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Analyse de réseaux multicouches×Analyse des motifs de réseau×
DomaineAnalyse de réseauxAnalyse de réseaux
FamilleProcess / pipelineProcess / pipeline
Année d'origine2013–2014 (formal mathematical framework)2002
Auteur d'origineKivelä et al. (2014); De Domenico et al. (2013)
TypeGraph-theoretic network modelStatistical pattern-detection method for directed graphs
Source fondatriceKivelä, M. et al. (2014). Multilayer Networks. Journal of Complex Networks, 2(3), 203–271. DOI ↗Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., & Alon, U. (2002). Network Motifs: Simple Building Blocks of Complex Networks. Science, 298(5594), 824-827. DOI ↗
Aliasmultiplex network analysis, multiplex networks, Çok Katmanlı Ağ Analizi (Multiplex Networks)network motifs, subgraph significance profile, Ağ Motif Analizi (Network Motifs)
Apparentées63
RésuméMultilayer network analysis is a graph-theoretic framework, formalised by Kivelä et al. (2014) and De Domenico et al. (2013), that represents the same set of nodes simultaneously across multiple relationship layers. Where a single-layer network collapses all relationships into one graph, the multilayer model preserves the distinct relational context of each layer — social platform, biological interaction type, or infrastructure tier — while also modelling how layers couple with each other through interlayer edges.Network motif analysis is a statistical method for directed networks, introduced by Milo, Shen-Orr, and Alon in 2002, that identifies small recurring subgraph patterns — motifs — that appear significantly more often than would be expected in a comparable random network. By comparing a real network against a null ensemble of randomised graphs, the method reveals the elementary structural building blocks that define the functional organisation of biological regulatory networks, social networks, and other complex systems.
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ScholarGateComparer des méthodes: Multilayer Network Analysis · Network Motif Analysis. Consulté le 2026-06-17 sur https://scholargate.app/fr/compare