Porovnať metódy

	Dynamický stochastický blokový model	Stochastic Block Model
Odbor	Analýza sietí	Analýza sietí
Rodina	Machine learning	Process / pipeline
Rok vzniku	2011	1983
Tvorca	Yang, T.; Chi, Y.; Zhu, S.; Gong, Y.; Jin, R.	—
Typ	Generative probabilistic model	Probabilistic generative graph model
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.	Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137.
Ďalšie názvy	DSBM, dynamic SBM, time-varying stochastic block model, temporal block model	SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM)
Príbuzné	5	7
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.	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.