Sammenlign metoder

	Bayesiansk fællesskabsdetektion	Stokastisk blokmodel
Fagområde	Netværksanalyse	Netværksanalyse
Familie	Machine learning	Process / pipeline
Oprindelsesår	2001–2014	1983
Ophavsperson	Nowicki, K. & Snijders, T. A. B. (formal Bayesian framing); extended by Peixoto, T. P.	—
Type	Probabilistic generative model / inference	Probabilistic generative graph model
Oprindelig kilde	Peixoto, T. P. (2014). Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models. Physical Review E, 89(1), 012804.	Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137.
Aliasser	Bayesian graph clustering, probabilistic community detection, Bayesian stochastic block model community detection, Bayesian network partitioning	SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM)
Relaterede	5	7
Resumé	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.	The Stochastic Block Model (SBM), introduced by Holland, Laskey and Leinhardt (1983), is a probabilistic generative model for graphs that assigns nodes to latent blocks and parametrically estimates the connection probabilities between blocks. It is the foundational approach for community detection, core-periphery identification, and hierarchical structure discovery in network analysis.