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):



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


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


#> 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 #>