Dear all,

I am a bit confused about the use of the weighted network models for a weight prediction task;

Suppose we have a weighted network where edges are integers. We fit a SBM with a Poisson kernel as follows:

data = gt.load_graph(...)  # The adjacency matrix has integer entries, and weights greater than zero are stored in data.ep.weights.
state = gt.inference.minimize_blockmodel(data, 
                                  B_min=10, B_max=10, 
                                  state_args= {'recs':[data.ep.weights], 'rec_types' : ["discrete-poisson"]})

My question, is how can we obtain, from state, a point estimate of the Poisson parameters in order to compute the distribution of the weights between pairs of nodes.

Regards,
Adrien Dulac