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| Bipartipartite netværksanalyse× | Fællesskabsdetektion× | |
|---|---|---|
| Fagområde | Netværksanalyse | Netværksanalyse |
| Familie | Process / pipeline | Process / pipeline |
| Oprindelsesår≠ | 1997 | 2002–2019 (algorithm family) |
| Ophavsperson≠ | Borgatti & Everett (1997) formalised the two-mode network framework | Louvain: Blondel et al. (2008); Leiden: Traag et al. (2019); Girvan-Newman: Girvan & Newman (2002); Infomap: Rosvall & Bergstrom (2008) |
| Type≠ | Graph-structural / relational analysis | Graph-partitioning / clustering algorithm family |
| Oprindelig kilde≠ | Borgatti, S.P. & Everett, M.G. (1997). Network Analysis of 2-Mode Data. Social Networks, 19(3), 243-269. link ↗ | 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 ↗ |
| Aliasser | two-mode network analysis, affiliation network analysis, İki Modlu Ağ Analizi (Bipartite Networks) | graph clustering, network partitioning, Topluluk Tespiti (Louvain, Girvan-Newman, Leiden) |
| Relaterede | 5 | 5 |
| Resumé≠ | Bipartite network analysis, formalised by Borgatti and Everett in 1997, is a graph-structural method for studying networks in which nodes are divided into two disjoint sets — actors and events — and edges exist only between sets, never within them. It is the natural framework for author–paper, patient–disease, user–product, and any other affiliation data, and it extends one-mode network analysis by providing metrics and projection techniques tailored to the two-mode structure. | 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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