Сравнение методов

	Динамическая центральность по степени	Взвешенная степень центральности
Область	Сетевой анализ	Сетевой анализ
Семейство	Machine learning	Machine learning
Год появления	2012	2004
Автор метода	Holme, P. & Saramaki, J.; Kim, H. & Anderson, R.	Barrat, A.; Barthélemy, M.; Pastor-Satorras, R.; Vespignani, A.
Тип	Centrality measure (temporal extension)	Centrality measure for weighted networks
Основополагающий источник	Holme, P. & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.	Barrat, A., Barthélemy, M., Pastor-Satorras, R., & Vespignani, A. (2004). The architecture of complex weighted networks. Proceedings of the National Academy of Sciences, 101(11), 3747–3752.
Другие названия	time-varying degree centrality, temporal degree centrality, evolving degree centrality, DDC	node strength, strength centrality, weighted node degree, WDC
Связанные	5	6
Сводка	Dynamic degree centrality extends the classical degree centrality measure to networks that change over time. Rather than counting a node's connections in a single static snapshot, it tracks how many contacts each node maintains across successive time windows or contact events, producing a time-resolved importance profile for every actor in the network.	Weighted degree centrality — also called node strength — extends the classic degree centrality measure to networks whose edges carry numeric weights. Instead of simply counting a node's connections, it sums the weights of all edges incident to that node, capturing both the volume and the intensity of a node's ties in a single, interpretable score.