Сравнение методов

	Временная стохастическая блочная модель	Выявление временных сообществ
Область	Сетевой анализ	Сетевой анализ
Семейство	Machine learning	Machine learning
Год появления	2014–2017	2010
Автор метода	Xu, K. S. & Hero, A. O.; Matias, C. & Miele, V.	Mucha, P. J. et al.
Тип	Generative probabilistic model	Network clustering algorithm
Основополагающий источник	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.
Другие названия	TSBM, dynamic stochastic block model, time-varying SBM, evolving block model	dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection
Связанные	4	6
Сводка	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.