قارن الطرق

	مركزية المتجه الذاتي الزمني	تحليل الشبكات الاجتماعية الزمنية
المجال	تحليل الشبكات	تحليل الشبكات
العائلة	Machine learning	Machine learning
سنة النشأة	2011-2017	2000s–2010s
صاحب الطريقة	Grindrod, P.; Higham, D. J.; Taylor, D. et al.	Moody, J.; Holme, P.; Saramäki, J.
النوع	Centrality measure for temporal networks	Longitudinal network analysis
المصدر التأسيسي	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.
الأسماء البديلة	dynamic eigenvector centrality, time-varying eigenvector centrality, TEC, temporal communicability centrality	TSNA, longitudinal social network analysis, time-varying network analysis, dynamic SNA
ذات صلة	5	4
الملخص	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 Social Network Analysis (TSNA) extends classic social network analysis by treating networks as time-varying structures. Rather than aggregating all ties into a single static snapshot, TSNA tracks when ties form, persist, and dissolve, enabling researchers to study how social structures evolve and how dynamic connectivity shapes diffusion, influence, and inequality over time.