Módszerek összehasonlítása

	Temporális Fokszám Központiság	Temporális él-központiság
Tudományterület	Hálózatelemzés	Hálózatelemzés
Módszercsalád	Machine learning	Machine learning
Keletkezés éve	2011–2012	2012
Megalkotó	Holme, P.; Saramaki, J.; Kim, H.; Anderson, R.	Kim, H. & Anderson, R.; Holme, P. & Saramäki, J.
Típus	Centrality measure (temporal extension)	Centrality measure for temporal networks
Alapmű	Holme, P. & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.	Holme, P., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.
Alternatív nevek	time-varying degree centrality, dynamic degree centrality, temporal node degree, TDC	TBC, time-varying betweenness centrality, dynamic betweenness centrality, time-respecting betweenness
Kapcsolódó	6	6
Összefoglaló	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 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.