পদ্ধতির তুলনা করুন

	স্টোকাস্টিক ব্লক মডেল	Hierarchical Clustering
ক্ষেত্র	নেটওয়ার্ক বিশ্লেষণ	যন্ত্র শিখন
পরিবার	Process / pipeline	Machine learning
উদ্ভবের বছর	1983	1963
প্রবর্তক	—	Ward, J. H.
ধরন	Probabilistic generative graph model	Unsupervised clustering (agglomerative)
মৌলিক উৎস	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.
অপর নাম	SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM)	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering
সম্পর্কিত	7	4
সারসংক্ষেপ	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.