পদ্ধতির তুলনা করুন

	ডাইনামিক স্টোকাস্টিক ব্লক মডেল	Temporal Network Analysis
ক্ষেত্র	নেটওয়ার্ক বিশ্লেষণ	নেটওয়ার্ক বিশ্লেষণ
পরিবার	Machine learning	Process / pipeline
উদ্ভবের বছর	2011	2012
প্রবর্তক	Yang, T.; Chi, Y.; Zhu, S.; Gong, Y.; Jin, R.	Holme & Saramäki (2012) — seminal framework
ধরন	Generative probabilistic model	Dynamic graph analysis
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.	Holme, P. & Saramäki, J. (2012). Temporal Networks. Physics Reports, 519(3), 97-125.
অপর নাম	DSBM, dynamic SBM, time-varying stochastic block model, temporal block model	dynamic network analysis, time-varying network analysis, Zamansal Ağ Analizi (Temporal / Dynamic Networks)
সম্পর্কিত	5	3
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.	Temporal network analysis, formalised by Holme and Saramäki in their landmark 2012 Physics Reports survey, is the study of networks in which edges appear and disappear over time. Rather than collapsing all contacts into a single static graph, the approach preserves the precise timing of interactions — whether as contact sequences, time-stamped event lists, or windowed snapshots — and uses that timing to track how influence, disease, or information can actually propagate through the system.