Stable Community Detection in Signed Social Networks

Sun, R; Chen, C; Wang, X; Zhang, Y; Wang, X

Stable Community Detection in Signed Social Networks

Sun, R Chen, C Wang, X Zhang, Y

Wang, X

Permalink

Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Knowledge and Data Engineering, 2021, PP, (99), pp. 1-1
Issue Date:: 2021-01-01

Closed Access

	Filename	Description	Size
	Stable_Community_Detection_in_Signed_Social_Networks.pdf	Published version	845.8 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Sun, R
dc.contributor.author	Chen, C
dc.contributor.author	Wang, X
dc.contributor.author	Zhang, Y https://orcid.org/0000-0002-2674-1638
dc.contributor.author	Wang, X
dc.date.accessioned	2022-03-30T03:44:17Z
dc.date.available	2022-03-30T03:44:17Z
dc.date.issued	2021-01-01
dc.identifier.citation	IEEE Transactions on Knowledge and Data Engineering, 2021, PP, (99), pp. 1-1
dc.identifier.issn	1041-4347
dc.identifier.issn	1558-2191
dc.identifier.uri	http://hdl.handle.net/10453/155709
dc.description.abstract	IEEE Community detection is one of the most fundamental problems in social network analysis, while most existing research focuses on unsigned graphs. In real applications, social networks involve not only positive relationships but also negative ones. It is important to exploit the signed information to identify more stable communities. In this paper, we propose a novel model, named stable k-core, to measure the stability of a community in signed graphs. The stable k-core model not only emphasizes user engagement, but also eliminates unstable structures. We show that the problem of finding the maximum stable k-core is NP-hard. To scale for large graphs, novel pruning strategies and searching methods are proposed. We conduct extensive experiments on 6 real-world signed networks to verify the efficiency and effectiveness of proposed model and techniques.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation	http://purl.org/au-research/grants/arc/FT170100128
dc.relation	http://purl.org/au-research/grants/arc/DP180103096
dc.relation.ispartof	IEEE Transactions on Knowledge and Data Engineering
dc.relation.isbasedon	10.1109/TKDE.2020.3047224
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences
dc.subject.classification	Information Systems
dc.title	Stable Community Detection in Signed Social Networks
dc.type	Journal Article
utslib.citation.volume	PP
utslib.for	08 Information and Computing Sciences
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	2022-03-30T03:44:16Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

IEEE Community detection is one of the most fundamental problems in social network analysis, while most existing research focuses on unsigned graphs. In real applications, social networks involve not only positive relationships but also negative ones. It is important to exploit the signed information to identify more stable communities. In this paper, we propose a novel model, named stable k-core, to measure the stability of a community in signed graphs. The stable k-core model not only emphasizes user engagement, but also eliminates unstable structures. We show that the problem of finding the maximum stable k-core is NP-hard. To scale for large graphs, novel pruning strategies and searching methods are proposed. We conduct extensive experiments on 6 real-world signed networks to verify the efficiency and effectiveness of proposed model and techniques.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/155709