  The aim of this paper is to analyse the gain of the update algorithm
associated to the recently proposed D-iteration: the D-iteration is a fluid
diffusion based new iterative method. It exploits a simple intuitive
decomposition of the product matrix-vector as elementary operations of fluid
diffusion (forward scheme) associated to a new algebraic representation. We
show through experimentations on real datasets how much this approach can
improve the computation efficiency in presence of the graph evolution.
