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Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.

Analiza rezilienței și vulnerabilității rețelelor×Detecția Comunităților×
DomeniuAnaliza rețelelorAnaliza rețelelor
FamilieProcess / pipelineProcess / pipeline
Anul apariției20002002–2019 (algorithm family)
Autorul originalAlbert, Jeong & BarabásiLouvain: Blondel et al. (2008); Leiden: Traag et al. (2019); Girvan-Newman: Girvan & Newman (2002); Infomap: Rosvall & Bergstrom (2008)
TipNetwork robustness / vulnerability frameworkGraph-partitioning / clustering algorithm family
Sursa seminalăAlbert, R., Jeong, H. & Barabási, A.L. (2000). Error and attack tolerance of complex networks. Nature, 406, 378–382. DOI ↗Blondel, V.D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. (2008). Fast Unfolding of Communities in Large Networks. Journal of Statistical Mechanics, 2008(10), P10008. DOI ↗
Denumiri alternativenetwork vulnerability analysis, attack tolerance analysis, Ağ Dayanıklılığı ve Güvenlik Açığı Analizigraph clustering, network partitioning, Topluluk Tespiti (Louvain, Girvan-Newman, Leiden)
Înrudite55
RezumatNetwork resilience and vulnerability analysis is an analytical framework, formalised by Albert, Jeong, and Barabási (2000), that measures how a network degrades functionally as nodes or edges are progressively removed. By running targeted-attack simulations — removing the highest-centrality nodes first — and random-failure simulations — removing nodes at uniform probability — the framework identifies which structural elements are critical to network integrity and where infrastructure is most exposed.Community detection is a family of graph-partitioning algorithms that discover densely connected sub-groups — communities — within a network. First formalised through the modularity measure by Girvan and Newman (2002), the field advanced rapidly with the Louvain method (Blondel et al., 2008), the Leiden refinement (Traag et al., 2019), and the information-theoretic Infomap approach. All variants answer the same question: which nodes cluster together more tightly among themselves than with the rest of the network?
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ScholarGateCompară metode: Network Resilience Analysis · Community Detection. Preluat la 2026-06-18 de pe https://scholargate.app/ro/compare