Clustering Enhanced Multiplex Graph Contrastive Representation Learning
文献类型：期刊论文
| 作者 | Yuan, Ruiwen2,3; Tang, Yongqiang2 ; Wu, Yajing2; Zhang, Wensheng1,2
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| 刊名 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
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| 出版日期 | 2023-11-28 |
| 页码 | 15 |
| 关键词 | Contrastive learning graph representation learning multiplex graph multiview graph clustering |
| ISSN号 | 2162-237X |
| DOI | 10.1109/TNNLS.2023.3334751 |
| 通讯作者 | Tang, Yongqiang(yongqiang.tang@ia.ac.cn) |
| 英文摘要 | 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. |
| 资助项目 | National Key Research and Development Program of China |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001167316400001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Key Research and Development Program of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/55677]  |
| 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.Guangzhou Univ, Sch Comp Sci & Cyber Engn, Guangzhou, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 101408, Peoples R China |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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