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