Comparer des méthodes

	Centralité par vecteur propre dirigé	Centralité de vecteur propre
Domaine	Analyse de réseaux	Analyse de réseaux
Famille	Machine learning	Machine learning
Année d'origine	1972–1987	1972
Auteur d'origine	Bonacich, P.	Bonacich, P.
Type	Centrality measure (eigenvector-based, directed)	Centrality measure
Source fondatrice	Bonacich, P. (1987). Power and centrality: A family of measures. American Journal of Sociology, 92(5), 1170–1182.	Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology, 2(1), 113–120.
Alias	directed EC, asymmetric eigenvector centrality, right eigenvector centrality, left eigenvector centrality	eigenvector centrality, EC, Bonacich centrality, power centrality
Apparentées	5	6
Résumé	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.	Eigenvector centrality, introduced by Bonacich in 1972, measures a node's influence by considering not just how many neighbors it has, but how influential those neighbors are. A node scores highly if it is connected to other high-scoring nodes, making it a recursive, globally-aware measure of structural importance in a network.