Dear Tiago,
thank you for this suggestion.
I tried but I am not
sure of the results that I got, maybe my computation is wrong ?
I proceeded as follows:
[...]
entropy = state.entropy()
e = g.add_edge(x,y)
g.ep.weights[e] = 42
new_state = state.copy(g=g, recs=[g.ep.weights], rec_types=['discrete-poisson'])
new_entropy = new_state.entropy()
# Here is the kind of value that I obtain given for the entropy in my working example
# where the graph has N = 167, E = 5787, max weight = 1458, mean weight = 14 (this is the manufacturing email network from KONECT")
entropy
Out[552]: 72938.4714059238
In [553]: new_entropy.entropy()
Out[553]: 109646.67346672397
Thus, as far as I
understand, to compute the conditional posterior distribution of
the weight I set, we do np.exp(entropy - new_entropy).
But as the difference is big, the exponential is always zero.
I tried with different nodes and weight but always obtain the same kind of results.
I wonder if there is not an error in my approach in order to compute the probability of a missing edge with a given covariate/weight ?
On 9/26/18 3:21 PM, Tiago de Paula Peixoto wrote:
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