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| Gewichtete Nähe-Zentralität× | Klupplungzentralität× | |
|---|---|---|
| Fachgebiet | Netzwerkanalyse | Netzwerkanalyse |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2010 | 1950 (formalized 1979) |
| Urheber≠ | Opsahl, T.; Agneessens, F.; Skvoretz, J. | Bavelas, A.; formalized by Freeman, L. C. |
| Typ≠ | Centrality measure (network analysis) | Node-level centrality index |
| Wegweisende Quelle≠ | Opsahl, T., Agneessens, F. & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251. DOI ↗ | Freeman, L. C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239. DOI ↗ |
| Aliasnamen | weighted closeness, generalized closeness centrality, WCC, distance-weighted closeness | closeness, farness-based centrality, geodesic closeness, normalized closeness centrality |
| Verwandt | 6 | 6 |
| Zusammenfassung≠ | Weighted closeness centrality extends the classic closeness measure to networks where edges carry numerical weights — such as frequency, strength, or cost — by incorporating those weights into shortest-path distances. Nodes that can reach others quickly along strong or efficient connections receive higher scores, making it a richer indicator of information-spreading potential than its binary counterpart. | 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. |
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