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162 7 ⋅ conclusion of α. The devil’s advocate will argue that such results are expected from an exploration of sensitivity analysis. This objection is unfounded. Consider the conversion from the derivative into PageRank problems. It is surprising that this conversion is possible because it depends crucially on the structure of the PageRank problem. Investing the effort in a speedy PageRank solver enables these, and other, experiments. The sensitivity measures helped the spam classification task. Nothing in the design of these measures is tuned to spam identification. This suggests that using the sensitivity vectors in other applications may produce similar improvement. Thus, do not ignore sensitivity. 7.1 discussion Will this thesis matter? Predicting the future is a difficult problem best avoided in this case. Instead, let us critically address a few points raised by this thesis. • IsPageRankresearchstilluseful? • Is picking a distribution for α really helpful? • Whyusesuchstricttolerancesinyourcomputations? • WhatabouttiesinthePageRankvector? We address each question in order. 7.1.1 Is PageRank research still useful? The death of PageRank has been forecast since 2003 [Zawodny, 2003]. Zawodny claims that the success of PageRank necessarily induces its future failure. Because PageRank utilizes the link structure of the web, it originally produced useful information for web ranking. But, the impact of PageRank on web search caused people to change their link structures to manipulate PageRank. Thus, links on the web will become less reliable over time. It is now 2009, and Google still uses PageRank [Cutts, 2009]. Rumors about its death are greatly exaggerated, apparently. In fact, Cutts [2009] discusses a critical change in the PageRank formu- lation used by Google. The change is that they no longer construct a 0, 1 sub-stochastic matrix from the link structure, but construct a general sub- stochastic matrix instead.1 This change shows that PageRank is still useful to Google, and thus research on it matters. On the other hand, Najork et al. [2007] claimed that PageRank is one of the least effective measures in a machine learning framework for web search. 1 Formally, for the new sub- stochastic matrix P ̄ we find that eT P ̄ can be any number between 0 and 1. These column-sums need not be 0 or 1 as in the formulation in chapter 2.PDF Image | Instagram Cheat Sheet
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