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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Beta(0,0,0.6,0.9) Beta(2,16,0,1) Beta(1,1,0.1,0.9) Beta(−0.5,−0.5,0.2,0.7) (a) Beta densities xx 11 xx 22 xx 33 xx 44 xx 55 xx 66 4.4 ⋅ the random alpha pagerank model 65 01 0 0.5 0 0.5 (b) Beta(0, 0, [0.6, 0.9]) xx 11 xx 22 xx 33 xx 44 xx 55 xx 66 0 0.5 0 (d) Beta(1, 1, [0.1, 0.9]) (c) Beta(2, 16, [0, 1]) Figure 4.4 – Beta distributions and Random Alpha PageRank vectors. The four-parameter Beta distribution is quite flexible and exhibits a range of behaviors as a function of α, β, l, and r. The four density plots correspond to the graph from figure 4.2 with A drawn from the Beta distribution in the caption. When α, β < 0, the resulting PageRank density functions are bimodal. (PageRank densities are computed with a kernel density estimator applied to 10,000 random samples.) (e) Beta(−0.5, −0.5, [0.2, 0.7]) 0.5

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