পদ্ধতির তুলনা করুন

	Dynamic Exponential Random Graph Model	ডাইনামিক স্টোকাস্টিক ব্লক মডেল
ক্ষেত্র	নেটওয়ার্ক বিশ্লেষণ	নেটওয়ার্ক বিশ্লেষণ
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2010–2014	2011
প্রবর্তক	Hanneke, Fu & Xing; Krivitsky & Handcock	Yang, T.; Chi, Y.; Zhu, S.; Gong, Y.; Jin, R.
ধরন	Probabilistic graphical model (temporal)	Generative probabilistic model
মৌলিক উৎস	Hanneke, S., Fu, W., & Xing, E. P. (2010). Discrete temporal models of social networks. Electronic Journal of Statistics, 4, 585–605.	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.
অপর নাম	TERGM, Temporal ERGM, Dynamic ERGM, STERGM	DSBM, dynamic SBM, time-varying stochastic block model, temporal block model
সম্পর্কিত	4	5
সারসংক্ষেপ	The Dynamic Exponential Random Graph Model (TERGM / STERGM) extends the classic ERGM framework to panel network data, modeling how a network's ties form and dissolve over time as a function of structural tendencies, nodal attributes, and the network's own past state. It provides statistically principled inference about longitudinal network change.	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.