Сравнение методов

	Динамическая центральность по собственному вектору	Выявление временных сообществ
Область	Сетевой анализ	Сетевой анализ
Семейство	Machine learning	Machine learning
Год появления	2010s	2010
Автор метода	Lerman, K.; Ghosh, R.; Kang, J. H.	Mucha, P. J. et al.
Тип	Centrality measure for time-evolving networks	Network clustering algorithm
Основополагающий источник	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.	Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. (2010). Community structure in time-dependent, multiscale, and multiplex networks. Science, 328(5980), 876–878.
Другие названия	temporal eigenvector centrality, time-varying eigenvector centrality, dynamic EC, evolving eigenvector centrality	dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection
Связанные	4	6
Сводка	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.	Temporal community detection identifies cohesive groups (communities) in networks whose structure changes over time. By treating each time snapshot as a network layer and coupling consecutive layers, it reveals how communities form, merge, split, grow, or dissolve — turning a sequence of static snapshots into a continuous narrative of group evolution.