Sammenlign metoder

	Dynamisk Stokastisk Blok Model	Stokastisk blokmodel
Fagområde	Netværksanalyse	Netværksanalyse
Familie	Machine learning	Process / pipeline
Oprindelsesår	2011	1983
Ophavsperson	Yang, T.; Chi, Y.; Zhu, S.; Gong, Y.; Jin, R.	—
Type	Generative probabilistic model	Probabilistic generative graph model
Oprindelig kilde	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.
Aliasser	DSBM, dynamic SBM, time-varying stochastic block model, temporal block model	SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM)
Relaterede	5	7
Resumé	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.