Handle URI:
http://hdl.handle.net/10754/624935
Title:
Community Detection for Large Graphs
Authors:
Peng, Chengbin ( 0000-0002-7445-2638 ) ; Kolda, Tamara G.; Pinar, Ali; Zhang, Zhihua; Keyes, David E. ( 0000-0002-4052-7224 )
Abstract:
Many real world networks have inherent community structures, including social networks, transportation networks, biological networks, etc. For large scale networks with millions or billions of nodes in real-world applications, accelerating current community detection algorithms is in demand, and we present two approaches to tackle this issue -A K-core based framework that can accelerate existing community detection algorithms significantly; -A parallel inference algorithm via stochastic block models that can distribute the workload.
KAUST Department:
Computer, Electrical and Mathematical Sciences & Engineering (CEMSE)
Conference/Event name:
SHAXC-2 Workshop 2014
Issue Date:
4-May-2014
Type:
Poster
Appears in Collections:
Posters; Scalable Hierarchical Algorithms for eXtreme Computing (SHAXC-2) Workshop 2014

Full metadata record

DC FieldValue Language
dc.contributor.authorPeng, Chengbinen
dc.contributor.authorKolda, Tamara G.en
dc.contributor.authorPinar, Alien
dc.contributor.authorZhang, Zhihuaen
dc.contributor.authorKeyes, David E.en
dc.date.accessioned2017-06-12T10:24:00Z-
dc.date.available2017-06-12T10:24:00Z-
dc.date.issued2014-05-04-
dc.identifier.urihttp://hdl.handle.net/10754/624935-
dc.description.abstractMany real world networks have inherent community structures, including social networks, transportation networks, biological networks, etc. For large scale networks with millions or billions of nodes in real-world applications, accelerating current community detection algorithms is in demand, and we present two approaches to tackle this issue -A K-core based framework that can accelerate existing community detection algorithms significantly; -A parallel inference algorithm via stochastic block models that can distribute the workload.en
dc.titleCommunity Detection for Large Graphsen
dc.typePosteren
dc.contributor.departmentComputer, Electrical and Mathematical Sciences & Engineering (CEMSE)en
dc.conference.dateMay 4-6, 2014en
dc.conference.nameSHAXC-2 Workshop 2014en
dc.conference.locationKAUSTen
dc.contributor.institutionSandia National Laboratoriesen
dc.contributor.institutionShaghai Jiao Tong Universityen
kaust.authorPeng, Chengbinen
kaust.authorKeyes, David E.en
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