NEAWalk: Inferring missing social interactions via topological-temporal embeddings of social groups

doi:10.1007/s10115-022-01724-2

	NEAWalk: Inferring missing social interactions via topological-temporal embeddings of social groups
	Shen, Yinghan 1,3; Jiang, Xuhui 1,3; Li, Zijian 1,3; Wang, Yuanzhuo 1,4; Jin, Xiaolong 2,3; Ma, Shengjie 5; Cheng, Xueqi 2,3
	2022-08-23
发表期刊	KNOWLEDGE AND INFORMATION SYSTEMS
ISSN	0219-1377
页码	25
摘要	Real-world network data consisting of social interactions can be incomplete due to deliberately erased or unsuccessful data collection, which cause the misleading of social interaction analysis for many various time-aware applications. Naturally, the link prediction task has drawn much research interest to predict the missing edges in the incomplete social network. However, existing studies of link prediction cannot effectively capture the entangling topological and temporal dynamics already residing in the social network, thus cannot effectively reasoning the missing interactions in dynamic networks. In this paper, we propose the NEAWalk, a novel model to infer the missing social interaction based on topological-temporal features of patterns in the social group. NEAWalk samples the query-relevant walks containing both the historical and evolving information by focusing on the temporal constraint and designs a dual-view anonymization procedure for extracting both topological and temporal features from the collected walks to conduct the inference. Two-track experiments on several well-known network datasets demonstrate that the NEAWalk stably achieves superior performance against several state-of-the-art baseline methods.
关键词	Dynamic network completion Dynamic graph representation learning Social group Anonymous walk
DOI	10.1007/s10115-022-01724-2
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[U1836206] ; National Natural Science Foundation of China[91646120] ; National Natural Science Foundation of China[U21B2046] ; National Natural Science Foundation of China[62172393] ; National Key Research and Development Program of China[2018YTFB1402601] ; Zhongyuanyingcai program[204200510002] ; Major Public Welfare Project of Henan Province[201300311200]
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems
WOS记录号	WOS:000843458800002
出版者	SPRINGER LONDON LTD
引用统计	被引频次：1[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/2XEOYT63/19457
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Wang, Yuanzhuo
作者单位	1.Chinese Acad Sci, Data Intelligence Syst Res Ctr, Inst Comp Technol, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Key Lab Network Data & Sci & Technol, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Beijing, Peoples R China 4.Zhongke Big Data Acad, Zhengzhou, Peoples R China 5.Renmin Univ China, Gaoling Sch Artificial Intelligence, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Shen, Yinghan,Jiang, Xuhui,Li, Zijian,et al. NEAWalk: Inferring missing social interactions via topological-temporal embeddings of social groups[J]. KNOWLEDGE AND INFORMATION SYSTEMS,2022:25.
APA	Shen, Yinghan.,Jiang, Xuhui.,Li, Zijian.,Wang, Yuanzhuo.,Jin, Xiaolong.,...&Cheng, Xueqi.(2022).NEAWalk: Inferring missing social interactions via topological-temporal embeddings of social groups.KNOWLEDGE AND INFORMATION SYSTEMS,25.
MLA	Shen, Yinghan,et al."NEAWalk: Inferring missing social interactions via topological-temporal embeddings of social groups".KNOWLEDGE AND INFORMATION SYSTEMS (2022):25.