Discovering strong communities with user engagement and tie strength

Zhang, F; Yuan, L; Zhang, Y; Qin, L; Lin, X; Zhou, A

Discovering strong communities with user engagement and tie strength

Zhang, F Yuan, L Zhang, Y

Qin, L

Lin, X Zhou, A

Permalink

Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, 10827 LNCS pp. 425 - 441
Issue Date:: 2018-01-01

Closed Access

	Filename	Description	Size
	Zhang2018_Chapter_DiscoveringStrongCommunitiesWi.pdf	Published version	1.09 MB	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	Zhang, F	en_US
dc.contributor.author	Yuan, L	en_US
dc.contributor.author	Zhang, Y https://orcid.org/0000-0002-2674-1638	en_US
dc.contributor.author	Qin, L https://orcid.org/0000-0001-6068-5062	en_US
dc.contributor.author	Lin, X	en_US
dc.contributor.author	Zhou, A	en_US
dc.date.issued	2018-01-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, 10827 LNCS pp. 425 - 441	en_US
dc.identifier.isbn	9783319914510	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/127288
dc.description.abstract	© Springer International Publishing AG, part of Springer Nature 2018. In this paper, we propose and study a novel cohesive subgraph model, named (k,s)-core, which requires each user to have at least k familiars or friends (not just acquaintances) in the subgraph. The model considers both user engagement and tie strength to discover strong communities. We compare the (k,s)-core model with k-core and k-truss theoretically and experimentally. We propose efficient algorithms to compute the (k,s)-core and decompose the graph by a particular sub-model k-fami. Extensive experiments show (1) our (k,s)-core and k-fami are effective cohesive subgraph models and (2) the (k,s)-core computation and k-fami decomposition are efficient on various real-life social networks.	en_US
dc.relation	http://purl.org/au-research/grants/arc/FT170100128
dc.relation	http://purl.org/au-research/grants/arc/DP180103096
dc.relation	http://purl.org/au-research/grants/arc/DP160101513
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-319-91452-7_28	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Discovering strong communities with user engagement and tie strength	en_US
dc.type	Conference Proceeding
utslib.citation.volume	10827 LNCS	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
pubs.embargo.period	Not known	en_US
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
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	10827 LNCS	en_US

Abstract:

© Springer International Publishing AG, part of Springer Nature 2018. In this paper, we propose and study a novel cohesive subgraph model, named (k,s)-core, which requires each user to have at least k familiars or friends (not just acquaintances) in the subgraph. The model considers both user engagement and tie strength to discover strong communities. We compare the (k,s)-core model with k-core and k-truss theoretically and experimentally. We propose efficient algorithms to compute the (k,s)-core and decompose the graph by a particular sub-model k-fami. Extensive experiments show (1) our (k,s)-core and k-fami are effective cohesive subgraph models and (2) the (k,s)-core computation and k-fami decomposition are efficient on various real-life social networks.

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

http://hdl.handle.net/10453/127288