  Over the past decade, various methods have been proposed for the
reconstruction of networks modeled as Gaussian Graphical Models. In this work,
we analyzed three different approaches: the Graphical Lasso (GLasso), the
Graphical Ridge (GGMridge), and the Local Partial Correlation (LPC). For the
evaluation of the methods, we used high dimensional data generated from
simulated random graphs (Erd\"os-R\'enyi, Barab\'asi-Albert, Watts-Strogatz).
The performance was assessed through the Receiver Operating Characteristic
(ROC) curve. In addition, the methods were used to reconstruct the
co-expression network for differentially expressed genes in human cervical
cancer data. The LPC method outperformed the GLasso in most simulated cases.
The GGMridge produced better ROC curves then both the other methods. Finally,
LPC and GGMridge obtained similar outcomes in real data studies.
