Сравнение методов

	Стохастическая блочная модель	Анализ главных компонент
Область	Сетевой анализ	Машинное обучение
Семейство	Process / pipeline	Machine learning
Год появления	1983	2002
Автор метода	—	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Тип	Probabilistic generative graph model	Unsupervised dimensionality reduction
Основополагающий источник	Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
Другие названия	SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM)	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
Связанные	7	3
Сводка	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.	Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures.