# Distribution while fitting weighted SBM

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## Distribution while fitting weighted SBM

 Hi Tiago, I have a short question regarding your implementation of the weighted SBM described here: https://arxiv.org/pdf/1708.01432.pdfhttps://graph-tool.skewed.de/static/doc/demos/inference/inference.html#edge-weights-and-covariatesDoes your implementation fit one distribution to sample edges per block combination or one global distribution? And how to retrieve the parameters of the distribution from a fitted model? We were trying to fit a model similar to the SBM in graspy used for simulation: https://graspy.neurodata.io/tutorials/simulations/sbm.htmlThere you can choose one distribution per block combination for simulation. I was figuring that this should also be possible when fitting the model. Thanks for your help in advance! -- Sent from: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/_______________________________________________ graph-tool mailing list [hidden email] https://lists.skewed.de/mailman/listinfo/graph-tool
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## Re: Distribution while fitting weighted SBM

 Administrator Am 04.06.20 um 10:09 schrieb kicasta: > Hi Tiago, > > I have a short question regarding your implementation of the weighted SBM > described here: > > https://arxiv.org/pdf/1708.01432.pdf> https://graph-tool.skewed.de/static/doc/demos/inference/inference.html#edge-weights-and-covariates> > Does your implementation fit one distribution to sample edges per block > combination or one global distribution? One distribution per pair of groups (otherwise the covariates and the group structure would be completely decoupled, which would be the same as ignoring them altogether.) > And how to retrieve the parameters > of the distribution from a fitted model? The distributions are "microcanonical", i.e. the parameters are quantities like the total sum of covariates, which is not allowed to fluctuate. For example the "exponential" distribution for nonegative covariates assumes that they are uniformly distributed among all possibilities that have the exact same sum. This means that the parameters need not be explicitly encoded. So, in this case, if you want to extract the parameter of the distribution, you just get the sum of covariates between any two pairs of groups. > We were trying to fit a model similar to the SBM in graspy used for > simulation: > > https://graspy.neurodata.io/tutorials/simulations/sbm.html> > There you can choose one distribution per block combination for simulation. > I was figuring that this should also be possible when fitting the model. Yes, this exactly what is described in the above paper, and what is implemented in graph-tool. Best, Tiago -- Tiago de Paula Peixoto <[hidden email]> _______________________________________________ graph-tool mailing list [hidden email] https://lists.skewed.de/mailman/listinfo/graph-tool signature.asc (849 bytes) Download Attachment -- Tiago de Paula Peixoto