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 ?
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?
Thanks in advance.
On Thu, Apr 26, 2018 at 6:35 PM, Zahra Sheikhbahaee email@example.com wrote:
Thanks a lot for the advice!
On Thu, Apr 26, 2018 at 5:26 PM, Tiago de Paula Peixoto firstname.lastname@example.org wrote:
On 26.04.2018 15:29, Zahra Sheikhbahaee wrote:
In my network, beside to the information of which two nodes create an
I have the information of the time duration which an edge has lasted. I included this information as weight and used them as the covariate of the SBM. The results seems more reasonable compared to not
any weights. However, the number of blocks changes slightly in each
ran my script with the piece of code given before. So I was wondering
must run minimize_nested_blockmodel_dl function by determining the
number of MCMC iterations as argument, and then I would get more
results with highest confidence interval or I just need to repeat this function in a loop and then compute the mean number of blocks? I hope my question makes sense.
You should run the algorithm multiple times, and choose the result with the smallest description length. You get this value via the method state.entropy().
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