Coevolutionary modeling in network formation

Handle URI:
http://hdl.handle.net/10754/550507
Title:
Coevolutionary modeling in network formation
Authors:
Al-Shyoukh, Ibrahim; Chasparis, Georgios; Shamma, Jeff S. ( 0000-0001-5638-9551 )
Abstract:
Network coevolution, the process of network topology evolution in feedback with dynamical processes over the network nodes, is a common feature of many engineered and natural networks. In such settings, the change in network topology occurs at a comparable time scale to nodal dynamics. Coevolutionary modeling offers the possibility to better understand how and why network structures emerge. For example, social networks can exhibit a variety of structures, ranging from almost uniform to scale-free degree distributions. While current models of network formation can reproduce these structures, coevolutionary modeling can offer a better understanding of the underlying dynamics. This paper presents an overview of recent work on coevolutionary models of network formation, with an emphasis on the following three settings: (i) dynamic flow of benefits and costs, (ii) transient link establishment costs, and (iii) latent preferential attachment.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Publisher:
IEEE
Journal:
Signal and Information Processing (GlobalSIP), 2014 IEEE Global Conference on
Conference/Event name:
2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014
Issue Date:
3-Dec-2014
DOI:
10.1109/GlobalSIP.2014.7032213
Type:
Conference Paper
Additional Links:
http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7032213
Appears in Collections:
Conference Papers; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorAl-Shyoukh, Ibrahimen
dc.contributor.authorChasparis, Georgiosen
dc.contributor.authorShamma, Jeff S.en
dc.date.accessioned2015-04-23T13:53:46Zen
dc.date.available2015-04-23T13:53:46Zen
dc.date.issued2014-12-03en
dc.identifier.doi10.1109/GlobalSIP.2014.7032213en
dc.identifier.urihttp://hdl.handle.net/10754/550507en
dc.description.abstractNetwork coevolution, the process of network topology evolution in feedback with dynamical processes over the network nodes, is a common feature of many engineered and natural networks. In such settings, the change in network topology occurs at a comparable time scale to nodal dynamics. Coevolutionary modeling offers the possibility to better understand how and why network structures emerge. For example, social networks can exhibit a variety of structures, ranging from almost uniform to scale-free degree distributions. While current models of network formation can reproduce these structures, coevolutionary modeling can offer a better understanding of the underlying dynamics. This paper presents an overview of recent work on coevolutionary models of network formation, with an emphasis on the following three settings: (i) dynamic flow of benefits and costs, (ii) transient link establishment costs, and (iii) latent preferential attachment.en
dc.publisherIEEEen
dc.relation.urlhttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7032213en
dc.rights(c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.en
dc.titleCoevolutionary modeling in network formationen
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalSignal and Information Processing (GlobalSIP), 2014 IEEE Global Conference onen
dc.conference.date3 December 2014 through 5 December 2014en
dc.conference.name2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014en
dc.eprint.versionPost-printen
dc.contributor.institutionSchool of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332en
dc.contributor.institutionDepartment of Data Analysis Systems, Software Competence Center Hagenberg GmbH, Hagenberg, Austriaen
kaust.authorShamma, Jeff S.en
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