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.
DcLbmPrior(p = NaN)DcLbm(alpha = 1, p = NaN)
a DcLbmPrior-class
a DcLbm-class object
Exponential prior parameter (default to Nan, in this case p will be estimated from data as the average intensities of X)
Dirichlet prior parameter over the cluster proportions (default to 1)
DcLbmFit-class, DcLbmPath-class
Other DlvmModels:
CombinedModels,
DcSbm,
DiagGmm,
DlvmPrior-class,
Gmm,
Lca,
MoM,
MoR,
MultSbm,
Sbm,
greed()
DcLbmPrior()
DcLbmPrior(p = 0.7)
DcLbm()
DcLbm(p = 0.7)
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