Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Vērstā starpniecības centralitāte× | Virziena Eigenvektora Centrālisms× | |
|---|---|---|
| Nozare | Tīklu analīze | Tīklu analīze |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 1977 | 1972–1987 |
| Autors≠ | Freeman, L. C. | Bonacich, P. |
| Tips≠ | Centrality measure (directed graph) | Centrality measure (eigenvector-based, directed) |
| Pirmavots≠ | Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35–41. DOI ↗ | Bonacich, P. (1987). Power and centrality: A family of measures. American Journal of Sociology, 92(5), 1170–1182. DOI ↗ |
| Citi nosaukumi | directed BC, digraph betweenness, asymmetric betweenness centrality, directed Freeman betweenness | directed EC, asymmetric eigenvector centrality, right eigenvector centrality, left eigenvector centrality |
| 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 eigenvector centrality extends the classic eigenvector centrality to directed graphs by scoring each node according to the centrality of the nodes that point to it (in-direction) or that it points to (out-direction). A node earns a high score not merely by having many connections but by being connected to other highly central nodes, capturing asymmetric influence in citation networks, social hierarchies, and information flows. |
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