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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