Võrdle meetodeid

	Suunatud omavektori tsentraalsus	Eigenvector Centrality
Valdkond	Võrgustikuanalüüs	Võrgustikuanalüüs
Perekond	Machine learning	Machine learning
Tekkeaasta	1972–1987	1972
Looja	Bonacich, P.	Bonacich, P.
Tüüp	Centrality measure (eigenvector-based, directed)	Centrality measure
Algallikas	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.
Rööpnimetused	directed EC, asymmetric eigenvector centrality, right eigenvector centrality, left eigenvector centrality	eigenvector centrality, EC, Bonacich centrality, power centrality
Seotud	5	6
Kokkuvõte	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.