Porovnat metody

	Temporální model stochastických bloků	Stochastický blokový model
Obor	Analýza sítí	Analýza sítí
Rodina	Machine learning	Process / pipeline
Rok vzniku	2014–2017	1983
Tvůrce	Xu, K. S. & Hero, A. O.; Matias, C. & Miele, V.	—
Typ	Generative probabilistic model	Probabilistic generative graph model
Původní zdroj	Matias, C. & Miele, V. (2017). Statistical clustering of temporal networks through a dynamic stochastic block model. Journal of the Royal Statistical Society: Series B, 79(4), 1119–1141.	Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137.
Další názvy	TSBM, dynamic stochastic block model, time-varying SBM, evolving block model	SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM)
Příbuzné	4	7
Shrnutí	The Temporal Stochastic Block Model (TSBM) extends the classic Stochastic Block Model to sequences of network snapshots, jointly inferring latent community memberships and how those memberships evolve across time. It combines a generative edge-probability model with a Markov process over block assignments, enabling principled statistical detection of community structure that changes over time.	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.