Continuous Geo-Social Group Monitoring in Dynamic LBSNs

Huaijie Zhu, Wei Liu*, Jian Yin, Libin Zheng, Xin Huang, Jianliang Xu*, Wang-Chien Lee

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review


Geo-social group queries, which return a social cohesive user group with a spatial constraint, have receive significant research interests due to their promising applications for group-based activity planning and scheduling in location-based social networks (LBSNs). However, existing studies on geo-social group queries mostly assume the users are stationary whereas in realistic LBSN application scenarios all users may continuously move over time. Thus, in this paper, we investigate the problem of continuous geo-social groups monitoring ( CGSGM ) over moving users. A challenge in answering CGSGM queries over moving users is how to efficiently update geo-social groups when users are continuously moving. To address the CGSGM problem, we first propose a baseline algorithm, namely Baseline-BB , which recomputes the new geo-social groups from scratch at each time instance by utilizing a branch and bound (BB) strategy. To improve the inefficiency of BB, we explore a new strategy, called common neighbor or neighbor expanding (CNNE), which expands the common neighbors of edges or the neighbors of users in intermediate groups to quickly produce the valid group combinations. Accordingly, another baseline algorithm, namely Baseline-CNNE , is proposed. As these baseline algorithms do not maintain intermediate results to facilitate further query processing, we develop an incremental algorithm, called incremental monitoring algorithm (IMA) , which maintains the support, common neighbors and the neighbors of current users when exploring possible user groups for further updates and query processing. Since IMA requires many times of truss decomposition when processing mutiple-users updates, we propose an improved incremental algorithm, called improved incremental monitoring algorithm (IIMA) , which performs truss decompostion only once. Moreover, we design algorithms for handling the social changes that result in insertion/deletion of some edges in the social network. Owing to the challenge in setting, an appropriate monitoring distance, we further study the top $N$ CGSGM problem, which finds top $N$ result groups at each time instance. Finally, we conduct extensive experiments using four real datasets to validate our ideas and evaluate the proposed algorithms.
Original languageEnglish
Pages (from-to)7815-7828
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number8
Early online date2 Nov 2022
Publication statusPublished - 1 Aug 2023

Scopus Subject Areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

User-Defined Keywords

  • Continuous queries
  • geo-social group query
  • location-based services
  • monitoring
  • nearest neighbor


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