Linganisha mbinu

	Aina kuu ya Kielelezo Sanifu cha Kizuizi cha Kielektroniki (DSBM)	Stochastic Block Model
Nyanja	Uchanganuzi wa Mitandao	Uchanganuzi wa Mitandao
Familia	Machine learning	Process / pipeline
Mwaka wa asili	2011	1983
Mwanzilishi	Yang, T.; Chi, Y.; Zhu, S.; Gong, Y.; Jin, R.	—
Aina	Generative probabilistic model	Probabilistic generative graph model
Chanzo asilia	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.
Majina mbadala	DSBM, dynamic SBM, time-varying stochastic block model, temporal block model	SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM)
Zinazohusiana	5	7
Muhtasari	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.