On 23.02.2017 02:01, treinz wrote: > Hi all, > > I'm new to the graph theory field and graph-tool package. Can anyone help me > with the following questions on SBM of layered graph: > > 1) In the example shown in > https://graph-tool.skewed.de/static/doc/demos/inference/inference.html#edge-layers-and-covariates, > the edge covariates for the Les Misérables network is passed via g.ep.value: > > state = gt.minimize_blockmodel_dl(g, deg_corr=False, layers=True, > state_args=dict(ec=g.ep.value, > layers=False)) > > In this case, does the constructed layered model automatically detect how > many layers there should be in order to obtain a best fit SBM? If so, how > can one retrieve the layer membership of each edge? If not, is there a way > to do so in graph-tool via other function calls?
Each layer corresponds to a particular value of the g.ep.value property map,
which was passed as the `ec` parameter. There is no need to extract
anything, since this information was provided to the function in the first
place.
> 2) There's a so called 'independent layers' model discussed in the
> reference: Peixoto, T. P., Phys. Rev. E, 2015, 92, 042807 and it seems that
> setting state_args=dict(ec=g.ep.value, layers=True) in the example should
> use this model instead of the edge covariate model. But it seems from the
> paper that on is required to input the number of layers ('C' as in Fig. 3 of
> the reference). So how exactly should I use graph-tool to use the
> 'independent layers' model? Or is the algorithm capable of automatically
> detecting 'C' or the number of layers from the data?
The number of layers is determined automatically from the supplied `ec`
parameter.
Best,
Tiago
--
Tiago de Paula Peixoto <[email protected]>
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