Krahasoni metodat

	Modeli Stokastik Dinamik i Bllokut	Analiza e Rrjeteve Temporale
Fusha	Analiza e rrjeteve	Analiza e rrjeteve
Familja	Machine learning	Process / pipeline
Viti i origjinës	2011	2012
Krijuesi	Yang, T.; Chi, Y.; Zhu, S.; Gong, Y.; Jin, R.	Holme & Saramäki (2012) — seminal framework
Lloji	Generative probabilistic model	Dynamic graph analysis
Burimi themelues	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.
Emërtime të tjera	DSBM, dynamic SBM, time-varying stochastic block model, temporal block model	dynamic network analysis, time-varying network analysis, Zamansal Ağ Analizi (Temporal / Dynamic Networks)
Të lidhura	5	3
Përmbledhja	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.