Sammenlign metoder

	Bayesiansk Stokastisk Blokmodel	Stokastisk blokmodel
Fagområde	Netværksanalyse	Netværksanalyse
Familie	Machine learning	Process / pipeline
Oprindelsesår	2001–2014	1983
Ophavsperson	Nowicki, K. & Snijders, T. A. B.; extended by Peixoto, T. P.	—
Type	Probabilistic generative model with Bayesian 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 SBM, B-SBM, probabilistic block model, Bayesian community detection model	SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM)
Relaterede	5	7
Resumé	The Bayesian Stochastic Block Model (Bayesian SBM) is a principled probabilistic method for community detection in networks. It treats group membership as a latent variable and uses Bayesian inference to simultaneously recover block structure and select the number of communities, avoiding the resolution-limit bias that plagues modularity-based approaches.	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.