User Identity Linkage across Social Networks with the Enhancement of Knowledge Graph and Time Decay Function

doi:10.3390/e24111603

	User Identity Linkage across Social Networks with the Enhancement of Knowledge Graph and Time Decay Function
	Gao, Hao 1,2,4; Wang, Yongqing 1; Shao, Jiangli 1,2,4; Shen, Huawei 1,2,4; Cheng, Xueqi 2,3
	2022-11-01
发表期刊	ENTROPY
卷号	24 期号:11 页码:22
摘要	Users participate in multiple social networks for different services. User identity linkage aims to predict whether users across different social networks refer to the same person, and it has received significant attention for downstream tasks such as recommendation and user profiling. Recently, researchers proposed measuring the relevance of user-generated content to predict identity linkages of users. However, there are two challenging problems with existing content-based methods: first, barely considering the word similarities of texts is insufficient where the semantical correlations of named entities in the texts are ignored; second, most methods use time discretization technology, where the texts are divided into different time slices, resulting in failure of relevance modeling. To address these issues, we propose a user identity linkage model with the enhancement of a knowledge graph and continuous time decay functions that are designed for mitigating the influence of time discretization. Apart from modeling the correlations of the words, we extract the named entities in the texts and link them into the knowledge graph to capture the correlations of named entities. The semantics of texts are enhanced through the external knowledge of the named entities in the knowledge graph, and the similarity discrimination of the texts is also improved. Furthermore, we propose continuous time decay functions to capture the closeness of the posting time of texts instead of time discretization to avoid the matching error of texts. We conduct experiments on two real public datasets, and the experimental results show that the proposed method outperforms state-of-the-art methods.
关键词	user identity linkage knowledge graph named entity time decay function text matching
DOI	10.3390/e24111603
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61802371] ; National Natural Science Foundation of China[91746301] ; National Natural Science Foundation of China[U1836111] ; National Key Research and Development Program of China[2018YFC0825200] ; National Social Science Fund of China[19ZDA329]
WOS研究方向	Physics
WOS类目	Physics, Multidisciplinary
WOS记录号	WOS:000883467800001
出版者	MDPI
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/19895
专题	中国科学院计算技术研究所期刊论文
通讯作者	Gao, Hao
作者单位	1.Chinese Acad Sci, Data Intelligence Syst Res Ctr, Inst Comp Technol, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, CAS Key Lab Network Data Sci & Technol, Beijing 100190, Peoples R China 4.Kexueyuannanlu 6, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Gao, Hao,Wang, Yongqing,Shao, Jiangli,et al. User Identity Linkage across Social Networks with the Enhancement of Knowledge Graph and Time Decay Function[J]. ENTROPY,2022,24(11):22.
APA	Gao, Hao,Wang, Yongqing,Shao, Jiangli,Shen, Huawei,&Cheng, Xueqi.(2022).User Identity Linkage across Social Networks with the Enhancement of Knowledge Graph and Time Decay Function.ENTROPY,24(11),22.
MLA	Gao, Hao,et al."User Identity Linkage across Social Networks with the Enhancement of Knowledge Graph and Time Decay Function".ENTROPY 24.11(2022):22.