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DyTSCL: Dynamic graph representation via tempo-structural contrastive learning

文献类型：期刊论文

作者Li, Jianian1; Bao, Peng1; Yan, Rong1; Shen, Huawei2
刊名NEUROCOMPUTING
出版日期2023-11-01
卷号556页码:8
关键词Graph representation learning Contrastive learning Dynamic graph Tempo-structural information
ISSN号0925-2312
DOI10.1016/j.neucom.2023.126660
英文摘要With the massive growth of graph-structured data, extensive research has focused on graph representation learning. Recently, graph representation learning frameworks have made great efforts toward dynamic graph learning. Although dynamic graph methods have achieved impressive results, they require labeled data for model training. The contrastive learning does not require human annotation to complete model training and has been shown to be extremely competitive in visual representation learning and natural language processing. In this paper, we propose a novel Dynamic graph representation framework via Tempo-Structural Contrastive Learning, DyTSCL, which trains the model by identifying three different subgraphs as a task, named Tempo-Structural subgraph, Non-Temporal subgraph and Non-Structural subgraph. Moreover, we propose a Tempo-Structural encoder, which aggregates the temporal and structural information. Finally, a TempoStructural contrastive learning module is proposed to maximize the consistency between node and subgraph in temporal and structural perspectives, respectively. To demonstrate the effectiveness of DyTSCL, we validate DyTSCL by applying it on the Wikipedia, Reddit and Mooc datasets, which show that DyTSCL can significantly outperform the existing approaches.
资助项目Fundamental Research Funds for the Central Universities, China[62272032] ; National Natural Science Foundation of China[U21B2046] ; CCF-Tencent Open Research Fund, China, CAAI- Huawei MindSpore Open Fund, China ; CCF-NSFOCUS Kunpeng Fund, China ; [2022JBMC001]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:001068763700001
出版者ELSEVIER
源URL[http://119.78.100.204/handle/2XEOYT63/21143]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Bao, Peng
作者单位1.Beijing Jiaotong Univ, Sch Software Engn, Beijing 100081, Peoples R China
2.Chinese Acad Sci, Data Intelligence Syst Res Ctr, Inst Comp Technol, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Li, Jianian,Bao, Peng,Yan, Rong,et al. DyTSCL: Dynamic graph representation via tempo-structural contrastive learning[J]. NEUROCOMPUTING,2023,556:8.
APA Li, Jianian,Bao, Peng,Yan, Rong,&Shen, Huawei.(2023).DyTSCL: Dynamic graph representation via tempo-structural contrastive learning.NEUROCOMPUTING,556,8.
MLA Li, Jianian,et al."DyTSCL: Dynamic graph representation via tempo-structural contrastive learning".NEUROCOMPUTING 556(2023):8.

入库方式： OAI收割

来源：计算技术研究所

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