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| Directed Betweenness Centrality× | Gerichtete Netzwerkanalyse× | |
|---|---|---|
| Fachgebiet | Netzwerkanalyse | Netzwerkanalyse |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 1977 | 1994 |
| Urheber≠ | Freeman, L. C. | Wasserman, S. & Faust, K. |
| Typ≠ | Centrality measure (directed graph) | Structural analysis of directed graphs |
| Wegweisende Quelle≠ | Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35–41. DOI ↗ | Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1 |
| Aliasnamen | directed BC, digraph betweenness, asymmetric betweenness centrality, directed Freeman betweenness | directed SNA, digraph analysis, directed graph network analysis, asymmetric network analysis |
| Verwandt | 5 | 5 |
| Zusammenfassung≠ | Directed Betweenness Centrality extends Freeman's classic betweenness measure to directed graphs, quantifying how often a node lies on the shortest directed paths between all other pairs of nodes. It identifies gatekeepers, brokers, and bottlenecks in asymmetric flows such as information cascades, citation networks, and organizational hierarchies. | 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. |
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