The study of the interactions of cellular components is an essential base step to understand the structure and dynamics of biological networks. So, various methods were recently developed in this purpose. While most of them combine different types of data and ¡em¿apriori¡/em¿ knowledge, methods based on Graphical Gaussian Models are capable of learning the network directly from raw data. They consider the full-order partial correlations which are partial correlations between two variables given the remaining ones, for modelling direct links between variables. Statistical methods were developed for estimating these links when the number of observations is larger than the number of variables. However, the rapid advance of new technologies that allow to simultaneous measure genome expression, led to largescale datasets where the number of variables is far larger than the number of observations. To get round this dimensionality problem, different strategies and new statistical methods were proposed. In this study we focused on statistical methods recently published. All are based on the fact that the number of direct relationship between two variables is very small in regards to the number of possible relationships, ¡em¿p(p-1)/2¡/em¿. In the biological context, this assumption is not always satisfied over the whole graph. So it is essential to precisely know the behaviour of the methods in regards to the characteristics of the studied object before applying them. For this purpose, we evaluated the validity domain of each method from wide-ranging simulated datasets. We then illustrated our results using recently published biological data