Vertaile menetelmiä
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| Dynaaminen satunnaisten verkkojen malli (TERGM / STERGM)× | Dynaaminen stokastinen lohkomalli× | |
|---|---|---|
| Tieteenala | Verkostoanalyysi | Verkostoanalyysi |
| Menetelmäperhe | Machine learning | Machine learning |
| Syntyvuosi≠ | 2010–2014 | 2011 |
| Kehittäjä≠ | Hanneke, Fu & Xing; Krivitsky & Handcock | Yang, T.; Chi, Y.; Zhu, S.; Gong, Y.; Jin, R. |
| Tyyppi≠ | Probabilistic graphical model (temporal) | Generative probabilistic model |
| Alkuperäislähde≠ | 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 ↗ |
| Rinnakkaisnimet | TERGM, Temporal ERGM, Dynamic ERGM, STERGM | DSBM, dynamic SBM, time-varying stochastic block model, temporal block model |
| Liittyvät≠ | 4 | 5 |
| Tiivistelmä≠ | 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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