Clustering Gaussian Graphical Models
This provides a practical solution for analyzing network structures in domains like neuroscience, though it appears incremental as it builds on existing graphical model clustering approaches.
The authors tackled the problem of clustering nodes in Gaussian graphical models from limited sample data by developing an efficient method that clusters nodes based on network neighborhood similarity using partial correlations, without needing to estimate covariance or precision matrices. They demonstrated the method on functional MRI data from the Human Connectome Project.
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.