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LCN: a random graph mixture model for community detection in functional brain networks
2017
Statistics and its Interface
The aim of this article is to develop a Bayesian random graph mixture model (RGMM) to detect the latent class network (LCN) structure of brain connectivity networks and estimate the parameters governing this structure. The use of conjugate priors for unknown parameters leads to efficient estimation, and a well-known nonidentifiability issue is avoided by a particular parameterization of the stochastic block model (SBM). Posterior computation proceeds via an efficient Markov Chain Monte Carlo
doi:10.4310/sii.2017.v10.n3.a1
pmid:29034059
pmcid:PMC5639930
fatcat:eirxxoufqfegtkpjcswhy37gvy