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 <[email protected]> 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 <[email protected]> > _______________________________________________ > graph-tool mailing list > [email protected] > https://lists.skewed.de/mailman/listinfo/graph-tool >
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