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| Dynamic Stochastic Block Model (DSBM)× | Bayesowski Model Bloków Stochastycznych× | |
|---|---|---|
| Dziedzina | Analiza sieci | Analiza sieci |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 2011 | 2001–2014 |
| Twórca≠ | Yang, T.; Chi, Y.; Zhu, S.; Gong, Y.; Jin, R. | Nowicki, K. & Snijders, T. A. B.; extended by Peixoto, T. P. |
| Typ≠ | Generative probabilistic model | Probabilistic generative model with Bayesian inference |
| Źródło pierwotne≠ | 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 ↗ | Peixoto, T. P. (2014). Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models. Physical Review E, 89(1), 012804. DOI ↗ |
| Inne nazwy | DSBM, dynamic SBM, time-varying stochastic block model, temporal block model | Bayesian SBM, B-SBM, probabilistic block model, Bayesian community detection model |
| Pokrewne | 5 | 5 |
| Podsumowanie≠ | 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 Bayesian Stochastic Block Model (Bayesian SBM) is a principled probabilistic method for community detection in networks. It treats group membership as a latent variable and uses Bayesian inference to simultaneously recover block structure and select the number of communities, avoiding the resolution-limit bias that plagues modularity-based approaches. |
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