Сравнение методов

	Временная собственная центральность	Временная центральность по степени
Область	Сетевой анализ	Сетевой анализ
Семейство	Machine learning	Machine learning
Год появления	2011-2017	2011–2012
Автор метода	Grindrod, P.; Higham, D. J.; Taylor, D. et al.	Holme, P.; Saramaki, J.; Kim, H.; Anderson, R.
Тип	Centrality measure for temporal networks	Centrality measure (temporal extension)
Основополагающий источник	Grindrod, P., Parsons, M. C., Higham, D. J., & Estrada, E. (2011). Communicability across evolving networks. Physical Review E, 83(4), 046120.	Holme, P. & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.
Другие названия	dynamic eigenvector centrality, time-varying eigenvector centrality, TEC, temporal communicability centrality	time-varying degree centrality, dynamic degree centrality, temporal node degree, TDC
Связанные	5	6
Сводка	Temporal eigenvector centrality extends the classical eigenvector centrality to networks that change over time. By accounting for the ordering and timing of connections, it identifies nodes that are influential not merely because of many simultaneous connections, but because they sit at the crossroads of sequentially important pathways across multiple time slices of the network.	Temporal degree centrality extends the classic degree centrality to time-varying networks by counting how many distinct contacts a node accumulates over time. Rather than collapsing a dynamic network into a single static graph, it preserves the temporal order of edges, yielding a more faithful measure of a node's activity and reachability across the observation window.