Сравнение методов

	Временная центральность по степени	Временная собственная центральность
Область	Сетевой анализ	Сетевой анализ
Семейство	Machine learning	Machine learning
Год появления	2011–2012	2011-2017
Автор метода	Holme, P.; Saramaki, J.; Kim, H.; Anderson, R.	Grindrod, P.; Higham, D. J.; Taylor, D. et al.
Тип	Centrality measure (temporal extension)	Centrality measure for temporal networks
Основополагающий источник	Holme, P. & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.	Grindrod, P., Parsons, M. C., Higham, D. J., & Estrada, E. (2011). Communicability across evolving networks. Physical Review E, 83(4), 046120.
Другие названия	time-varying degree centrality, dynamic degree centrality, temporal node degree, TDC	dynamic eigenvector centrality, time-varying eigenvector centrality, TEC, temporal communicability centrality
Связанные	6	5
Сводка	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.	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.