Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Vērstā starpniecības centralitāte× | Vērstā tuvuma centralitāte× | |
|---|---|---|
| Nozare | Tīklu analīze | Tīklu analīze |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 1977 | 1979–1994 |
| Autors≠ | Freeman, L. C. | Freeman, L. C.; Wasserman, S. & Faust, K. |
| Tips≠ | Centrality measure (directed graph) | Centrality measure |
| Pirmavots≠ | 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: 978-0-521-38269-4 |
| Citi nosaukumi | directed BC, digraph betweenness, asymmetric betweenness centrality, directed Freeman betweenness | directed closeness, in-closeness centrality, out-closeness centrality, directional closeness |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | 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 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. |
| ScholarGateDatu kopa ↗ |
|
|