I've had a hard time interpreting resulting clusters of an adjacency matrix. I have 200 relatively big matrices representing subjects that contains partial correlations (z scores) of time series (neural data). The goal is to cluster those 210 matrices and detect any potential undiscovered communities. So I did another partial correlation calculations resulting in 200x200 adjacency matrix. Whenever I run a community detection algorithm (eg Newmann's) it comes up with hardly interpretable communities.
The question is that what kind of statistical tests that will tell if these communities or clusters are significant at all ? and if so, are there systematic ways to work out the interpretation ?