Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Centralita směrované blízkosti× | Centralita blízkosti× | |
|---|---|---|
| Obor | Analýza sítí | Analýza sítí |
| Rodina | Machine learning | Machine learning |
| Rok vzniku≠ | 1979–1994 | 1950 (formalized 1979) |
| Tvůrce≠ | Freeman, L. C.; Wasserman, S. & Faust, K. | Bavelas, A.; formalized by Freeman, L. C. |
| Typ≠ | Centrality measure | Node-level centrality index |
| Původní zdroj≠ | Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38269-4 | Freeman, L. C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239. DOI ↗ |
| Další názvy | directed closeness, in-closeness centrality, out-closeness centrality, directional closeness | closeness, farness-based centrality, geodesic closeness, normalized closeness centrality |
| Příbuzné≠ | 5 | 6 |
| Shrnutí≠ | 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. | 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. |
| ScholarGateDatová sada ↗ |
|
|