MultSbm: Multinomial Stochastic Block Model Prior class

In comeetie/greed: Clustering and Model Selection with the Integrated Classification Likelihood

Multinomial Stochastic Block Model Prior class

Description

An S4 class to represent a Multinomial Stochastic Block Model. Such model can be used to cluster multi-layer graph vertex, and model a square adjacency cube X of size NxNxM with the following generative model :

π \sim Dirichlet(α)

Z_i \sim \mathcal{M}(1,π)

θ_{kl} \sim Dirichlet(β)

X_{ij.}|Z_{ik}Z_{jl}=1 \sim \mathcal{M}(L_{ij},θ_{kl})

With L_{ij}=∑_{m=1}^MX_{ijm}. These classes mainly store the prior parameters value α,β of this generative model. The MultSbm-class must be used when fitting a simple MultSbm whereas the MultSbmPrior-class must be sued when fitting a CombinedModels-class.

Usage

MultSbmPrior(beta = 1, type = "guess")

MultSbm(alpha = 1, beta = 1, type = "guess")

Arguments

beta	Dirichlet prior parameter over Multinomial links
type	define the type of networks (either "directed", "undirected" or "guess", default to "guess"), for undirected graphs the adjacency matrix is supposed to be symmetric.
alpha	Dirichlet prior parameter over the cluster proportions (default to 1)

Value

a MultSbmPrior-class object

a MultSbm-class object

See Also

MultSbmFit-class, MultSbmPath-class

Other DlvmModels: CombinedModels, DcLbm, DcSbm, DiagGmm, DlvmPrior-class, Gmm, Lca, MoM, MoR, Sbm, greed()

Examples

MultSbmPrior()
MultSbmPrior(type = "undirected")
MultSbm()
MultSbm(type = "undirected")

comeetie/greed documentation built on Oct. 10, 2022, 5:37 p.m.