방법 비교

	동적 확률 블록 모형	시간적 네트워크 분석
분야	네트워크 분석	네트워크 분석
계열	Machine learning	Process / pipeline
기원 연도	2011	2012
창시자	Yang, T.; Chi, Y.; Zhu, S.; Gong, Y.; Jin, R.	Holme & Saramäki (2012) — seminal framework
유형	Generative probabilistic model	Dynamic graph analysis
원전	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.
별칭	DSBM, dynamic SBM, time-varying stochastic block model, temporal block model	dynamic network analysis, time-varying network analysis, Zamansal Ağ Analizi (Temporal / Dynamic Networks)
관련	5	3
요약	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.