Сравнение на методи

	Динамична собствена векторна централност	Собствена централност (Eigenvector Centrality)
Област	Мрежови анализ	Мрежови анализ
Семейство	Machine learning	Machine learning
Година на възникване	2010s	1972
Създател	Lerman, K.; Ghosh, R.; Kang, J. H.	Bonacich, P.
Тип	Centrality measure for time-evolving networks	Centrality measure
Основополагащ източник	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.
Други названия	temporal eigenvector centrality, time-varying eigenvector centrality, dynamic EC, evolving eigenvector centrality	eigenvector centrality, EC, Bonacich centrality, power centrality
Свързани	4	6
Резюме	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.