Salīdzināt metodes

	Laika mērauklu īpašvērtību centrālās vērtības	Laika starpības centrālās vērtības noteikšana
Nozare	Tīklu analīze	Tīklu analīze
Saime	Machine learning	Machine learning
Izcelsmes gads	2011-2017	2012
Autors	Grindrod, P.; Higham, D. J.; Taylor, D. et al.	Kim, H. & Anderson, R.; Holme, P. & Saramäki, J.
Tips	Centrality measure for temporal networks	Centrality measure for temporal networks
Pirmavots	Grindrod, P., Parsons, M. C., Higham, D. J., & Estrada, E. (2011). Communicability across evolving networks. Physical Review E, 83(4), 046120.	Holme, P., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.
Citi nosaukumi	dynamic eigenvector centrality, time-varying eigenvector centrality, TEC, temporal communicability centrality	TBC, time-varying betweenness centrality, dynamic betweenness centrality, time-respecting betweenness
Saistītās	5	6
Kopsavilkums	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 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.