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| Netzwerkresilienz und Schwachstellenanalyse× | Community Detection× | |
|---|---|---|
| Fachgebiet | Netzwerkanalyse | Netzwerkanalyse |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 2000 | 2002–2019 (algorithm family) |
| Urheber≠ | Albert, Jeong & Barabási | Louvain: Blondel et al. (2008); Leiden: Traag et al. (2019); Girvan-Newman: Girvan & Newman (2002); Infomap: Rosvall & Bergstrom (2008) |
| Typ≠ | Network robustness / vulnerability framework | Graph-partitioning / clustering algorithm family |
| Wegweisende Quelle≠ | Albert, 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 ↗ |
| Aliasnamen | network vulnerability analysis, attack tolerance analysis, Ağ Dayanıklılığı ve Güvenlik Açığı Analizi | graph clustering, network partitioning, Topluluk Tespiti (Louvain, Girvan-Newman, Leiden) |
| Verwandt | 5 | 5 |
| Zusammenfassung≠ | Network 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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