I have 2 coexpression networks: (1) control phenotype; and (2) treatment phenotype.  They are using the same subset of genes so they are directly comparable (n=1700).  I used WGCNA to create 2 separate coexpression networks using the same soft power threshold (beta=10) for both.  This question is more conceptual than actual commands or tools to use but I am looking for methods that can allow me to compare the differences in network topology between the 2 phenotypes.  My first idea was to calculate the differential coexpression network as suggested in this paper https://www.nature.com/articles/srep13295 but it's a little weird to work with the resulting matrix.  The only other approach I could think of was to have a pairwise analysis of modules between each network to get an overlap measure. 

Does anyone know of any methods, tools, or approaches that work well for datasets like this?  
**My main research question is to identify the biggest changes in network topology between the 2 phenotypes.** 

Dataset description:

It's a dataset with 49 samples of patients without a disease and 34 with a disease.  28 of the patients have a twin in the dataset (n=28*2=56) and 27 do not have a twin account for in the dataset for a total of 56 + 27  = 83 samples/patients.  The gene subset I'm investigating is 1700 so each network consist of a 1700x1700 symmetrical similarity matrix. There is a separate network for the healthy state and a separate one for the diseased state.  
You could simultaneously cluster the two graphs using a tensor factorization approach. For more details, see some [notes from a workshop I taught][1].  


  [1]: http://jkh1.github.io/data_integration_and_mining_with_graphs-dynamic_graphs.html