In this work1 we propose a new methodology in the eld of the Social Network Analysis based on Statis procedure developed by Escoufier (1980). In particular Statis is an extension over the time of the classical Principal Component Analysis (PCA) and we'll apply this procedure to explore the network dynamics. In this framework, the Statis procedure allows to synthetize the information contained in n matrices, defined as different "occasions", and to compute a special one that could be defined as the "compromise" matrix, which is specifically a sort of weighted synthesis from the initial one. The final step is obtaining the trajectory of each statistical unit (the nodes of a given network) over the time. In that sense we are trying to reduce dimensionally all the variables considered and the measures computed over the time, and we'll project the factors obtained on special artificial axis. So finally we'll reproduce the initial structure of data on the two projected axis, and we'll be able to identify the dynamics of the statistical units over the time (the nodes which are behaving abnormally). In the field of Finance and the Corporate Governance we analyze the relationships between interlocking directorship networks and the market capitalization, in Italy, over the period 1998-2006. Previous results on the phenomenon of interlocking directorship networks shows a strong level of persistence in the financial Italian network 1998-2006 in Santella, Drago, Polo (2007), and the existence of the same phenomenon across various countries in Europe (see Santella Drago Polo and Gagliardi (2008)). Here, by using the methodology, we found two important additional results: firstly we observed that seems does not exist a specific relationship between network centrality and the market capitalization, secondly we showed a specific group dynamics (both for centrality and market capitalization) for various companies over the time. This result seems to be consistent with the Literature in Corporate Governance by providing a specific quantification over the time. This methodology can be successfully applied to any type of Dynamic Social Network.