Maximum Co-located Community Search in Large Scale Social Networks

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

doi:10.14778/3231751.3231755

Maximum Co-located Community Search in Large Scale Social Networks

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

School of Physics, Mathematics and Computing

Research output: Contribution to journal › Conference article

10 Citations (Scopus)

Abstract

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 language	English
Pages (from-to)	1233-1246
Number of pages	14
Journal	Proceedings of the VLDB Endowment
Volume	11
Issue number	10
DOIs	https://doi.org/10.14778/3231751.3231755
Publication status	Published - Jun 2018
Event	44th International Conference on Very Large Data Bases 2018 - Rio De Janeiro, Brazil Duration: 27 Aug 2018 → 31 Aug 2018 http://vldb2018.lncc.br/

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@article{725a33776a974e70a506ed0bac36cf6c,

title = "Maximum Co-located Community Search in Large Scale Social Networks",

abstract = "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 considerthe constraint of users{\textquoteright} 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 optimisethe search regions with regards to the given queryregion. Finally, we verify the performance of our proposedalgorithms and index using five real datasets",

author = "Lu Chen and Chengfei Liu and Rui Zhou and Jianxin Li and Xiaochun Yang and Bin Wang",

year = "2018",

month = jun,

doi = "10.14778/3231751.3231755",

language = "English",

volume = "11",

pages = "1233--1246",

journal = "PVLDB",

issn = "2150-8097",

publisher = "Very Large Data Base Endowment Inc.",

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note = "44th International Conference on Very Large Data Bases 2018 ; Conference date: 27-08-2018 Through 31-08-2018",

url = "http://vldb2018.lncc.br/",

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TY - JOUR

T1 - Maximum Co-located Community Search in Large Scale Social Networks

AU - Chen, Lu

AU - Liu, Chengfei

AU - Zhou, Rui

AU - Li, Jianxin

AU - Yang, Xiaochun

AU - Wang, Bin

PY - 2018/6

Y1 - 2018/6

N2 - 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 considerthe 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 optimisethe search regions with regards to the given queryregion. Finally, we verify the performance of our proposedalgorithms and index using five real datasets

AB - 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 considerthe 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 optimisethe search regions with regards to the given queryregion. Finally, we verify the performance of our proposedalgorithms and index using five real datasets

U2 - 10.14778/3231751.3231755

DO - 10.14778/3231751.3231755

M3 - Conference article

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EP - 1246

JO - PVLDB

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SN - 2150-8097

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