DyTSCL: Dynamic graph representation via tempo-structural contrastive learning

doi:10.1016/j.neucom.2023.126660

	DyTSCL: Dynamic graph representation via tempo-structural contrastive learning
	Li, Jianian 1; Bao, Peng 1; Yan, Rong 1; Shen, Huawei 2
	2023-11-01
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
卷号	556 页码:8
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.
关键词	Graph representation learning Contrastive learning Dynamic graph Tempo-structural information
DOI	10.1016/j.neucom.2023.126660
收录类别	SCI
语种	英语
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
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:001068763700001
出版者	ELSEVIER
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/21144
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.