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| Centralità del Vettore Proprio Direzionato× | Analisi delle Reti Sociali Dirette× | |
|---|---|---|
| Campo | Analisi delle reti | Analisi delle reti |
| Famiglia | Machine learning | Machine learning |
| Anno di origine≠ | 1972–1987 | 1994 |
| Ideatore≠ | Bonacich, P. | Wasserman, S. & Faust, K. |
| Tipo≠ | Centrality measure (eigenvector-based, directed) | Structural analysis of directed graphs |
| Fonte seminale≠ | Bonacich, P. (1987). Power and centrality: A family of measures. American Journal of Sociology, 92(5), 1170–1182. DOI ↗ | Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1 |
| Alias | directed EC, asymmetric eigenvector centrality, right eigenvector centrality, left eigenvector centrality | directed SNA, digraph analysis, directed graph network analysis, asymmetric network analysis |
| Correlati | 5 | 5 |
| Sintesi≠ | 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. | Directed Social Network Analysis (directed SNA) studies networks in which every tie has an explicit direction — from a sender to a receiver — rather than treating relationships as symmetric. It extends the classical SNA toolkit with in-degree, out-degree, reciprocity, and asymmetric path measures, making it the appropriate framework wherever relationship direction carries substantive meaning, such as citation flows, advice-seeking, follower graphs, or information cascades. |
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