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| Betweenness-Zentralität× | Klupplungzentralität× | |
|---|---|---|
| Fachgebiet | Netzwerkanalyse | Netzwerkanalyse |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 1977 | 1950 (formalized 1979) |
| Urheber≠ | Freeman, L. C. | Bavelas, A.; formalized by Freeman, L. C. |
| Typ≠ | Centrality measure | Node-level centrality index |
| Wegweisende Quelle≠ | Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35–41. DOI ↗ | Freeman, L. C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239. DOI ↗ |
| Aliasnamen | Freeman betweenness, BC, geodesic betweenness, shortest-path betweenness | closeness, farness-based centrality, geodesic closeness, normalized closeness centrality |
| Verwandt | 6 | 6 |
| Zusammenfassung≠ | Betweenness centrality, formalized by Linton C. Freeman in 1977, measures how often a node lies on the shortest path connecting every other pair of nodes in a network. High-betweenness nodes act as bridges or brokers: removing them fragments the network into disconnected components more severely than removing any other nodes. | 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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