Сравнение методов

	Динамическая стохастическая блочная модель	Стохастическая блочная модель
Область	Сетевой анализ	Сетевой анализ
Семейство	Machine learning	Process / pipeline
Год появления	2011	1983
Автор метода	Yang, T.; Chi, Y.; Zhu, S.; Gong, Y.; Jin, R.	—
Тип	Generative probabilistic model	Probabilistic generative graph model
Основополагающий источник	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.	Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137.
Другие названия	DSBM, dynamic SBM, time-varying stochastic block model, temporal block model	SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM)
Связанные	5	7
Сводка	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.	The Stochastic Block Model (SBM), introduced by Holland, Laskey and Leinhardt (1983), is a probabilistic generative model for graphs that assigns nodes to latent blocks and parametrically estimates the connection probabilities between blocks. It is the foundational approach for community detection, core-periphery identification, and hierarchical structure discovery in network analysis.