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| Netzwerkmotiv-Analyse× | Community Detection× | |
|---|---|---|
| Fachgebiet | Netzwerkanalyse | Netzwerkanalyse |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 2002 | 2002–2019 (algorithm family) |
| Urheber≠ | — | Louvain: Blondel et al. (2008); Leiden: Traag et al. (2019); Girvan-Newman: Girvan & Newman (2002); Infomap: Rosvall & Bergstrom (2008) |
| Typ≠ | Statistical pattern-detection method for directed graphs | Graph-partitioning / clustering algorithm family |
| Wegweisende Quelle≠ | 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 ↗ | 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 motifs, subgraph significance profile, Ağ Motif Analizi (Network Motifs) | graph clustering, network partitioning, Topluluk Tespiti (Louvain, Girvan-Newman, Leiden) |
| Verwandt≠ | 3 | 5 |
| Zusammenfassung≠ | 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. | 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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