TotalRank: Ranking Without Damping

PageRank is defined as the stationary state of a Markov chain obtained by perturbing the transition matrix of a web graph with a damping factor # that spreads part of the rank. The choice of # is eminently empirical, but most applications use # = 0.85; nonetheless, the selection of # is critical, and some believe that link farms may use this choice adversarially. Recent results [1] prove that the PageRank of a page is a rational function of #, and that this function can be approximated quite efficiently: this fact can be used to define a new form of ranking, TotalRank, that averages PageRanks over all possible #'s. We show how this rank can be computed efficiently, and provide some preliminary experimental results on its quality and comparisons with PageRank