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	Bayesowskie wykrywanie społeczności	Detekcja społeczności w czasie
Dziedzina	Analiza sieci	Analiza sieci
Rodzina	Machine learning	Machine learning
Rok powstania	2001–2014	2010
Twórca	Nowicki, K. & Snijders, T. A. B. (formal Bayesian framing); extended by Peixoto, T. P.	Mucha, P. J. et al.
Typ	Probabilistic generative model / inference	Network clustering algorithm
Źródło pierwotne	Peixoto, T. P. (2014). Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models. Physical Review E, 89(1), 012804.	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.
Inne nazwy	Bayesian graph clustering, probabilistic community detection, Bayesian stochastic block model community detection, Bayesian network partitioning	dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection
Pokrewne	5	6
Podsumowanie	Bayesian community detection infers latent group structure in networks by treating community membership as unobserved variables and using Bayesian inference — typically via Markov chain Monte Carlo or variational methods — to compute a posterior distribution over all plausible partitions. Unlike modularity optimisation, it selects the number of communities from data and provides principled uncertainty estimates for every node assignment.	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.