השוואת שיטות

	מרכזיות וקטור עצמי מכוון	מדדי קרבה מכוונים
תחום	ניתוח רשתות	ניתוח רשתות
משפחה	Machine learning	Machine learning
שנת המקור	1972–1987	1979–1994
הוגה השיטה	Bonacich, P.	Freeman, L. C.; Wasserman, S. & Faust, K.
סוג	Centrality measure (eigenvector-based, directed)	Centrality measure
מקור מכונן	Bonacich, P. (1987). Power and centrality: A family of measures. American Journal of Sociology, 92(5), 1170–1182.	Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38269-4
כינויים	directed EC, asymmetric eigenvector centrality, right eigenvector centrality, left eigenvector centrality	directed closeness, in-closeness centrality, out-closeness centrality, directional closeness
קשורות	5	5
תקציר	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 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.