Сравнение методов

	Многослойная стохастическая блочная модель	Стохастическая блочная модель
Область	Сетевой анализ	Сетевой анализ
Семейство	Machine learning	Process / pipeline
Год появления	2015-2017	1983
Автор метода	Peixoto, T. P.; De Bacco, C. and colleagues	—
Тип	Generative probabilistic model	Probabilistic generative graph model
Основополагающий источник	Peixoto, T. P. (2015). Inferring the mesoscale structure of layered, edge-valued, and time-varying networks. Physical Review E, 92(4), 042807.	Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137.
Другие названия	ML-SBM, multilayer SBM, multi-layer stochastic block model, multiplex stochastic block model	SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM)
Связанные	4	7
Сводка	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.	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.