Clustering Enhanced Multiplex Graph Contrastive Representation Learning

doi:10.1109/TNNLS.2023.3334751

	Clustering Enhanced Multiplex Graph Contrastive Representation Learning
	Yuan, Ruiwen 1,2; Tang, Yongqiang2 ; Wu, Yajing2 ; Zhang, Wensheng2,3
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN	2162-237X
	2023-11-28
页码	15
通讯作者	Tang, Yongqiang(yongqiang.tang@ia.ac.cn)
摘要	Multiplex graph representation learning has attracted considerable attention due to its powerful capacity to depict multiple relation types between nodes. Previous methods generally learn representations of each relation-based subgraph and then aggregate them into final representations. Despite the enormous success, they commonly encounter two challenges: 1) the latent community structure is overlooked and 2) consistent and complementary information across relation types remains largely unexplored. To address these issues, we propose a clustering-enhanced multiplex graph contrastive representation learning model (CEMR). In CEMR, by formulating each relation type as a view, we propose a multiview graph clustering framework to discover the potential community structure, which promotes representations to incorporate global semantic correlations. Moreover, under the proposed multiview clustering framework, we develop cross-view contrastive learning and cross-view cosupervision modules to explore consistent and complementary information in different views, respectively. Specifically, the cross-view contrastive learning module equipped with a novel negative pairs selecting mechanism enables the view-specific representations to extract common knowledge across views. The cross-view cosupervision module exploits the high-confidence complementary information in one view to guide low-confidence clustering in other views by contrastive learning. Comprehensive experiments on four datasets confirm the superiority of our CEMR when compared to the state-of-the-art rivals.
关键词	Contrastive learning graph representation learning multiplex graph multiview graph clustering
DOI	10.1109/TNNLS.2023.3334751
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China
项目资助者	National Key Research and Development Program of China
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:001167316400001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/55678
专题	多模态人工智能系统全国重点实验室
通讯作者	Tang, Yongqiang
作者单位	1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 101408, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China 3.Guangzhou Univ, Sch Comp Sci & Cyber Engn, Guangzhou, Peoples R China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Yuan, Ruiwen,Tang, Yongqiang,Wu, Yajing,et al. Clustering Enhanced Multiplex Graph Contrastive Representation Learning[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2023:15.
APA	Yuan, Ruiwen,Tang, Yongqiang,Wu, Yajing,&Zhang, Wensheng.(2023).Clustering Enhanced Multiplex Graph Contrastive Representation Learning.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,15.
MLA	Yuan, Ruiwen,et al."Clustering Enhanced Multiplex Graph Contrastive Representation Learning".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2023):15.