Bandingkan metode

	Model Blok Stokastik Dinamis	Deteksi Komunitas Dinamis
Bidang	Analisis Jaringan	Analisis Jaringan
Keluarga	Machine learning	Machine learning
Tahun asal	2011	2010 (key formalization); earlier work 2002–2009
Pencetus	Yang, T.; Chi, Y.; Zhu, S.; Gong, Y.; Jin, R.	Mucha, P. J. et al. (key formalization); earlier work by Girvan & Newman (2002)
Tipe	Generative probabilistic model	Graph clustering / community discovery
Sumber perintis	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.	Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. (2010). Community structure in time-dependent, multiscale, and multiplex networks. Science, 328(5980), 876–878.
Alias	DSBM, dynamic SBM, time-varying stochastic block model, temporal block model	DCD, temporal community detection, evolving community detection, dynamic graph clustering
Terkait	5	5
Ringkasan	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.	Dynamic community detection identifies groups of densely connected nodes in networks that evolve over time, tracking how communities form, merge, split, and dissolve across temporal snapshots. Developed to extend static modularity optimization to time-varying structures, it is widely used in social, biological, and communication network research.