[graph-tool] Question regarding layered SBM

Tiago de Paula Peixoto tiago at skewed.de
Thu Feb 23 19:39:26 CET 2017


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 <tiago at skewed.de>

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