Vertaile menetelmiä

	Stochastic Block Model	Hierarkkinen ryvästyminen
Tieteenala	Verkostoanalyysi	Koneoppiminen
Menetelmäperhe	Process / pipeline	Machine learning
Syntyvuosi	1983	1963
Kehittäjä	—	Ward, J. H.
Tyyppi	Probabilistic generative graph model	Unsupervised clustering (agglomerative)
Alkuperäislähde	Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137.	Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244.
Rinnakkaisnimet	SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM)	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering
Liittyvät	7	4
Tiivistelmä	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.	Hierarchical clustering is an unsupervised method that groups observations into nested clusters and draws the result as a dendrogram, so the number of clusters need not be fixed in advance. Its agglomerative form rests on the objective-function grouping criterion introduced by Joe Ward in 1963.