Сравнение методов

	Байесовский анализ социальных сетей	Стохастическая блочная модель
Область	Сетевой анализ	Сетевой анализ
Семейство	Machine learning	Process / pipeline
Год появления	2002	1983
Автор метода	Hoff, P. D.; Raftery, A. E.; Handcock, M. S.	—
Тип	Probabilistic / Bayesian network model	Probabilistic generative graph model
Основополагающий источник	Hoff, P. D., Raftery, A. E., & Handcock, M. S. (2002). Latent space approaches to social network analysis. Journal of the American Statistical Association, 97(460), 1090–1098.	Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137.
Другие названия	Bayesian SNA, Bayesian network modeling, probabilistic social network analysis, Bayesian relational modeling	SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM)
Связанные	5	7
Сводка	Bayesian Social Network Analysis applies Bayesian probabilistic inference to relational data, placing prior distributions over network parameters and updating them with observed tie data to yield full posterior distributions over structural features, tie probabilities, and latent actor positions. It enables principled uncertainty quantification in network models, making it especially valuable when data are sparse, partially observed, or subject to measurement error.	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.