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3THE PAGERANK DERIVATIVE Winston Churchhill The aim of this thesis is to study the sensitivity of PageRank with respect to the damping parameter α. Sensitivity, though more general, is often examined via perturbation. And perturbation theory, applied to PageRank, attacks the question: will a small change in α produce a large change in the PageRank vector? For a sufficiently small change, it is the derivative that determines the behavior of any smooth function,1 and the examination begins there. Using the derivative is a fine starting point, provided it exists. Does it? As discussed in the previous chapter (sections 2.6 and 2.7), PageRank is a rational function of α for all 0 ≤ α ≤ 1. Thus, the derivative exists. It even exists for complex α where ∣α∣ < 1, though that is not an important fact for this thesis. Existence sets the stage for the exploration in this chapter. Because the derivative exists, section 3.1 evaluates different algebraic formulations for the derivative vector. With an algebraic expression in hand, section 3.2 next demonstrates a few ways to compute the derivative. PageRank derivatives are remarkably close problems to PageRank and the best algorithm involves only computing PageRank, using any algorithm, and computing a second strongly personalized PageRank vector, again using any algorithm. Algorithms, especially efficient ones, enable experiments. Sometimes, ex- periments even expose theory. The experiments with the PageRank derivative in section 3.3 follow this trajectory and expose a nice property of a Taylor step along the PageRank derivative. Theory, of course, is not everything and the final section investigates the predictive power of the PageRank derivative. Studying the derivative began independently around 2004 in three papers. First, Golub and Greif [2006] mentioned it in a 2004 preprint. Second, Boldi et al. [2005] included the derivative for an extrapolation technique. Third, and finally, Berkhin [2005] includes methods to compute the derivative of PageRank.2 Throughout the chapter, we freely inject discussions of related background material, though the algorithm to compute the derivative is novel, as are pieces 1 This follows from the Taylor series f(x)= f(x0)+(x−x0)f′(x)+ (1/2)(x−x0)2f′′(x)+...when x−x0 issmall.Insuchacase (x−x0)2 isminusculeand f′(x) determines the behavior. 47 There is nothing wrong with change, if it is in the right direction. 2 Although two of these are 2005 publications, most would have been submitted in 2004.PDF Image | Instagram Cheat Sheet
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