Salīdzināt metodes

	Dinamiskā īpašvērtību centralitāte	Īpašvektoru centralitāte
Nozare	Tīklu analīze	Tīklu analīze
Saime	Machine learning	Machine learning
Izcelsmes gads	2010s	1972
Autors	Lerman, K.; Ghosh, R.; Kang, J. H.	Bonacich, P.
Tips	Centrality measure for time-evolving networks	Centrality measure
Pirmavots	Lerman, K., Ghosh, R., & Kang, J. H. (2010). Centrality metric for dynamic networks. Proceedings of the 8th Workshop on Mining and Learning with Graphs (MLG '10). ACM.	Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology, 2(1), 113–120.
Citi nosaukumi	temporal eigenvector centrality, time-varying eigenvector centrality, dynamic EC, evolving eigenvector centrality	eigenvector centrality, EC, Bonacich centrality, power centrality
Saistītās	4	6
Kopsavilkums	Dynamic eigenvector centrality extends the classic eigenvector centrality measure to networks that change over time. Rather than computing a single leading eigenvector on a static adjacency matrix, it tracks how a node's influence — defined by the importance of its neighbours — evolves across snapshots or time windows. The method is used in social network analysis, epidemiology, and information diffusion studies where network topology shifts continuously.	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.