Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Aina kuu ya Kielelezo Sanifu cha Kizuizi cha Kielektroniki (DSBM)× | Utambuzi wa Jumuiya Zinazobadilika× | |
|---|---|---|
| Nyanja | Uchanganuzi wa Mitandao | Uchanganuzi wa Mitandao |
| Familia | Machine learning | Machine learning |
| Mwaka wa asili≠ | 2011 | 2010 (key formalization); earlier work 2002–2009 |
| Mwanzilishi≠ | Yang, T.; Chi, Y.; Zhu, S.; Gong, Y.; Jin, R. | Mucha, P. J. et al. (key formalization); earlier work by Girvan & Newman (2002) |
| Aina≠ | Generative probabilistic model | Graph clustering / community discovery |
| Chanzo asilia≠ | 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 ↗ |
| Majina mbadala | DSBM, dynamic SBM, time-varying stochastic block model, temporal block model | DCD, temporal community detection, evolving community detection, dynamic graph clustering |
| Zinazohusiana | 5 | 5 |
| Muhtasari≠ | 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. |
| ScholarGateSeti ya data ↗ |
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