A survey of community search over big graphs.

Fang, Y; Huang, X; Qin, L; Zhang, Y; Zhang, W; Cheng, R; Lin, X

A survey of community search over big graphs.

Fang, Y Huang, X Qin, L

Zhang, Y Zhang, W Cheng, R Lin, X

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Publisher:: VLDB Endowment
Publication Type:: Journal Article
Citation:: The VLDB Journal, 2020, 29, pp. 353-392
Issue Date:: 2020

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Field	Value	Language
dc.contributor.author	Fang, Y
dc.contributor.author	Huang, X
dc.contributor.author	Qin, L https://orcid.org/0000-0001-6068-5062
dc.contributor.author	Zhang, Y
dc.contributor.author	Zhang, W
dc.contributor.author	Cheng, R
dc.contributor.author	Lin, X
dc.date.accessioned	2021-03-09T19:20:38Z
dc.date.available	2021-03-09T19:20:38Z
dc.date.issued	2020
dc.identifier.citation	The VLDB Journal, 2020, 29, pp. 353-392
dc.identifier.issn	0949-877X
dc.identifier.uri	http://hdl.handle.net/10453/146965
dc.description.abstract	With the rapid development of information technologies, various big graphs are prevalent in many real applications (e.g., social media and knowledge bases). An important component of these graphs is the network community. Essentially, a community is a group of vertices which are densely connected internally. Community retrieval can be used in many real applications, such as event organization, friend recommendation, and so on. Consequently, how to efficiently find high-quality communities from big graphs is an important research topic in the era of big data. Recently, a large group of research works, called community search, have been proposed. They aim to provide efficient solutions for searching high-quality communities from large networks in real time. Nevertheless, these works focus on different types of graphs and formulate communities in different manners, and thus, it is desirable to have a comprehensive review of these works. In this survey, we conduct a thorough review of existing community search works. Moreover, we analyze and compare the quality of communities under their models, and the performance of different solutions. Furthermore, we point out new research directions. This survey does not only help researchers to have better understanding of existing community search solutions, but also provides practitioners a better judgment on choosing the proper solutions.
dc.language	en
dc.publisher	VLDB Endowment
dc.relation.ispartof	The VLDB Journal
dc.relation.isbasedon	10.1007/s00778-019-00556-x
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0804 Data Format, 0805 Distributed Computing, 0806 Information Systems
dc.subject.classification	Information Systems
dc.title	A survey of community search over big graphs.
dc.type	Journal Article
utslib.citation.volume	29
utslib.for	0804 Data Format
utslib.for	0805 Distributed Computing
utslib.for	0806 Information Systems
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-03-09T19:20:35Z
pubs.publication-status	Published
pubs.volume	29

Abstract:

With the rapid development of information technologies, various big graphs are prevalent in many real applications (e.g., social media and knowledge bases). An important component of these graphs is the network community. Essentially, a community is a group of vertices which are densely connected internally. Community retrieval can be used in many real applications, such as event organization, friend recommendation, and so on. Consequently, how to efficiently find high-quality communities from big graphs is an important research topic in the era of big data. Recently, a large group of research works, called community search, have been proposed. They aim to provide efficient solutions for searching high-quality communities from large networks in real time. Nevertheless, these works focus on different types of graphs and formulate communities in different manners, and thus, it is desirable to have a comprehensive review of these works. In this survey, we conduct a thorough review of existing community search works. Moreover, we analyze and compare the quality of communities under their models, and the performance of different solutions. Furthermore, we point out new research directions. This survey does not only help researchers to have better understanding of existing community search solutions, but also provides practitioners a better judgment on choosing the proper solutions.

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http://hdl.handle.net/10453/146965