Bandingkan kaedah

	Model Blok Rawak Stokastik Dinamik	Stochastic Block Model
Bidang	Analisis Rangkaian	Analisis Rangkaian
Keluarga	Machine learning	Process / pipeline
Tahun asal	2011	1983
Pengasas	Yang, T.; Chi, Y.; Zhu, S.; Gong, Y.; Jin, R.	—
Jenis	Generative probabilistic model	Probabilistic generative graph model
Sumber perintis	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.
Alias	DSBM, dynamic SBM, time-varying stochastic block model, temporal block model	SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM)
Berkaitan	5	7
Ringkasan	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.