Linganisha mbinu

	Uchanganuzi wa Mitandao ya Muda yenye Uzito	Uchanganuzi wa Mitandao ya Muda
Nyanja	Uchanganuzi wa Mitandao	Uchanganuzi wa Mitandao
Familia	Machine learning	Process / pipeline
Mwaka wa asili	2004–2012	2012
Mwanzilishi	Holme, P. & Saramaki, J. (temporal networks); Barrat et al. (weighted networks)	Holme & Saramäki (2012) — seminal framework
Aina	Network analysis technique	Dynamic graph analysis
Chanzo asilia	Holme, P. & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.	Holme, P. & Saramäki, J. (2012). Temporal Networks. Physics Reports, 519(3), 97-125.
Majina mbadala	WTNA, weighted time-varying network analysis, weighted dynamic network analysis, weighted evolving network analysis	dynamic network analysis, time-varying network analysis, Zamansal Ağ Analizi (Temporal / Dynamic Networks)
Zinazohusiana	6	3
Muhtasari	Weighted temporal network analysis studies networks whose edges carry numerical weights — representing interaction strength, frequency, or intensity — and whose structure changes over time. It combines the time-varying perspective of temporal network analysis with the quantitative precision of weighted graph metrics, revealing not only when connections exist but how strong they are at each moment.	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.