Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Centralidade de Intermediação Direcionada× | Centralidade de Proximidade Direcionada× | |
|---|---|---|
| Área | Análise de redes | Análise de redes |
| Família | Machine learning | Machine learning |
| Ano de origem≠ | 1977 | 1979–1994 |
| Autor original≠ | Freeman, L. C. | Freeman, L. C.; Wasserman, S. & Faust, K. |
| Tipo≠ | Centrality measure (directed graph) | Centrality measure |
| Fonte seminal≠ | 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 |
| Outros nomes | directed BC, digraph betweenness, asymmetric betweenness centrality, directed Freeman betweenness | directed closeness, in-closeness centrality, out-closeness centrality, directional closeness |
| Relacionados | 5 | 5 |
| Resumo≠ | 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. |
| ScholarGateConjunto de dados ↗ |
|
|