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| Ego-netværksanalyse× | Fællesskabsdetektion× | |
|---|---|---|
| Fagområde | Netværksanalyse | Netværksanalyse |
| Familie | Process / pipeline | Process / pipeline |
| Oprindelsesår≠ | 1992 (Burt); foundational measurement formalised by Marsden 2002 | 2002–2019 (algorithm family) |
| Ophavsperson≠ | Ronald S. Burt (structural holes framework); Peter V. Marsden (egocentric measures) | Louvain: Blondel et al. (2008); Leiden: Traag et al. (2019); Girvan-Newman: Girvan & Newman (2002); Infomap: Rosvall & Bergstrom (2008) |
| Type≠ | Descriptive / relational network analysis | Graph-partitioning / clustering algorithm family |
| Oprindelig kilde≠ | Burt, R.S. (1992). Structural Holes: The Social Structure of Competition. Harvard University Press. ISBN: 9780674843714 | 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 | personal network analysis, egocentric network analysis, Ego Ağı Analizi (Personal Network Analysis) | graph clustering, network partitioning, Topluluk Tespiti (Louvain, Girvan-Newman, Leiden) |
| Relaterede≠ | 6 | 5 |
| Resumé≠ | Ego network analysis examines the personal network of a focal individual — the ego — by mapping their direct contacts (alters) and the ties those contacts share with one another. Formalised through Ronald Burt's structural holes framework (1992) and Marsden's egocentric measurement approach (2002), the method produces ego-level indicators such as network size, density, constraint, and brokerage role that reveal how each individual's social position shapes their access to information, resources, and influence. | 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? |
| ScholarGateDatasæt ↗ |
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