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| Centralità di Vicinanza Diretta× | Centralità del Vettore Proprio Direzionato× | |
|---|---|---|
| Campo | Analisi delle reti | Analisi delle reti |
| Famiglia | Machine learning | Machine learning |
| Anno di origine≠ | 1979–1994 | 1972–1987 |
| Ideatore≠ | Freeman, L. C.; Wasserman, S. & Faust, K. | Bonacich, P. |
| Tipo≠ | Centrality measure | Centrality measure (eigenvector-based, directed) |
| Fonte seminale≠ | Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38269-4 | Bonacich, P. (1987). Power and centrality: A family of measures. American Journal of Sociology, 92(5), 1170–1182. DOI ↗ |
| Alias | directed closeness, in-closeness centrality, out-closeness centrality, directional closeness | directed EC, asymmetric eigenvector centrality, right eigenvector centrality, left eigenvector centrality |
| Correlati | 5 | 5 |
| Sintesi≠ | 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 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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