Maximum Co-located Community Search in Large Scale Social Networks

Lu Chen, Chengfei Liu, Rui Zhou, Jianxin Li, Xiaochun Yang, Bin Wang

Research output: Contribution to journalConference articlepeer-review

48 Citations (Scopus)


The problem of k-truss search has been well defined and investigated to find the highly correlated user groups in social networks. But there is no previous study to consider
the constraint of users’ spatial information in k-truss search, denoted as co-located community search in this paper. The co-located community can serve many real applications. To search the maximum co-located communities efficiently, we first develop an efficient exact algorithm with several pruning techniques. After that, we further develop an approximation algorithm with adjustable accuracy guarantees and explore more effective pruning rules, which can reduce the computational cost significantly. To accelerate the real-time efficiency, we also devise a novel quadtree based index to support the efficient retrieval of users in a region and optimise
the search regions with regards to the given query
region. Finally, we verify the performance of our proposed
algorithms and index using five real datasets
Original languageEnglish
Pages (from-to)1233-1246
Number of pages14
JournalProceedings of the VLDB Endowment
Issue number10
Publication statusPublished - Jun 2018
Event44th International Conference on Very Large Data Bases 2018 - Rio De Janeiro, Brazil
Duration: 27 Aug 201831 Aug 2018


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