MODELS AND ALGORITHMS FOR PAGERANK SENSITIVITY

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MODELS AND ALGORITHMS FOR PAGERANK SENSITIVITY ( models-and-algorithms-for-pagerank-sensitivity )

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62 4 ⋅ random alpha pagerank 4.3.5 Spam ranking Zhang et al. [2004] investigates using the PageRank at different values of α to infer spam pages. Spam pages, they argue, ought to be sensitive to α. Their goal is to trap the surfer and boost their rank. Thus, changing α will reveal them. After computing PageRank at a few α’s, they measure the correlation between the function 1/(1 − α) and the PageRank value on their small set of αs. This idea is related to the Gauss quadrature algorithm of section 4.6.4. In RAPr, the random variable A has an associated quadrature rule for its expectation that specifies the αs at which to compute the function. RAPr is also more general. It is not tied to just computing a spam correlation but produces a correlation between any group of pages as we illustrated in section 4.2.

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