DcLbm {greed}

Degree Corrected Latent Block Model for bipartite graph class

Description

An S4 class to represent a degree corrected stochastic block model for co_clustering of bipartite graph. Such model can be used to cluster graph vertex, and model a bipartite graph adjacency matrix X with the following generative model :

\pi \sim Dirichlet(\alpha)

Z_i^r \sim \mathcal{M}(1,\pi^r)

Z_j^c \sim \mathcal{M}(1,\pi^c)

\theta_{kl} \sim Exponential(p)

\gamma_i^r\sim \mathcal{U}(S_k)

\gamma_i^c\sim \mathcal{U}(S_l)

X_{ij}|Z_{ik}^cZ_{jl}^r=1 \sim \mathcal{P}(\gamma_i^r\theta_{kl}\gamma_j^c)

The individuals parameters \gamma_i^r,\gamma_j^c allow to take into account the node degree heterogeneity. These parameters have uniform priors over simplex S_k. These classes mainly store the prior parameters value \alpha,p of this generative model. The DcLbm-class must be used when fitting a simple Diagonal Gaussian Mixture Model whereas the DcLbmPrior-class must be sued when fitting a CombinedModels-class.

Usage

DcLbmPrior(p = NaN)

DcLbm(alpha = 1, p = NaN)

Arguments

p

Exponential prior parameter (default to Nan, in this case p will be estimated from data as the average intensities of X)

alpha

Dirichlet prior parameter over the cluster proportions (default to 1)

Value

a DcLbmPrior-class

a DcLbm-class object

See Also

DcLbmFit-class, DcLbmPath-class

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

Examples

DcLbmPrior()
DcLbmPrior(p = 0.7)
DcLbm()
DcLbm(p = 0.7)