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| 动态指数随机图模型× | Dynamic Stochastic Block Model× | |
|---|---|---|
| 领域 | 网络分析 | 网络分析 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2010–2014 | 2011 |
| 提出者≠ | Hanneke, Fu & Xing; Krivitsky & Handcock | Yang, T.; Chi, Y.; Zhu, S.; Gong, Y.; Jin, R. |
| 类型≠ | Probabilistic graphical model (temporal) | Generative probabilistic model |
| 开创性文献≠ | Hanneke, S., Fu, W., & Xing, E. P. (2010). Discrete temporal models of social networks. Electronic Journal of Statistics, 4, 585–605. DOI ↗ | 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 ↗ |
| 别名 | TERGM, Temporal ERGM, Dynamic ERGM, STERGM | DSBM, dynamic SBM, time-varying stochastic block model, temporal block model |
| 相关≠ | 4 | 5 |
| 摘要≠ | The Dynamic Exponential Random Graph Model (TERGM / STERGM) extends the classic ERGM framework to panel network data, modeling how a network's ties form and dissolve over time as a function of structural tendencies, nodal attributes, and the network's own past state. It provides statistically principled inference about longitudinal network change. | 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. |
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