Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Часова стохастична блокова модель× | Аналіз часової модулярності× | |
|---|---|---|
| Галузь | Мережевий аналіз | Мережевий аналіз |
| Родина | Machine learning | Machine learning |
| Рік появи≠ | 2014–2017 | 2010 |
| Автор методу≠ | Xu, K. S. & Hero, A. O.; Matias, C. & Miele, V. | Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. |
| Тип≠ | Generative probabilistic model | Community detection (temporal extension of modularity optimization) |
| Основоположне джерело≠ | Matias, C. & Miele, V. (2017). Statistical clustering of temporal networks through a dynamic stochastic block model. Journal of the Royal Statistical Society: Series B, 79(4), 1119–1141. 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 ↗ |
| Інші назви | TSBM, dynamic stochastic block model, time-varying SBM, evolving block model | dynamic modularity, time-varying modularity, longitudinal community detection, temporal community structure analysis |
| Пов'язані≠ | 4 | 5 |
| Підсумок≠ | The Temporal Stochastic Block Model (TSBM) extends the classic Stochastic Block Model to sequences of network snapshots, jointly inferring latent community memberships and how those memberships evolve across time. It combines a generative edge-probability model with a Markov process over block assignments, enabling principled statistical detection of community structure that changes over time. | Temporal modularity analysis extends standard modularity-based community detection to time-varying networks by treating each time slice as a network layer and coupling adjacent layers with inter-temporal links. This allows researchers to identify how communities form, persist, merge, split, and dissolve over time in dynamic relational data. |
| ScholarGateНабір даних ↗ |
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