Started in 1998, the search engine Google sorts pages using several parameters. PageRank is one of those. Precisely, PageRank is a distribution of probability on the web pages that depends on the web graph. Our purpose is to show that the PageRank can split into two terms, an internal and an external PageRank. These two PageRanks allow a better comprehension of the PageRank signification inside and outside a site. A first application is a local algorithm to estimate the PageRank of a given site pages. We will also show quantitative results on the possibilities for a site to boost its own PageRank.