Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Centralidad de Cercanía Dirigida× | Centralidad de intermediación dirigida× | |
|---|---|---|
| Campo | Análisis de redes | Análisis de redes |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 1979–1994 | 1977 |
| Autor original≠ | Freeman, L. C.; Wasserman, S. & Faust, K. | Freeman, L. C. |
| Tipo≠ | Centrality measure | Centrality measure (directed graph) |
| Fuente seminal≠ | Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38269-4 | Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35–41. DOI ↗ |
| Alias | directed closeness, in-closeness centrality, out-closeness centrality, directional closeness | directed BC, digraph betweenness, asymmetric betweenness centrality, directed Freeman betweenness |
| Relacionados | 5 | 5 |
| Resumen≠ | 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 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. |
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