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