Knowledge Commons of Institute of Automation,CAS
Multi-aspect self-supervised learning for heterogeneous information network | |
Che, Feihu1,2; Tao, Jianhua1,2,3; Yang, Guohua1; Liu, Tong1; Zhang, Dawei1 | |
发表期刊 | KNOWLEDGE-BASED SYSTEMS |
ISSN | 0950-7051 |
2021-12-05 | |
卷号 | 233页码:14 |
摘要 | Graph neural networks (GNNs) have made remarkable advancements in processing graph-structured data with all nodes and edges belonging to the same type. However, various types of node and relations exist in heterogeneous information networks (HINs), and due to this, HINs contain rich structural and semantic information. To tackle this heterogeneity, existing methods usually apply several well-designed metapaths to HINs to obtain the corresponding homogeneous subgraphs. However, these methods either fail to capture the interconnections between the same nodes in different subgraphs or require qualified labels. To address these issues, we propose a new multi-aspect self-supervised learning (SSL) framework for HIN representation in an unsupervised manner: (1) we design a new contrastive learning model to capture the similarities between the same nodes in different homogeneous subgraphs, and (2) we maximize the mutual information between the local patches and the global representation in one subgraph. Extensive experiments on various downstream tasks demonstrate the superiority of our model in comparison to the existing state-of-the-art methods. (c) 2021 Elsevier B.V. All rights reserved. |
关键词 | Heterogeneous information network Self-supervised Contrastive learning Graph neural network |
DOI | 10.1016/j.knosys.2021.107474 |
收录类别 | SCI |
语种 | 英语 |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Artificial Intelligence |
WOS记录号 | WOS:000709919000012 |
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/173211/46304 |
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,Tao, Jianhua,Yang, Guohua,et al. Multi-aspect self-supervised learning for heterogeneous information network[J]. KNOWLEDGE-BASED SYSTEMS,2021,233:14. |
APA | Che, Feihu,Tao, Jianhua,Yang, Guohua,Liu, Tong,&Zhang, Dawei.(2021).Multi-aspect self-supervised learning for heterogeneous information network.KNOWLEDGE-BASED SYSTEMS,233,14. |
MLA | Che, Feihu,et al."Multi-aspect self-supervised learning for heterogeneous information network".KNOWLEDGE-BASED SYSTEMS 233(2021):14. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Multi-aspect self-su(2661KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论