Influence analysis in social networks: A survey

Peng, S; Zhou, Y; Cao, L; Yu, S; Niu, J; Jia, W

Influence analysis in social networks: A survey

Peng, S Zhou, Y Cao, L Yu, S

Niu, J Jia, W

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Publisher:: ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Journal of Network and Computer Applications, 2018, 106, pp. 17-32
Issue Date:: 2018-03-15

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Field	Value	Language
dc.contributor.author	Peng, S
dc.contributor.author	Zhou, Y
dc.contributor.author	Cao, L
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743
dc.contributor.author	Niu, J
dc.contributor.author	Jia, W
dc.date.accessioned	2021-04-29T00:11:53Z
dc.date.available	2021-04-29T00:11:53Z
dc.date.issued	2018-03-15
dc.identifier.citation	Journal of Network and Computer Applications, 2018, 106, pp. 17-32
dc.identifier.issn	1084-8045
dc.identifier.issn	1095-8592
dc.identifier.uri	http://hdl.handle.net/10453/148522
dc.description.abstract	Complementary to the fancy applications of social networks, influence analysis is an indispensable technique supporting these practical applications. In recent years, this emerging research branch has obtained significant attention from both industry and academia. In this new territory, researchers are facing many unprecedented theoretical and practical challenges. Thus, in this survey, we aim to pave a comprehensive and solid starting ground for interested readers by soliciting the latest work in this area. Firstly, we provide an overview of social networks, including definition, and types of social networks. Secondly, we present the current understanding of social influence analysis from different levels, such as its definition, properties, architecture, applications, and diffusion models. Thirdly, we discuss the evaluation metrics for social influence. Fourthly, we summarize the existing evaluation models on social influence in social networks. We further provide an overview of the existing methods for influence maximization. Finally, we discuss the problems of current algorithms and future trends from various perspectives in this field. We hope this work will shed light for more and more forthcoming researchers to further explore the uncharted part of this promising research field.
dc.language	English
dc.publisher	ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
dc.relation.ispartof	Journal of Network and Computer Applications
dc.relation.isbasedon	10.1016/j.jnca.2018.01.005
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 1005 Communications Technologies
dc.subject.classification	Networking & Telecommunications
dc.title	Influence analysis in social networks: A survey
dc.type	Journal Article
utslib.citation.volume	106
utslib.for	0805 Distributed Computing
utslib.for	1005 Communications Technologies
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/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2021-04-29T00:11:51Z
pubs.publication-status	Published
pubs.volume	106

Abstract:

Complementary to the fancy applications of social networks, influence analysis is an indispensable technique supporting these practical applications. In recent years, this emerging research branch has obtained significant attention from both industry and academia. In this new territory, researchers are facing many unprecedented theoretical and practical challenges. Thus, in this survey, we aim to pave a comprehensive and solid starting ground for interested readers by soliciting the latest work in this area. Firstly, we provide an overview of social networks, including definition, and types of social networks. Secondly, we present the current understanding of social influence analysis from different levels, such as its definition, properties, architecture, applications, and diffusion models. Thirdly, we discuss the evaluation metrics for social influence. Fourthly, we summarize the existing evaluation models on social influence in social networks. We further provide an overview of the existing methods for influence maximization. Finally, we discuss the problems of current algorithms and future trends from various perspectives in this field. We hope this work will shed light for more and more forthcoming researchers to further explore the uncharted part of this promising research field.

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http://hdl.handle.net/10453/148522