  We study the relation between PageRank and other parameters of information
networks such as in-degree, out-degree, and the fraction of dangling nodes. We
model this relation through a stochastic equation inspired by the original
definition of PageRank. Further, we use the theory of regular variation to
prove that PageRank and in-degree follow power laws with the same exponent. The
difference between these two power laws is in a multiple coefficient, which
depends mainly on the fraction of dangling nodes, average in-degree, the power
law exponent, and damping factor. The out-degree distribution has a minor
effect, which we explicitly quantify. Our theoretical predictions show a good
agreement with experimental data on three different samples of the Web.
