Salīdzināt metodes

	Stohastiskais bloku modelis	K-Means klasterizācija
Nozare	Tīklu analīze	Mašīnmācīšanās
Saime	Process / pipeline	Machine learning
Izcelsmes gads	1983	1967
Autors	—	MacQueen, J.
Tips	Probabilistic generative graph model	Partitional clustering (centroid-based)
Pirmavots	Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137.	MacQueen, J. (1967). Some Methods for Classification and Analysis of Multivariate Observations. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, 1, 281–297.
Citi nosaukumi	SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM)	K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering
Saistītās	7	3
Kopsavilkums	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.	K-Means Clustering is a centroid-based partitional clustering algorithm, traced to J. MacQueen in 1967, that splits data into k clusters by assigning each observation to its nearest cluster centre. It is widely used for marketing segmentation, customer grouping, and exploratory analysis.