Linganisha mbinu

	Uchambuzi wa Mitandao ya Kibayesia ya Muda	Uchanganuzi wa Mitandao ya Muda
Nyanja	Uchanganuzi wa Mitandao	Uchanganuzi wa Mitandao
Familia	Machine learning	Process / pipeline
Mwaka wa asili	2010s	2012
Mwanzilishi	Hanneke, S.; Fu, W.; Xing, E. P. (among key contributors)	Holme & Saramäki (2012) — seminal framework
Aina	Probabilistic generative model	Dynamic graph analysis
Chanzo asilia	Hanneke, S., Fu, W., & Xing, E. P. (2010). Discrete temporal models of social networks. Electronic Journal of Statistics, 4, 585–605.	Holme, P. & Saramäki, J. (2012). Temporal Networks. Physics Reports, 519(3), 97-125.
Majina mbadala	Bayesian dynamic network analysis, Bayesian time-varying network model, BTNA, Bayesian longitudinal network analysis	dynamic network analysis, time-varying network analysis, Zamansal Ağ Analizi (Temporal / Dynamic Networks)
Zinazohusiana	4	3
Muhtasari	Bayesian temporal network analysis combines probabilistic Bayesian inference with time-ordered relational data to model how network structures evolve, quantify uncertainty around structural estimates, and make principled predictions about future connectivity patterns. It provides credible intervals on edge probabilities and community assignments rather than bare point estimates.	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.