Salīdzināt metodes

	Beiziešu temporālās tīklu analīzes	Laika tīklu analīze
Nozare	Tīklu analīze	Tīklu analīze
Saime	Machine learning	Process / pipeline
Izcelsmes gads	2010s	2012
Autors	Hanneke, S.; Fu, W.; Xing, E. P. (among key contributors)	Holme & Saramäki (2012) — seminal framework
Tips	Probabilistic generative model	Dynamic graph analysis
Pirmavots	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.
Citi nosaukumi	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)
Saistītās	4	3
Kopsavilkums	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.