Comparer des méthodes

	Analyse de la diffusion en réseau	Centralité de vecteur propre
Domaine	Analyse de réseaux	Analyse de réseaux
Famille	Machine learning	Machine learning
Année d'origine	1927 (epidemic roots); network formalization 1990s–2000s	1972
Auteur d'origine	Kermack, W. O. & McKendrick, A. G.	Bonacich, P.
Type	Simulation / analytical model	Centrality measure
Source fondatrice	Kermack, W. O. & McKendrick, A. G. (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London A, 115(772), 700–721.	Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology, 2(1), 113–120.
Alias	diffusion on networks, information diffusion, contagion spreading model, network propagation model	eigenvector centrality, EC, Bonacich centrality, power centrality
Apparentées	5	6
Résumé	Network diffusion analysis models how information, diseases, behaviors, or innovations spread across a graph of nodes and edges. Drawing on classical epidemic theory (SI, SIR, SIS) and modern network science, it tracks which nodes become infected, how quickly, and whether the spread reaches a global cascade or dies out locally.	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.