Linganisha mbinu

	Umuhimu wa Kielektroniki unaobadilika	Uchanganuzi wa Mitandao ya Muda
Nyanja	Uchanganuzi wa Mitandao	Uchanganuzi wa Mitandao
Familia	Machine learning	Process / pipeline
Mwaka wa asili	2010s	2012
Mwanzilishi	Lerman, K.; Ghosh, R.; Kang, J. H.	Holme & Saramäki (2012) — seminal framework
Aina	Centrality measure for time-evolving networks	Dynamic graph analysis
Chanzo asilia	Lerman, K., Ghosh, R., & Kang, J. H. (2010). Centrality metric for dynamic networks. Proceedings of the 8th Workshop on Mining and Learning with Graphs (MLG '10). ACM.	Holme, P. & Saramäki, J. (2012). Temporal Networks. Physics Reports, 519(3), 97-125.
Majina mbadala	temporal eigenvector centrality, time-varying eigenvector centrality, dynamic EC, evolving eigenvector centrality	dynamic network analysis, time-varying network analysis, Zamansal Ağ Analizi (Temporal / Dynamic Networks)
Zinazohusiana	4	3
Muhtasari	Dynamic eigenvector centrality extends the classic eigenvector centrality measure to networks that change over time. Rather than computing a single leading eigenvector on a static adjacency matrix, it tracks how a node's influence — defined by the importance of its neighbours — evolves across snapshots or time windows. The method is used in social network analysis, epidemiology, and information diffusion studies where network topology shifts continuously.	Temporal network analysis, formalised by Holme and Saramäki in their landmark 2012 Physics Reports survey, is the study of networks in which edges appear and disappear over time. Rather than collapsing all contacts into a single static graph, the approach preserves the precise timing of interactions — whether as contact sequences, time-stamped event lists, or windowed snapshots — and uses that timing to track how influence, disease, or information can actually propagate through the system.