Bandingkan metode

	Model Blok Stokastik Temporal	Analisis Modularitas 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., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P.
Tipe	Generative probabilistic model	Community detection (temporal extension of modularity optimization)
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 modularity, time-varying modularity, longitudinal community detection, temporal community structure analysis
Terkait	4	5
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 modularity analysis extends standard modularity-based community detection to time-varying networks by treating each time slice as a network layer and coupling adjacent layers with inter-temporal links. This allows researchers to identify how communities form, persist, merge, split, and dissolve over time in dynamic relational data.