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	Stochastic Block Model	Grafová neurónová sieť	Hierarchické zhlukovanie
Odbor	Analýza sietí	Hlboké učenie	Strojové učenie
Rodina	Process / pipeline	Machine learning	Machine learning
Rok vzniku	1983	2017	1963
Tvorca	—	Kipf, T.N. & Welling, M.	Ward, J. H.
Typ	Probabilistic generative graph model	Deep learning on graph-structured data	Unsupervised clustering (agglomerative)
Pôvodný zdroj	Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137.	Kipf, T.N. & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. ICLR.	Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244.
Ďalšie názvy	SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM)	Grafik Sinir Ağı (GNN), GNN, graph neural net, graph convolutional network	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering
Príbuzné	7	4	4
Zhrnutie	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.	A Graph Neural Network (GNN) is a deep learning method, popularised by Kipf and Welling in 2017 with the Graph Convolutional Network, that learns from the relationships in network (graph) structures made of nodes and edges. It is designed for data that is naturally relational, such as social networks, molecular structures, and recommendation systems.	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.