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| Gerichtete Nähe-Zentralität× | Gerichtete Netzwerkanalyse× | |
|---|---|---|
| Fachgebiet | Netzwerkanalyse | Netzwerkanalyse |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 1979–1994 | 1994 |
| Urheber≠ | Freeman, L. C.; Wasserman, S. & Faust, K. | Wasserman, S. & Faust, K. |
| Typ≠ | Centrality measure | Structural analysis of directed graphs |
| Wegweisende Quelle | Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38269-4 | Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1 |
| Aliasnamen | directed closeness, in-closeness centrality, out-closeness centrality, directional closeness | directed SNA, digraph analysis, directed graph network analysis, asymmetric network analysis |
| Verwandt | 5 | 5 |
| Zusammenfassung≠ | Directed closeness centrality extends the classical closeness measure to directed networks by separately quantifying how quickly a node can be reached by others (in-closeness) and how quickly it can reach all others (out-closeness). It is a foundational node-level metric in social network analysis and graph theory, used wherever link direction conveys meaningful asymmetry such as citation flows, information cascades, or authority 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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