Comparer des méthodes

	Centralité par vecteur propre temporel	Centralité de degré temporel
Domaine	Analyse de réseaux	Analyse de réseaux
Famille	Machine learning	Machine learning
Année d'origine	2011-2017	2011–2012
Auteur d'origine	Grindrod, P.; Higham, D. J.; Taylor, D. et al.	Holme, P.; Saramaki, J.; Kim, H.; Anderson, R.
Type	Centrality measure for temporal networks	Centrality measure (temporal extension)
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. & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.
Alias	dynamic eigenvector centrality, time-varying eigenvector centrality, TEC, temporal communicability centrality	time-varying degree centrality, dynamic degree centrality, temporal node degree, TDC
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 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.