which means the online and target networks can learn from each other. As a result, the proposed DGB can learn graph representation without negative examples in an unsupervised manner. In addition, we summarize three kinds of augmentation methods for graph-structured data and apply them to the DGB. Experiments on the benchmark datasets show the DGB performs better than the current state-of-the-art methods and how the augmentation methods affect the performances. (c) 2021 Elsevier B.V. All rights reserved. |
关键词 | Graph representation learning Self-supervised Bootstrapping Graph neural network |
DOI | 10.1016/j.neucom.2021.03.123 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key Research and Develop ment Plan of China[2018YFB1005003] ; National Natural Science Foundation of China (NSFC)[61831022] ; National Natural Science Foundation of China (NSFC)[61901473] ; National Natural Science Foundation of China (NSFC)[61771472] ; National Natural Science Foundation of China (NSFC)[61773379] |
项目资助者 | National Key Research and Develop ment Plan of China ; National Natural Science Foundation of China (NSFC) |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Artificial Intelligence |
WOS记录号 | WOS:000687471900008 |
出版者 | ELSEVIER |
七大方向——子方向分类 | 知识表示与推理 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/45912 |
专题 | 多模态人工智能系统全国重点实验室_智能交互 |
通讯作者 | Tao, Jianhua |
作者单位 | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 3.CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing, Peoples R China |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Che, Feihu,Yang, Guohua,Zhang, Dawei,et al. Self-supervised graph representation learning via bootstrapping[J]. NEUROCOMPUTING,2021,456:88-96. |
APA | Che, Feihu,Yang, Guohua,Zhang, Dawei,Tao, Jianhua,&Liu, Tong.(2021).Self-supervised graph representation learning via bootstrapping.NEUROCOMPUTING,456,88-96. |
MLA | Che, Feihu,et al."Self-supervised graph representation learning via bootstrapping".NEUROCOMPUTING 456(2021):88-96. |
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