Salīdzināt metodes

	Laika Stohastiskais Bloku Modelis	Daudzslāņu stohastiskais bloku modelis
Nozare	Tīklu analīze	Tīklu analīze
Saime	Machine learning	Machine learning
Izcelsmes gads	2014–2017	2015-2017
Autors	Xu, K. S. & Hero, A. O.; Matias, C. & Miele, V.	Peixoto, T. P.; De Bacco, C. and colleagues
Tips	Generative probabilistic model	Generative probabilistic model
Pirmavots	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.	Peixoto, T. P. (2015). Inferring the mesoscale structure of layered, edge-valued, and time-varying networks. Physical Review E, 92(4), 042807.
Citi nosaukumi	TSBM, dynamic stochastic block model, time-varying SBM, evolving block model	ML-SBM, multilayer SBM, multi-layer stochastic block model, multiplex stochastic block model
Saistītās	4	4
Kopsavilkums	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 Multilayer Stochastic Block Model (ML-SBM) is a generative probabilistic framework that extends the classical stochastic block model to networks with multiple relation types or layers. It simultaneously infers community structure and block-to-block connection probabilities across all layers, capturing how communities cohere differently depending on context or relationship type.