Порівняння методів

	Зважена стохастична блокова модель	Стохастична блокова модель
Галузь	Мережевий аналіз	Мережевий аналіз
Родина	Machine learning	Process / pipeline
Рік появи	2014	1983
Автор методу	Aicher, C.; Jacobs, A. Z.; Clauset, A.	—
Тип	Generative probabilistic model	Probabilistic generative graph model
Основоположне джерело	Aicher, C., Jacobs, A. Z., & Clauset, A. (2014). Learning latent block structure in weighted networks. Journal of Complex Networks, 3(2), 221–248.	Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137.
Інші назви	W-SBM, weighted SBM, weighted block model, weighted community detection via SBM	SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM)
Пов'язані	6	7
Підсумок	The Weighted Stochastic Block Model (W-SBM) extends the classical stochastic block model to networks whose edges carry numerical weights. By positing that edge weights between node pairs arise from distributions that depend on the block memberships of those nodes, it simultaneously infers a partition of nodes into communities and a set of block-to-block weight parameters — recovering structure invisible to unweighted methods.	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.