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| Dinamikus Sztochasztikus Blokk Modell× | Sztochasztikus Blokk Modell× | |
|---|---|---|
| Tudományterület | Hálózatelemzés | Hálózatelemzés |
| Módszercsalád≠ | Machine learning | Process / pipeline |
| Keletkezés éve≠ | 2011 | 1983 |
| Megalkotó≠ | Yang, T.; Chi, Y.; Zhu, S.; Gong, Y.; Jin, R. | — |
| Típus≠ | Generative probabilistic model | Probabilistic generative graph model |
| Alapmű≠ | 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 ↗ | Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137. DOI ↗ |
| Alternatív nevek | DSBM, dynamic SBM, time-varying stochastic block model, temporal block model | SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM) |
| Kapcsolódó≠ | 5 | 7 |
| Összefoglaló≠ | 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. | 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. |
| ScholarGateAdatkészlet ↗ |
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