Porovnať metódy

	Dynamický stochastický blokový model	Analýza časových sietí
Odbor	Analýza sietí	Analýza sietí
Rodina	Machine learning	Process / pipeline
Rok vzniku	2011	2012
Tvorca	Yang, T.; Chi, Y.; Zhu, S.; Gong, Y.; Jin, R.	Holme & Saramäki (2012) — seminal framework
Typ	Generative probabilistic model	Dynamic graph analysis
Pôvodný zdroj	Yang, T., Chi, Y., Zhu, S., Gong, Y., & Jin, R. (2011). Detecting communities and their evolutions in dynamic social networks — a Bayesian approach. Machine Learning, 82(2), 157–189.	Holme, P. & Saramäki, J. (2012). Temporal Networks. Physics Reports, 519(3), 97-125.
Ďalšie názvy	DSBM, dynamic SBM, time-varying stochastic block model, temporal block model	dynamic network analysis, time-varying network analysis, Zamansal Ağ Analizi (Temporal / Dynamic Networks)
Príbuzné	5	3
Zhrnutie	The Dynamic Stochastic Block Model (DSBM) is a generative probabilistic framework that extends the static stochastic block model to networks observed across multiple time points. It jointly models community membership and community evolution, allowing researchers to detect and track latent groups and their structural changes over time in longitudinal network data.	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.