পদ্ধতির তুলনা করুন

	ডাইনামিক স্টোকাস্টিক ব্লক মডেল	বেইসিয়ান স্টোকাস্টিক ব্লক মডেল
ক্ষেত্র	নেটওয়ার্ক বিশ্লেষণ	নেটওয়ার্ক বিশ্লেষণ
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2011	2001–2014
প্রবর্তক	Yang, T.; Chi, Y.; Zhu, S.; Gong, Y.; Jin, R.	Nowicki, K. & Snijders, T. A. B.; extended by Peixoto, T. P.
ধরন	Generative probabilistic model	Probabilistic generative model with Bayesian inference
মৌলিক উৎস	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.
অপর নাম	DSBM, dynamic SBM, time-varying stochastic block model, temporal block model	Bayesian SBM, B-SBM, probabilistic block model, Bayesian community detection model
সম্পর্কিত	5	5
সারসংক্ষেপ	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.