方法对比

	Dynamic Stochastic Block Model	贝叶斯随机块模型
领域	网络分析	网络分析
方法族	Machine learning	Machine learning
起源年份	2011	2001–2014
提出者	Yang, T.; Chi, Y.; Zhu, S.; Gong, Y.; Jin, R.	Nowicki, K. & Snijders, T. A. B.; extended by Peixoto, T. P.
类型	Generative probabilistic model	Probabilistic generative model with Bayesian inference
开创性文献	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.
别名	DSBM, dynamic SBM, time-varying stochastic block model, temporal block model	Bayesian SBM, B-SBM, probabilistic block model, Bayesian community detection model
相关	5	5
摘要	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.