Am 26.09.18 um 14:43 schrieb Adrien Dulac:
> 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.
It's not this simple, since the model is microcanonical and contains
hyperpriors, etc. The easiest thing you can do is compute the conditional
posterior distribution of an edge and its weight. You get this by adding the
missing edge with the desired weight to the graph, and computing the
difference in the state.entropy(), which gives the (un-normalized) negative
log probability (remember you have to copy the state with
state.copy(g=g_new), after modifying the graph). By normalizing this over
all weight values, you have the conditional posterior distribution of the
weight.
(This could be done faster by using BlockState.get_edges_prob(), but that
does not support edge covariates yet.)
Best,
Tiago
--
Tiago de Paula Peixoto <[email protected]>
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