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| Fællesskabsdetektion× | Social netværksanalyse× | |
|---|---|---|
| Fagområde | Netværksanalyse | Netværksanalyse |
| Familie≠ | Process / pipeline | Machine learning |
| Oprindelsesår≠ | 2002–2019 (algorithm family) | 1934 (sociometry); 1994 (modern formalization) |
| Ophavsperson≠ | Louvain: Blondel et al. (2008); Leiden: Traag et al. (2019); Girvan-Newman: Girvan & Newman (2002); Infomap: Rosvall & Bergstrom (2008) | Moreno, J.L.; formalized by Wasserman & Faust |
| Type≠ | Graph-partitioning / clustering algorithm family | Structural/relational analysis framework |
| Oprindelig kilde≠ | 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 ↗ | Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1 |
| Aliasser≠ | graph clustering, network partitioning, Topluluk Tespiti (Louvain, Girvan-Newman, Leiden) | SNA, network analysis, sociometric analysis, relational analysis |
| Relaterede | 5 | 5 |
| Resumé≠ | 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? | Social Network Analysis (SNA) is a structural method that maps and measures relationships and flows between people, groups, organizations, or other entities modeled as nodes connected by ties (edges). Rather than focusing on individual attributes, SNA reveals how the pattern of connections shapes behavior, influence, information flow, and outcomes within a system. |
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