HiWalk: Learning node embeddings from heterogeneous networks

doi:10.1016/j.is.2018.11.008

	HiWalk: Learning node embeddings from heterogeneous networks
	Bai, Jie1,2 ; Li, Linjing1 ; Zeng, Daniel1,3
刊名	INFORMATION SYSTEMS
	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
DOI	10.1016/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.
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关键词	FRAMEWORK
WOS研究方向	Computer Science
语种	英语
出版者	PERGAMON-ELSEVIER SCIENCE LTD
WOS记录号	WOS:000459839400005
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
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/25000]
专题	中国科学院自动化研究所
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.