Порівняння методів

	Динамічна стохастична блокова модель	Аналіз часових мереж
Галузь	Мережевий аналіз	Мережевий аналіз
Родина	Machine learning	Process / pipeline
Рік появи	2011	2012
Автор методу	Yang, T.; Chi, Y.; Zhu, S.; Gong, Y.; Jin, R.	Holme & Saramäki (2012) — seminal framework
Тип	Generative probabilistic model	Dynamic graph analysis
Основоположне джерело	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.
Інші назви	DSBM, dynamic SBM, time-varying stochastic block model, temporal block model	dynamic network analysis, time-varying network analysis, Zamansal Ağ Analizi (Temporal / Dynamic Networks)
Пов'язані	5	3
Підсумок	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.