Started in 1998, the search engine Google estimates page importance 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 be decomposed into two terms, internal and 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 PageR- ank inside a site. We will also show quantitative results on the possibilities for a site to boost its own PageRank.