SBM on correlation based network

[Fuschi]

Hello!
First of all thank you for the really interesting package, I'm a beginner in sbm and I would like to ask you for suggestions on this topics. I am interested in the search for latent group networks built starting from a correlation matrix and obtaining the adjacency matrix by thresholding the absolute values of correlation; therefore each link is associated with a weight that can vary from -1 to 1.
I would like to ask you if it was possible to adapt the functions present in the package to this type of correlation networks and my main doubts are:

• What is the "best input" I can give to the estimate sbm functions, should I consider only significant positive links and binarize them into 0,1 for example?
• Is it possible to keep the information of negative links as well? I would like if the vertices are connected by negative weighted links, then they are also labeled in different groups (like a sort of repulsion between them). Can I introduce those informations in the covariates argument?

Thanks in advance and have a nice day.

[jchiquet]

Sorry for the late reply.

In my opinion, you should transform the data from correlation matrix using a Fisher type transformation (https://en.wikipedia.org/wiki/Fisher_transformation) and then apply an SBM with a Gaussian emission law.
Thus, you will keep all the information available on the edges

netMat <- atanh(my_corr_mat)
 mySBM <-    estimateSimpleSBM(
       netMat,
       model = "gaussian",
       directed = FALSE
     )