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| Klupplungzentralität× | Analyse sozialer Netzwerke× | |
|---|---|---|
| Fachgebiet | Netzwerkanalyse | Netzwerkanalyse |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 1950 (formalized 1979) | 1934 (sociometry); 1994 (modern formalization) |
| Urheber≠ | Bavelas, A.; formalized by Freeman, L. C. | Moreno, J.L.; formalized by Wasserman & Faust |
| Typ≠ | Node-level centrality index | Structural/relational analysis framework |
| Wegweisende Quelle≠ | Freeman, L. C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239. DOI ↗ | Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1 |
| Aliasnamen | closeness, farness-based centrality, geodesic closeness, normalized closeness centrality | SNA, network analysis, sociometric analysis, relational analysis |
| Verwandt≠ | 6 | 5 |
| Zusammenfassung≠ | Closeness centrality measures how quickly a node can reach all others in a network by computing the inverse of its average shortest-path distance to every other node. First described by Bavelas (1950) and formally unified by Freeman (1979), it identifies nodes that can spread information or resources efficiently across the entire graph — not merely nodes with many direct contacts. | 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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