Salīdzināt metodes

	Laika Stohastiskais Bloku Modelis	Laika modulu analīze
Nozare	Tīklu analīze	Tīklu analīze
Saime	Machine learning	Machine learning
Izcelsmes gads	2014–2017	2010
Autors	Xu, K. S. & Hero, A. O.; Matias, C. & Miele, V.	Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P.
Tips	Generative probabilistic model	Community detection (temporal extension of modularity optimization)
Pirmavots	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.
Citi nosaukumi	TSBM, dynamic stochastic block model, time-varying SBM, evolving block model	dynamic modularity, time-varying modularity, longitudinal community detection, temporal community structure analysis
Saistītās	4	5
Kopsavilkums	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.