Usporedite metode

	Usmjerena bliskostna centralnost	Usmjerena svojstvena centralnost
Područje	Analiza mreža	Analiza mreža
Obitelj	Machine learning	Machine learning
Godina nastanka	1979–1994	1972–1987
Tvorac	Freeman, L. C.; Wasserman, S. & Faust, K.	Bonacich, P.
Vrsta	Centrality measure	Centrality measure (eigenvector-based, directed)
Temeljni izvor	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.
Drugi nazivi	directed closeness, in-closeness centrality, out-closeness centrality, directional closeness	directed EC, asymmetric eigenvector centrality, right eigenvector centrality, left eigenvector centrality
Srodne	5	5
Sažetak	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.