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1. Identity statement
Reference TypeJournal Article
Sitemtc-m21b.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34P/3LQAJBE
Repositorysid.inpe.br/mtc-m21b/2016/06.01.16.05   (restricted access)
Last Update2016:06.01.16.06.29 (UTC) simone
Metadata Repositorysid.inpe.br/mtc-m21b/2016/06.01.16.05.51
Metadata Last Update2018:06.04.02.40.48 (UTC) administrator
DOI10.1038/srep25570
ISSN2045-2322
Citation KeyQuilesMacaRubi:2016:DyDeNe
TitleDynamical detection of network communities
Year2016
MonthMay
Access Date2024, May 19
Type of Workjournal article
Secondary TypePRE PI
Number of Files1
Size1922 KiB
2. Context
Author1 Quiles, Marcos G.
2 Macau, Elbert Einstein Nehrer
3 Rubido, Nicolás
Resume Identifier1
2 8JMKD3MGP5W/3C9JGUT
Group1
2 LAC-CTE-INPE-MCTI-GOV-BR
Affiliation1 Universidade Federal de São Paulo (UNIFESP)
2 Instituto Nacional de Pesquisas Espaciais (INPE)
3 Universidad de la República
Author e-Mail Address1
2 elbert.macau@inpe.br
JournalScientific Reports
Volume6
Pages25570
Secondary MarkB2_BIODIVERSIDADE B3_ODONTOLOGIA B3_LETRAS_/_LINGUÍSTICA C_CIÊNCIAS_BIOLÓGICAS_III C_BIOTECNOLOGIA C_ASTRONOMIA_/_FÍSICA
History (UTC)2016-06-01 16:06:29 :: simone -> administrator :: 2016
2018-06-04 02:40:48 :: administrator -> simone :: 2016
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
Abstractstructures. Specifically, communities are groups of nodes that are densely connected among each other but connect sparsely with others. However, detecting communities in networks is so far a major challenge, in particular, when networks evolve in time. Here, we propose a change in the community detection approach. It underlies in defining an intrinsic dynamic for the nodes of the network as interacting particles (based on diffusive equations of motion and on the topological properties of the network) that results in a fast convergence of the particle system into clustered patterns. The resulting patterns correspond to the communities of the network. Since our detection of communities is constructed from a dynamical process, it is able to analyse time-varying networks straightforwardly. Moreover, for static networks, our numerical experiments show that our approach achieves similar results as the methodologies currently recognized as the most efficient ones. Also, since our approach defines an N-body problem, it allows for efficient numerical implementations using parallel computations that increase its speed performance.
AreaCOMP
Arrangementurlib.net > BDMCI > Fonds > Produção anterior à 2021 > LABAC > Dynamical detection of...
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4. Conditions of access and use
Languageen
Target Filequiles_dynamical.pdf
User Groupsimone
Reader Groupadministrator
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Visibilityshown
Read Permissiondeny from all and allow from 150.163
Update Permissionnot transferred
5. Allied materials
Mirror Repositoryurlib.net/www/2011/03.29.20.55
Next Higher Units8JMKD3MGPCW/3ESGTTP
Citing Item Listsid.inpe.br/bibdigital/2013/09.22.23.14 2
sid.inpe.br/mtc-m21/2012/07.13.14.45.07 1
DisseminationWEBSCI; PORTALCAPES; SCOPUS.
Host Collectionsid.inpe.br/mtc-m21b/2013/09.26.14.25.20
6. Notes
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