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Instead of using these approaches, we devise a method to compute the PageRank vector analytically by solving two PageRank problems. The key to this result is an observation by Golub and Greif [2006]: Px(α)−v = 1(x(α)−v). α We use this result to rewrite equation (3.1) as (I − αP)x′(α) = 1 (x(α) − v). (3.4) (3.5) α The vector x′(α) then decomposes into a linear combination of two PageRank vectors: x′(α) = βz(α) − βx(α), (3.6) where(I−αP)z(α)=(1−α)x(α)andβ= 1 . Thisideayieldsan α(1−α) algorithm for computing the PageRank derivative as the solution of two PageRank systems with different teleportation distribution vectors but the same value of α. Berkhin [2005] also made this observation. This reduction is progress. It takes advantage of the structure of the deriva- tive to express it as a combination of PageRank solutions. Recall, however, that efficient algorithms for PageRank operate at the level of PageRank vari- ants. One concern with the previous approach is that it requires computing PageRank for a column stochastic matrix P. As discussed in section 2.2.2, many codes for PageRank choose to work with the column sub-stochastic ma- trix P ̄ and use the strongly preferential PageRank model with P = P ̄ + vdT .12 To maximize our computational advantage, we want to solve only strongly- preferential PageRank problems when starting with a strongly-preferential PageRank problem.13 Virtually all PageRank solvers work with P ̄ and implic- itly use the strongly preferential framework, whereas relatively few work with P or the weakly preferential framework. 12 A column sub-stochastic matrix satisfies (eTP ̄)=⎧⎪⎨1 Pi,j>0foranyi i ⎪⎩0 Pi,j=0foralli. 13 For general problems, it will be hard to do better than using the reduction to problems with P. 3.2 ⋅ algorithms 53PDF Image | Instagram Cheat Sheet
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