  We derive an efficient method to perform clustering of nodes in Gaussian
graphical models directly from sample data. Nodes are clustered based on the
similarity of their network neighborhoods, with edge weights defined by partial
correlations. In the limited-data scenario, where the covariance matrix would
be rank-deficient, we are able to make use of matrix factors, and never need to
estimate the actual covariance or precision matrix. We demonstrate the method
on functional MRI data from the Human Connectome Project. A matlab
implementation of the algorithm is provided.
