Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Динамическая стохастическая блочная модель× | Динамическое обнаружение сообществ× | |
|---|---|---|
| Область | Сетевой анализ | Сетевой анализ |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 2011 | 2010 (key formalization); earlier work 2002–2009 |
| Автор метода≠ | Yang, T.; Chi, Y.; Zhu, S.; Gong, Y.; Jin, R. | Mucha, P. J. et al. (key formalization); earlier work by Girvan & Newman (2002) |
| Тип≠ | Generative probabilistic model | Graph clustering / community discovery |
| Основополагающий источник≠ | Yang, T., Chi, Y., Zhu, S., Gong, Y., & Jin, R. (2011). Detecting communities and their evolutions in dynamic social networks — a Bayesian approach. Machine Learning, 82(2), 157–189. DOI ↗ | 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 ↗ |
| Другие названия | DSBM, dynamic SBM, time-varying stochastic block model, temporal block model | DCD, temporal community detection, evolving community detection, dynamic graph clustering |
| Связанные | 5 | 5 |
| Сводка≠ | The Dynamic Stochastic Block Model (DSBM) is a generative probabilistic framework that extends the static stochastic block model to networks observed across multiple time points. It jointly models community membership and community evolution, allowing researchers to detect and track latent groups and their structural changes over time in longitudinal network data. | 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. |
| ScholarGateНабор данных ↗ |
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