Bandingkan metode

	Model Blok Stokastik Temporal	Deteksi Komunitas Temporal
Bidang	Analisis Jaringan	Analisis Jaringan
Keluarga	Machine learning	Machine learning
Tahun asal	2014–2017	2010
Pencetus	Xu, K. S. & Hero, A. O.; Matias, C. & Miele, V.	Mucha, P. J. et al.
Tipe	Generative probabilistic model	Network clustering algorithm
Sumber perintis	Matias, C. & Miele, V. (2017). Statistical clustering of temporal networks through a dynamic stochastic block model. Journal of the Royal Statistical Society: Series B, 79(4), 1119–1141.	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	TSBM, dynamic stochastic block model, time-varying SBM, evolving block model	dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection
Terkait	4	6
Ringkasan	The Temporal Stochastic Block Model (TSBM) extends the classic Stochastic Block Model to sequences of network snapshots, jointly inferring latent community memberships and how those memberships evolve across time. It combines a generative edge-probability model with a Markov process over block assignments, enabling principled statistical detection of community structure that changes over time.	Temporal community detection identifies cohesive groups (communities) in networks whose structure changes over time. By treating each time snapshot as a network layer and coupling consecutive layers, it reveals how communities form, merge, split, grow, or dissolve — turning a sequence of static snapshots into a continuous narrative of group evolution.