Hi Tiago,


Thanks for the reply. In the section (VI) of your paper "Inferring the mesoscale structure of layered, edge-valued and time-varying networks", you used the layered stochastic block model for a temporal network. I have a similar data set which I do not want to fix the membership for the nodes of different layers to the same block over all layers (nodes can change their block memberships  over time). I am wondering again how I can use graph tool for this case?  Which method or constructor should I use?

Regards,
Zahra

On Wed, Jul 18, 2018 at 1:07 PM, Tiago de Paula Peixoto <tiago@skewed.de> wrote:
Am 16.07.2018 um 22:46 schrieb Zahra Sheikhbahaee:
> Hi Tiago,
>
> Thanks for the explanation. I have another question:
>
> In the "Inferring the mesoscale structure of layered, edge-valued and
> time-varying networks", you compared two way of constructing layered
> structures: first approach: You assumed an adjacency matrix in each
> independent layer. The second method, the collapsed graph considered as a
> result of merging all the adjacency matrices together.
>
> I am wondering how I can use graph_tool for the first method? Which method
> or class should I use?

You have to pass the option "layers=True" to the LayeredBlockState constructor:

https://graph-tool.skewed.de/static/doc/inference.html#graph_tool.inference.layered_blockmodel.LayeredBlockState

> If there is a class, is it still possible to consider
> a graph with weighted edges?

Yes, it accepts 'recs/rec_types/rec_params' just like the regular BlockState.

Best,
Tiago

--
Tiago de Paula Peixoto <tiago@skewed.de>
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