Bandingkan metode

	Model Blok Stokastik Dinamis	Model Blok Stokastik Bayesian
Bidang	Analisis Jaringan	Analisis Jaringan
Keluarga	Machine learning	Machine learning
Tahun asal	2011	2001–2014
Pencetus	Yang, T.; Chi, Y.; Zhu, S.; Gong, Y.; Jin, R.	Nowicki, K. & Snijders, T. A. B.; extended by Peixoto, T. P.
Tipe	Generative probabilistic model	Probabilistic generative model with Bayesian inference
Sumber perintis	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
Terkait	5	5
Ringkasan	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.