  In this paper we present a new method that can accelerate the computation of
the PageRank importance vector. Our method, called D-Iteration (DI), is based
on the decomposition of the matrix-vector product that can be seen as a fluid
diffusion model and is potentially adapted to asynchronous implementation. We
give theoretical results about the convergence of our algorithm and we show
through experimentations on a real Web graph that DI can improve the
computation efficiency compared to other classical algorithm like Power
Iteration, Gauss-Seidel or OPIC.
