قارن الطرق

	مركزية المتجه الذاتي الموجه	تحليل الشبكات الاجتماعية الموجهة
المجال	تحليل الشبكات	تحليل الشبكات
العائلة	Machine learning	Machine learning
سنة النشأة	1972–1987	1994
صاحب الطريقة	Bonacich, P.	Wasserman, S. & Faust, K.
النوع	Centrality measure (eigenvector-based, directed)	Structural analysis of directed graphs
المصدر التأسيسي	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: 978-0-521-38707-1
الأسماء البديلة	directed EC, asymmetric eigenvector centrality, right eigenvector centrality, left eigenvector centrality	directed SNA, digraph analysis, directed graph network analysis, asymmetric network analysis
ذات صلة	5	5
الملخص	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.