Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Modelul Dinamic Exponențial de Grafuri Aleatorii× | Modelul Stocastic Dinamic de Blocuri× | |
|---|---|---|
| Domeniu | Analiza rețelelor | Analiza rețelelor |
| Familie | Machine learning | Machine learning |
| Anul apariției≠ | 2010–2014 | 2011 |
| Autorul original≠ | Hanneke, Fu & Xing; Krivitsky & Handcock | Yang, T.; Chi, Y.; Zhu, S.; Gong, Y.; Jin, R. |
| Tip≠ | Probabilistic graphical model (temporal) | Generative probabilistic model |
| Sursa seminală≠ | 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 ↗ |
| Denumiri alternative | TERGM, Temporal ERGM, Dynamic ERGM, STERGM | DSBM, dynamic SBM, time-varying stochastic block model, temporal block model |
| Înrudite≠ | 4 | 5 |
| Rezumat≠ | 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. |
| ScholarGateSet de date ↗ |
|
|