From link-prediction in brain connectomes and protein interactomes to the local-community-paradigm in complex networks.

Handle URI:
http://hdl.handle.net/10754/334511
Title:
From link-prediction in brain connectomes and protein interactomes to the local-community-paradigm in complex networks.
Authors:
Cannistraci, C.V.; Alanis-Lobato, G.; Ravasi, Timothy ( 0000-0002-9950-465X )
Abstract:
Growth and remodelling impact the network topology of complex systems, yet a general theory explaining how new links arise between existing nodes has been lacking, and little is known about the topological properties that facilitate link-prediction. Here we investigate the extent to which the connectivity evolution of a network might be predicted by mere topological features. We show how a link/community-based strategy triggers substantial prediction improvements because it accounts for the singular topology of several real networks organised in multiple local communities - a tendency here named local-community-paradigm (LCP). We observe that LCP networks are mainly formed by weak interactions and characterise heterogeneous and dynamic systems that use self-organisation as a major adaptation strategy. These systems seem designed for global delivery of information and processing via multiple local modules. Conversely, non-LCP networks have steady architectures formed by strong interactions, and seem designed for systems in which information/energy storage is crucial.
KAUST Department:
Biological and Environmental Sciences and Engineering (BESE) Division
Citation:
Cannistraci CV, Alanis-Lobato G, Ravasi T (2013) From link-prediction in brain connectomes and protein interactomes to the local-community-paradigm in complex networks. Sci Rep 3. doi:10.1038/srep01613.
Publisher:
Nature Publishing Group
Journal:
Scientific reports
Issue Date:
8-Apr-2013
DOI:
10.1038/srep01613
PubMed ID:
23563395
PubMed Central ID:
PMC3619147
Type:
Article
ISSN:
20452322
Appears in Collections:
Articles; Biological and Environmental Sciences and Engineering (BESE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorCannistraci, C.V.en
dc.contributor.authorAlanis-Lobato, G.en
dc.contributor.authorRavasi, Timothyen
dc.date.accessioned2014-11-11T14:27:48Z-
dc.date.available2014-11-11T14:27:48Z-
dc.date.issued2013-04-08en
dc.identifier.citationCannistraci CV, Alanis-Lobato G, Ravasi T (2013) From link-prediction in brain connectomes and protein interactomes to the local-community-paradigm in complex networks. Sci Rep 3. doi:10.1038/srep01613.en
dc.identifier.issn20452322en
dc.identifier.pmid23563395en
dc.identifier.doi10.1038/srep01613en
dc.identifier.urihttp://hdl.handle.net/10754/334511en
dc.description.abstractGrowth and remodelling impact the network topology of complex systems, yet a general theory explaining how new links arise between existing nodes has been lacking, and little is known about the topological properties that facilitate link-prediction. Here we investigate the extent to which the connectivity evolution of a network might be predicted by mere topological features. We show how a link/community-based strategy triggers substantial prediction improvements because it accounts for the singular topology of several real networks organised in multiple local communities - a tendency here named local-community-paradigm (LCP). We observe that LCP networks are mainly formed by weak interactions and characterise heterogeneous and dynamic systems that use self-organisation as a major adaptation strategy. These systems seem designed for global delivery of information and processing via multiple local modules. Conversely, non-LCP networks have steady architectures formed by strong interactions, and seem designed for systems in which information/energy storage is crucial.en
dc.language.isoenen
dc.publisherNature Publishing Groupen
dc.rightshttp://creativecommons.org/licenses/by-nc-sa/3.0/en
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/en
dc.subjectnerve proteinen
dc.subjectproteomeen
dc.subjectbiological modelen
dc.subjectcomputer simulationen
dc.subjectconnectomeen
dc.subjectcytologyen
dc.subjectmetabolismen
dc.subjectmethodologyen
dc.subjectnerve cell networken
dc.subjectprotein analysisen
dc.subjectstructure activity relationen
dc.subjectComputer Simulationen
dc.subjectConnectomeen
dc.subjectModels, Neurologicalen
dc.subjectNerve Neten
dc.subjectNerve Tissue Proteinsen
dc.subjectProtein Interaction Mappingen
dc.subjectProteomeen
dc.subjectStructure-Activity Relationshipen
dc.titleFrom link-prediction in brain connectomes and protein interactomes to the local-community-paradigm in complex networks.en
dc.typeArticleen
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Divisionen
dc.identifier.journalScientific reportsen
dc.identifier.pmcidPMC3619147en
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorCannistraci, Carloen

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