Methoden vergelijken

	Bayesiaans Stochastisch Blokmodel	Community Detection
Vakgebied	Netwerkanalyse	Netwerkanalyse
Familie	Machine learning	Process / pipeline
Jaar van ontstaan	2001–2014	2002–2019 (algorithm family)
Grondlegger	Nowicki, K. & Snijders, T. A. B.; extended by Peixoto, T. P.	Louvain: Blondel et al. (2008); Leiden: Traag et al. (2019); Girvan-Newman: Girvan & Newman (2002); Infomap: Rosvall & Bergstrom (2008)
Type	Probabilistic generative model with Bayesian inference	Graph-partitioning / clustering algorithm family
Oorspronkelijke bron	Peixoto, T. P. (2014). Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models. Physical Review E, 89(1), 012804.	Blondel, V.D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. (2008). Fast Unfolding of Communities in Large Networks. Journal of Statistical Mechanics, 2008(10), P10008.
Aliassen	Bayesian SBM, B-SBM, probabilistic block model, Bayesian community detection model	graph clustering, network partitioning, Topluluk Tespiti (Louvain, Girvan-Newman, Leiden)
Verwant	5	5
Samenvatting	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.	Community detection is a family of graph-partitioning algorithms that discover densely connected sub-groups — communities — within a network. First formalised through the modularity measure by Girvan and Newman (2002), the field advanced rapidly with the Louvain method (Blondel et al., 2008), the Leiden refinement (Traag et al., 2019), and the information-theoretic Infomap approach. All variants answer the same question: which nodes cluster together more tightly among themselves than with the rest of the network?