中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 自动化研究所
  3. 中国科学院自动化研究所
  4. 多模态人工智能系统全国重点实验室
  5. 互联网大数据与安全信息学研究中心
HiWalk: Learning node embeddings from heterogeneous networks

文献类型：期刊论文

作者Bai, Jie1,2; Li, Linjing1; Zeng, Daniel1,3
刊名INFORMATION SYSTEMS
出版日期2019-03-01
卷号81页码:82-91
关键词Network analysis Representation learning Behavioral analysis Random walk Heterogeneous network
ISSN号0306-4379
DOI10.1016/j.is.2018.11.008
通讯作者Bai, Jie(baijie2013@ia.ac.cn) ; Li, Linjing(linjing.li@ia.ac.cn)
英文摘要Heterogeneous networks, such as bibliographical networks and online business networks, are ubiquitous in everyday life. Nevertheless, analyzing them for high-level semantic understanding still poses a great challenge for modern information systems. In this paper, we propose HiWalk to learn distributed vector representations of the nodes in heterogeneous networks. HiWalk is inspired by the state-of-the-art representation learning algorithms employed in the context of both homogeneous networks and heterogeneous networks, based on word embedding learning models. Different from existing methods in the literature, the purpose of HiWalk is to learn vector representations of the targeted set of nodes by leveraging the other nodes as "background knowledge", which maximizes the structural correlations of contiguous nodes. HiWalk decomposes the adjacent probabilities of the nodes and adopts a hierarchical random walk strategy, which makes it more effective, efficient and concentrated when applied to practical large-scale heterogeneous networks. HiWalk can be widely applied in heterogeneous networks environments to analyze targeted types of nodes. We further validate the effectiveness of the proposed HiWalk through multiple tasks conducted on two real-world datasets. (C) 2018 Elsevier Ltd. All rights reserved.
WOS关键词FRAMEWORK
资助项目National Key R&D Program of China[2016QY02D0305] ; National Natural Science Foundation of China[71621002] ; National Natural Science Foundation of China[71602184] ; National Natural Science Foundation of China[71202169] ; National Natural Science Foundation of China[61671450] ; National Natural Science Foundation of China[U1435221] ; Key Research Program of the Chinese Academy of Sciences[ZDRW-XH-2017-3]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000459839400005
出版者PERGAMON-ELSEVIER SCIENCE LTD
资助机构National Key R&D Program of China ; National Natural Science Foundation of China ; Key Research Program of the Chinese Academy of Sciences
源URL[http://ir.ia.ac.cn/handle/173211/25000]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_互联网大数据与安全信息学研究中心
通讯作者Bai, Jie; Li, Linjing
作者单位1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 100049, Peoples R China
3.Univ Arizona, Dept Management Informat Syst, Tucson, AZ 85721 USA
推荐引用方式
GB/T 7714		 Bai, Jie,Li, Linjing,Zeng, Daniel. HiWalk: Learning node embeddings from heterogeneous networks[J]. INFORMATION SYSTEMS,2019,81:82-91.
APA Bai, Jie,Li, Linjing,&Zeng, Daniel.(2019).HiWalk: Learning node embeddings from heterogeneous networks.INFORMATION SYSTEMS,81,82-91.
MLA Bai, Jie,et al."HiWalk: Learning node embeddings from heterogeneous networks".INFORMATION SYSTEMS 81(2019):82-91.

入库方式： OAI收割

来源：自动化研究所

浏览0
下载0
收藏0
其他版本

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。