Hi Tiago,
I have a short question regarding your implementation of the weighted SBM
described here:
https://arxiv.org/pdf/1708.01432.pdf
https://graph-tool.skewed.de/static/doc/demos/inference/inference.html#edge-weights-and-covariates
Does your implementation fit one distribution to sample edge
Am 26.05.20 um 12:10 schrieb Carlo Nicolini:
> More generally, what about introducing a split-apply-reduce framework to
> vertices and edges, or is it already possible with some modifications to
> `gt.condensation_graph`?
This indeed would be quite nice, and no it's not currently possible with
con
Am 28.05.20 um 01:48 schrieb timhannigan:
> Hi,
>
> I've been using graph-tool extensively over the past 6 months with
> hSBM_topic modeling (https://github.com/martingerlach/hSBM_Topicmodel; from
> the Gerlach, Peixoto & Altmann, 2018 paper). I have one small question that
> may dramatically impr
Am 02.06.20 um 21:53 schrieb rezccy:
> Hi,
>
> In the paper "Inferring the mesoscale structure of layered, edge-valued, and
> time-varying networks", you described the procedure of finding change points
> in layered networks. I am wondering if this function is implemented in the
> graph-tool libra
Dear James,
This is a very involved question, and it's a bit difficult to get to the
bottom of what you want.
I'll start with the last question, which I think can be addressed more
directly:
Am 03.06.20 um 20:35 schrieb James Ruffle:
> Alternatively, do you have any suggestions on how I would ac
Am 04.06.20 um 10:09 schrieb kicasta:
> Hi Tiago,
>
> I have a short question regarding your implementation of the weighted SBM
> described here:
>
> https://arxiv.org/pdf/1708.01432.pdf
> https://graph-tool.skewed.de/static/doc/demos/inference/inference.html#edge-weights-and-covariates
>
> Does
Am 03.06.20 um 10:26 schrieb Mathias Versichele:
> Anyone that can help me out ? There must be something I'm missing, or there
> must be an easy fix. Any help would be greatly appreciated.
This is a numerical precision problem. You have very low edge weights
(1e-6) combined with very large values
Awesome. Thanks for that.
One question though. Apart from the edge filter which will make Dijkstra
calculation faster, can I also get around storing lots of Inf values in an
edgepropertymap ? Suppose, I only want to route along highways. Right now, I
would have an edgepropertymap with 98% inf valu
Hi Tiago,
thanks, this is already quite helpful. Short follow-up:
Our final aim is to sample from the fitted model, also according to the
inferred edge covariate distributions between groups. Is this somehow possible
with graph-tool? (Up to now we were not able to do this.)
Would it, in any ca
Am 04.06.20 um 21:05 schrieb Dominik Schlechtweg:
> Hi Tiago,
>
> thanks, this is already quite helpful. Short follow-up:
>
> Our final aim is to sample from the fitted model, also according to the
> inferred edge covariate distributions between groups. Is this somehow
> possible with graph-too
Hi Tiago,
I tried to register to git.skewed.de using my email, in order to open an
issue as discussed in another thread, but never received the confirmation
email. Afterward, I tried to login via my github account, but since it has
the same email as my first attempt it didn´t let me; fair enough.
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