مقایسهٔ روشها
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| شناسایی پویای جوامع× | مدل بلوک تصادفی× | |
|---|---|---|
| حوزه | تحلیل شبکه | تحلیل شبکه |
| خانواده≠ | Machine learning | Process / pipeline |
| سال پیدایش≠ | 2010 (key formalization); earlier work 2002–2009 | 1983 |
| پدیدآور≠ | Mucha, P. J. et al. (key formalization); earlier work by Girvan & Newman (2002) | — |
| نوع≠ | Graph clustering / community discovery | Probabilistic generative graph model |
| منبع بنیادین≠ | 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 ↗ | Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137. DOI ↗ |
| نامهای دیگر | DCD, temporal community detection, evolving community detection, dynamic graph clustering | SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM) |
| مرتبط≠ | 5 | 7 |
| خلاصه≠ | 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. | The Stochastic Block Model (SBM), introduced by Holland, Laskey and Leinhardt (1983), is a probabilistic generative model for graphs that assigns nodes to latent blocks and parametrically estimates the connection probabilities between blocks. It is the foundational approach for community detection, core-periphery identification, and hierarchical structure discovery in network analysis. |
| ScholarGateمجموعهداده ↗ |
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