Geo-Socially Tenuous Group Query

In location-based social networks, it is important to find a specific group/community. Current research has focused on finding dense subgraphs of close relationships between groups. Compared with the dense group/subgraph, there are few studies on tenuous groups. Although the existing work has begun...

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Autor principal:	LI Na, ZHU Huaijie, LIU Wei, YIN Jian
Formato:	article
Lenguaje:	ZH
Publicado:	Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press 2021
Materias:	location-based social network graph tenuous group c-neighbor distance pruning Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/fed3fd3269c644e28956188d4da9f069
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id	oai:doaj.org-article:fed3fd3269c644e28956188d4da9f069
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spelling	oai:doaj.org-article:fed3fd3269c644e28956188d4da9f0692021-11-10T08:18:35ZGeo-Socially Tenuous Group Query10.3778/j.issn.1673-9418.20080991673-9418https://doaj.org/article/fed3fd3269c644e28956188d4da9f0692021-11-01T00:00:00Zhttp://fcst.ceaj.org/CN/abstract/abstract2954.shtmlhttps://doaj.org/toc/1673-9418In location-based social networks, it is important to find a specific group/community. Current research has focused on finding dense subgraphs of close relationships between groups. Compared with the dense group/subgraph, there are few studies on tenuous groups. Although the existing work has begun to study the tenuous population query, geo-socially tenuous group query has not been studied, and location-based services have a lot of demands in real life. Therefore, it becomes valuable to study the geo-socially tenuous group query. Geo-socially tenuous group query is to find a group of users, which not only satisfies a certain sparsity between users (i.e. the social distance between users is greater than [k]), but also minimizes the distance between users and the query location. To address this problem, this paper first proposes a basic processing algorithm based on c-neighbor (baseline), which uses stored c-neighbor information and distance pruning to help obtain query results quickly. However, the basic processing algorithm based on c-neighbor (baseline) uses too much space and the query efficiency is not high when parameter [k>c]. To solve these problems, a query optimization algorithm based on c-neighbor and reverse c-neighbor (ICN) is proposed, which not only utilizes stored c-neighbor information but also reverse c-neighbor information to effectively filter out invalid users and obtain query results quickly. The experimental results and theory show that the proposed two query processing methods are effective and correct.LI Na, ZHU Huaijie, LIU Wei, YIN JianJournal of Computer Engineering and Applications Beijing Co., Ltd., Science Pressarticlelocation-based social network graphtenuous groupc-neighbordistance pruningElectronic computers. Computer scienceQA75.5-76.95ZHJisuanji kexue yu tansuo, Vol 15, Iss 11, Pp 2151-2160 (2021)
institution	DOAJ
collection	DOAJ
language	ZH
topic	location-based social network graph tenuous group c-neighbor distance pruning Electronic computers. Computer science QA75.5-76.95
spellingShingle	location-based social network graph tenuous group c-neighbor distance pruning Electronic computers. Computer science QA75.5-76.95 LI Na, ZHU Huaijie, LIU Wei, YIN Jian Geo-Socially Tenuous Group Query
description	In location-based social networks, it is important to find a specific group/community. Current research has focused on finding dense subgraphs of close relationships between groups. Compared with the dense group/subgraph, there are few studies on tenuous groups. Although the existing work has begun to study the tenuous population query, geo-socially tenuous group query has not been studied, and location-based services have a lot of demands in real life. Therefore, it becomes valuable to study the geo-socially tenuous group query. Geo-socially tenuous group query is to find a group of users, which not only satisfies a certain sparsity between users (i.e. the social distance between users is greater than [k]), but also minimizes the distance between users and the query location. To address this problem, this paper first proposes a basic processing algorithm based on c-neighbor (baseline), which uses stored c-neighbor information and distance pruning to help obtain query results quickly. However, the basic processing algorithm based on c-neighbor (baseline) uses too much space and the query efficiency is not high when parameter [k>c]. To solve these problems, a query optimization algorithm based on c-neighbor and reverse c-neighbor (ICN) is proposed, which not only utilizes stored c-neighbor information but also reverse c-neighbor information to effectively filter out invalid users and obtain query results quickly. The experimental results and theory show that the proposed two query processing methods are effective and correct.
format	article
author	LI Na, ZHU Huaijie, LIU Wei, YIN Jian
author_facet	LI Na, ZHU Huaijie, LIU Wei, YIN Jian
author_sort	LI Na, ZHU Huaijie, LIU Wei, YIN Jian
title	Geo-Socially Tenuous Group Query
title_short	Geo-Socially Tenuous Group Query
title_full	Geo-Socially Tenuous Group Query
title_fullStr	Geo-Socially Tenuous Group Query
title_full_unstemmed	Geo-Socially Tenuous Group Query
title_sort	geo-socially tenuous group query
publisher	Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
publishDate	2021
url	https://doaj.org/article/fed3fd3269c644e28956188d4da9f069
work_keys_str_mv	AT linazhuhuaijieliuweiyinjian geosociallytenuousgroupquery
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Geo-Socially Tenuous Group Query

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