Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Detección dinámica de comunidades× | Modelo de Bloques Estocásticos× | |
|---|---|---|
| Campo | Análisis de redes | Análisis de redes |
| Familia≠ | Machine learning | Process / pipeline |
| Año de origen≠ | 2010 (key formalization); earlier work 2002–2009 | 1983 |
| Autor original≠ | Mucha, P. J. et al. (key formalization); earlier work by Girvan & Newman (2002) | — |
| Tipo≠ | Graph clustering / community discovery | Probabilistic generative graph model |
| Fuente seminal≠ | Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. (2010). Community structure in time-dependent, multiscale, and multiplex networks. Science, 328(5980), 876–878. DOI ↗ | Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137. DOI ↗ |
| Alias | DCD, temporal community detection, evolving community detection, dynamic graph clustering | SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM) |
| Relacionados≠ | 5 | 7 |
| Resumen≠ | Dynamic community detection identifies groups of densely connected nodes in networks that evolve over time, tracking how communities form, merge, split, and dissolve across temporal snapshots. Developed to extend static modularity optimization to time-varying structures, it is widely used in social, biological, and communication network research. | 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. |
| ScholarGateConjunto de datos ↗ |
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