Confronta i metodi

	Modello Stocastico a Blocchi Dinamico	Modello Bayesiano a Blocchi Stocastici
Campo	Analisi delle reti	Analisi delle reti
Famiglia	Machine learning	Machine learning
Anno di origine	2011	2001–2014
Ideatore	Yang, T.; Chi, Y.; Zhu, S.; Gong, Y.; Jin, R.	Nowicki, K. & Snijders, T. A. B.; extended by Peixoto, T. P.
Tipo	Generative probabilistic model	Probabilistic generative model with Bayesian inference
Fonte seminale	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.	Peixoto, T. P. (2014). Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models. Physical Review E, 89(1), 012804.
Alias	DSBM, dynamic SBM, time-varying stochastic block model, temporal block model	Bayesian SBM, B-SBM, probabilistic block model, Bayesian community detection model
Correlati	5	5
Sintesi	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.