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| Gerichtete Netzwerkanalyse× | Betweenness-Zentralität× | |
|---|---|---|
| Fachgebiet | Netzwerkanalyse | Netzwerkanalyse |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 1994 | 1977 |
| Urheber≠ | Wasserman, S. & Faust, K. | Freeman, L. C. |
| Typ≠ | Structural analysis of directed graphs | Centrality measure |
| Wegweisende Quelle≠ | Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1 | Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35–41. DOI ↗ |
| Aliasnamen | directed SNA, digraph analysis, directed graph network analysis, asymmetric network analysis | Freeman betweenness, BC, geodesic betweenness, shortest-path betweenness |
| Verwandt≠ | 5 | 6 |
| Zusammenfassung≠ | Directed Social Network Analysis (directed SNA) studies networks in which every tie has an explicit direction — from a sender to a receiver — rather than treating relationships as symmetric. It extends the classical SNA toolkit with in-degree, out-degree, reciprocity, and asymmetric path measures, making it the appropriate framework wherever relationship direction carries substantive meaning, such as citation flows, advice-seeking, follower graphs, or information cascades. | 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. |
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