Compară metode

	Modelul Blocurilor Stocastice (SBM)	DBSCAN	Clustering Ierarhic
Domeniu	Analiza rețelelor	Învățare automată	Învățare automată
Familie	Process / pipeline	Machine learning	Machine learning
Anul apariției	1983	1996	1963
Autorul original	—	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	Ward, J. H.
Tip	Probabilistic generative graph model	Density-based clustering algorithm	Unsupervised clustering (agglomerative)
Sursa seminală	Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137.	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of the 2nd KDD, 226–231.	Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244.
Denumiri alternative	SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM)	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering
Înrudite	7	3	4
Rezumat	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.	DBSCAN is a density-based clustering algorithm, introduced by Ester, Kriegel, Sander and Xu in 1996, that groups together points lying in dense regions and flags points in sparse regions as noise. It is effective on noisy data and on clusters of irregular, non-spherical shapes.	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.