  The Google matrix is a positive, column-stochastic matrix that is used to
compute the pagerank of all the web pages on the Internet: the eigenvector
corresponding to the eigenvalue 1 is the pagerank vector. Due to its huge
dimension, of the order of billions, the (presently) only viable method to
compute the eigenvector is the power method. For the convergence of the
iteration, it is essential to know the eigenvalue distribution of the matrix. A
theorem concerning the eigenvalues was recently proved by Langville and Meyer.
In this note another proof is given.
