Am 01.04.20 um 02:40 schrieb Deklan Webster: > I was under the impression that passing a list corresponds to getting > the probability that *all* the edges are missing. Indeed, when I try it > out I get back a scalar not a np array. I want to collect the > probability that each individual edge is missing.
Yes, this is true.
> Also, with respect to the heuristics I mention, I just saw this paper
> "Evaluating Overfit and Underfit in Models of Network Community
> Structure" use "s_ij = θ_i *θ_ j* l_gi,gj"
This is not substantially faster that what is actually computed in
graph-tool, it is just less accurate.
> If sampling is not computationally feasible, this is what I had in mind.
>
> 1) Is there a way built into graph-tool to compute this similarity
> function efficiently? (i.e., without Python slowing me down)
You should switch to using
MeasuredBlockState.get_edge_prob()
which is implemented entirely in C++.
The whole approach described in
https://graph-tool.skewed.de/static/doc/demos/inference/inference.html#measured-networks
should be preferred over the older binary classification scheme, as it
completely subsumes it.
> 2) Is there a hierarchical analog, like just summing this similarity at
> each level?
Yes, any of the above approaches work in the exact say way with the
hierarchical model.
Best,
Tiago
--
Tiago de Paula Peixoto <[email protected]>
signature.asc
Description: OpenPGP digital signature
_______________________________________________ graph-tool mailing list [email protected] https://lists.skewed.de/mailman/listinfo/graph-tool
