南方科技大学知识苑(SUSTech KC): HMGCL: Heterogeneous multigraph contrastive learning for LBSN friend recommendation

题名	HMGCL: Heterogeneous multigraph contrastive learning for LBSN friend recommendation
作者	Li, Yongkang1,2,3 ; Fan, Zipei 1,4; Yin, Du1,2,3 ; Jiang, Renhe 1,4; Deng, Jinliang1,5 ; Song, Xuan1,2,3
通讯作者	Fan, Zipei; Song, Xuan
发表日期	2022-10-01
DOI	10.1007/s11280-022-01092-5
发表期刊	WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS 影响因子和分区
ISSN	1386-145X
EISSN	1573-1413
卷号	26 期号:4页码:1625-1648
摘要	Friend recommendation from user trajectory is a vital real-world application of location-based social networks (LBSN) services. Previous statistical analysis indicated that social network relationships could explain 10% to 30% of human movement, especially long-distance travel. Therefore, it is necessary to recognize patterns from human mobility to assist the friend recommendation. However, previous works either modelled friendships and check-in records by simple graphs with only one connection between any two nodes or ignored a large amount of vital spatio-temporal information and semantic information in raw LBSN data. To overcome the limitation of the simple graph commonly seen in previous works, we leverage heterogeneous multigraph to model LBSN data and define various semantic connections between nodes. Against this background, we propose a Heterogeneous Multigraph Contrastive Learning (HMGCL) model to capture spatio-temporal characteristics of human trajectories for user node embedding learning. Extensive experiments show that our method outperforms the state-of-the-art approaches in six real-world city datasets.
关键词	Heterogeneou multigraph Location-based social networks Contrastive learning Friend recommendation
相关链接	[来源记录]
收录类别	SCI ; EI
语种	英语
学校署名	第一 ; 通讯
资助项目	Japan's Ministry of Education, Culture, Sports, Science, and Technology (MEXT)[22H03573] ; National Key Research and Development Project of China[2021YFB1714400] ; Guangdong Provincial Key Laboratory[2020B121201001]
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering
WOS记录号	WOS:000864567300001
出版者	SPRINGER
EI入藏号	20224112889937
EI主题词	Directed graphs ; Learning systems
EI分类号	Combinatorial Mathematics, Includes Graph Theory, Set Theory:921.4
ESI学科分类	COMPUTER SCIENCE
来源库	Web of Science
引用统计	被引频次[WOS]：8
成果类型	期刊论文
条目标识符	http://sustech.caswiz.com/handle/2SGJ60CL/405961
专题	工学院_计算机科学与工程系
作者单位	1.Southern Univ Sci & Technol, SUSTech UTokyo Joint Res Ctr Super Smart City, Dept Comp Sci & Engn, Shenzhen, Peoples R China 2.Southern Univ Sci & Technol, Dept Comp Sci & Engn, Guangdong Prov Key Lab Brain Inspired Intelligent, Shenzhen, Peoples R China 3.Southern Univ Sci & Technol, Res Inst Trustworthy Autonomous Syst, Shenzhen, Peoples R China 4.Univ Tokyo, Ctr Spatial Informat Sci, Tokyo, Japan 5.Univ Technol Sydney, Australian Artificial Intelligence Inst, Sydney, NSW, Australia
第一作者单位	计算机科学与工程系; 南方科技大学
通讯作者单位	计算机科学与工程系; 南方科技大学
第一作者的第一单位	计算机科学与工程系
推荐引用方式 GB/T 7714	Li, Yongkang,Fan, Zipei,Yin, Du,et al. HMGCL: Heterogeneous multigraph contrastive learning for LBSN friend recommendation[J]. WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS,2022,26(4):1625-1648.
APA	Li, Yongkang,Fan, Zipei,Yin, Du,Jiang, Renhe,Deng, Jinliang,&Song, Xuan.(2022).HMGCL: Heterogeneous multigraph contrastive learning for LBSN friend recommendation.WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS,26(4),1625-1648.
MLA	Li, Yongkang,et al."HMGCL: Heterogeneous multigraph contrastive learning for LBSN friend recommendation".WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS 26.4(2022):1625-1648.