Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Динамическое обнаружение сообществ× | Стохастическая блочная модель× | |
|---|---|---|
| Область | Сетевой анализ | Сетевой анализ |
| Семейство≠ | Machine learning | Process / pipeline |
| Год появления≠ | 2010 (key formalization); earlier work 2002–2009 | 1983 |
| Автор метода≠ | Mucha, P. J. et al. (key formalization); earlier work by Girvan & Newman (2002) | — |
| Тип≠ | Graph clustering / community discovery | Probabilistic generative graph model |
| Основополагающий источник≠ | 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 ↗ |
| Другие названия | DCD, temporal community detection, evolving community detection, dynamic graph clustering | SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM) |
| Связанные≠ | 5 | 7 |
| Сводка≠ | 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. |
| ScholarGateНабор данных ↗ |
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