Comparer des méthodes

	Centralité de vecteur propre	Analyse de modularité
Domaine	Analyse de réseaux	Analyse de réseaux
Famille	Machine learning	Machine learning
Année d'origine	1972	2004
Auteur d'origine	Bonacich, P.	Newman, M. E. J. & Girvan, M.
Type	Centrality measure	Community detection / graph partitioning
Source fondatrice	Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology, 2(1), 113–120.	Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113.
Alias	eigenvector centrality, EC, Bonacich centrality, power centrality	Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity
Apparentées	6	5
Résumé	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.	Modularity analysis is a network science method, formalized by Newman and Girvan in 2004, that detects community structure in graphs by measuring whether edges are more concentrated within groups than expected by chance. Its scalar quality index Q guides algorithms that partition nodes into cohesive clusters, making it the most widely adopted framework for community detection in social, biological, and technological networks.