Comparer des méthodes

	Centralité par vecteur propre temporel	Centralité d'intermédiarité temporelle
Domaine	Analyse de réseaux	Analyse de réseaux
Famille	Machine learning	Machine learning
Année d'origine	2011-2017	2012
Auteur d'origine	Grindrod, P.; Higham, D. J.; Taylor, D. et al.	Kim, H. & Anderson, R.; Holme, P. & Saramäki, J.
Type	Centrality measure for temporal networks	Centrality measure for temporal networks
Source fondatrice	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.
Alias	dynamic eigenvector centrality, time-varying eigenvector centrality, TEC, temporal communicability centrality	TBC, time-varying betweenness centrality, dynamic betweenness centrality, time-respecting betweenness
Apparentées	5	6
Résumé	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.