Comparer des méthodes

	Centralité de proximité	Centralité de vecteur propre
Domaine	Analyse de réseaux	Analyse de réseaux
Famille	Machine learning	Machine learning
Année d'origine	1950 (formalized 1979)	1972
Auteur d'origine	Bavelas, A.; formalized by Freeman, L. C.	Bonacich, P.
Type	Node-level centrality index	Centrality measure
Source fondatrice	Freeman, L. C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239.	Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology, 2(1), 113–120.
Alias	closeness, farness-based centrality, geodesic closeness, normalized closeness centrality	eigenvector centrality, EC, Bonacich centrality, power centrality
Apparentées	6	6
Résumé	Closeness centrality measures how quickly a node can reach all others in a network by computing the inverse of its average shortest-path distance to every other node. First described by Bavelas (1950) and formally unified by Freeman (1979), it identifies nodes that can spread information or resources efficiently across the entire graph — not merely nodes with many direct contacts.	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.