Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Modelo de Bloco Estocástico× | Agrupamento Hierárquico× | |
|---|---|---|
| Área≠ | Análise de redes | Aprendizado de máquina |
| Família≠ | Process / pipeline | Machine learning |
| Ano de origem≠ | 1983 | 1963 |
| Autor original≠ | — | Ward, J. H. |
| Tipo≠ | Probabilistic generative graph model | Unsupervised clustering (agglomerative) |
| Fonte seminal≠ | Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137. DOI ↗ | Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244. DOI ↗ |
| Outros nomes≠ | SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM) | Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering |
| Relacionados≠ | 7 | 4 |
| Resumo≠ | 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. |
| ScholarGateConjunto de dados ↗ |
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