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? If there is a class, is it still possible to consider a graph with weighted edges? Thanks again. Regards, Zahra On Mon, Jul 16, 2018 at 4:38 PM, Tiago de Paula Peixoto <[email protected]> wrote: > Am 16.07.2018 um 15:15 schrieb Zahra Sheikhbahaee: > > For the non-parametric weighted SBMs, how can I extract the "description > > length" from the the state.entropy() method? Is it also equivalent of > having > > the maximum entropy values after running the algorithm multiple times ? > > The entropy() method returns the negative joint log-likelihood of the data > and model parameters. For discrete data and model parameters, this equals > the description length. > > For the weighted SBM with continuous covariates, the data and model are no > longer discrete, so this value can no longer be called a description > length, > although it plays the same role. However, for discrete covariates, it is > the > description length. > > > I also have a theoretical question: I read most of your recent papers > and I > > see this statement but I could not find more description why it is the > case? > > Why do you use the "micro-canonical formulation"? You stated that "it > > approaches to the canonical distributions asymptotically". In case you > have > > explained it in one of your papers, would you kindly refer me to the > right > > paper? > > The microcanonical model is identical to the canonical model, if the latter > is integrated over its continuous parameters using uninformative priors, as > explained in detail here: > > https://arxiv.org/abs/1705.10225 > > Therefore, in a Bayesian setting, it makes no difference which one is used, > as they yield the same posterior distribution. > > The main reason to use the microcanonical formulation is that it makes it > easier to extend the Bayesian hierarchy, i.e. include deeper priors and > hyperpriors, thus achieving more robust models without a resolution limit, > accepting of arbitrary group sizes and degree distributions, etc. Within > the > canonical formulation, this is technically more difficult. > > 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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