Сравнение методов

	Temporal Betweenness Centrality	Временная центральность по степени
Область	Сетевой анализ	Сетевой анализ
Семейство	Machine learning	Machine learning
Год появления	2012	2011–2012
Автор метода	Kim, H. & Anderson, R.; Holme, P. & Saramäki, J.	Holme, P.; Saramaki, J.; Kim, H.; Anderson, R.
Тип	Centrality measure for temporal networks	Centrality measure (temporal extension)
Основополагающий источник	Holme, P., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.	Holme, P. & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.
Другие названия	TBC, time-varying betweenness centrality, dynamic betweenness centrality, time-respecting betweenness	time-varying degree centrality, dynamic degree centrality, temporal node degree, TDC
Связанные	6	6
Сводка	Temporal Betweenness Centrality (TBC) extends classical betweenness centrality to time-stamped networks by counting how often a node lies on time-respecting shortest paths — paths that traverse edges in chronological order. It identifies nodes that act as temporal brokers, controlling information or resource flow as it evolves over time, rather than in a static snapshot.	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.