An S4 class to represent a degree corrected stochastic block model for co_clustering of bipartite graph, extends icl_model-class class. 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 $$S_k$$. This class mainly store the prior parameters value $$\alpha$$ of this generative model in the following slots (the prior parameter $$p$$ is estimated from the data as the global average probability of connection between two nodes):

## Slots

alpha

Dirichlet parameters for the prior over clusters proportions (default to 1)

p

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

## Examples

new("co_dcsbm")
#> An object of class "co_dcsbm"
#> Slot "p":
#> [1] NaN
#>
#> Slot "name":
#> [1] "co_dcsbm"
#>
#> Slot "alpha":
#> [1] 1
#> new("co_dcsbm", p = 0.1)
#> An object of class "co_dcsbm"
#> Slot "p":
#> [1] 0.1
#>
#> Slot "name":
#> [1] "co_dcsbm"
#>
#> Slot "alpha":
#> [1] 1
#>