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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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0 10 −2 10 −4 10 −6 10 0123 10 10 10 10 0 10 −5 10 −10 10 −15 10 (a)E[An −An+1] 4.4 ⋅ the random alpha pagerank model 69 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) 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) 012345 10 10 10 10 10 10 (b) E [An − An+1] for larger n 00 10 10 −1 −1 10 10 −2 −2 10 10 −3 −3 10 10 −4 −4 10 10 −5 −5 10 10 −6 −6 10 10 012012 10 10 10 10 10 10 (c) Beta(0, 0, [0.6, 0.9]) 00 10 10 −1 −1 10 10 −2 −2 10 10 −3 −3 10 10 −4 −4 10 10 −5 −5 10 10 −6 −6 (d) Beta(2, 16, [0, 1]) 10 10 012012 10 10 10 10 10 10 (e) Beta(1, 1, [0.1, 0.9]) (f) Beta(−0.5, −0.5, [0.2, 0.7]) Figure 4.5 – Path damping coefcients for the Random Alpha PageRank model. The first two subfigures show the path damping coefficients for the distributions from figure 4.4 drawn with the same legend. The following figures have six lines showing E [An − An+1 ] Pn v for each of the six pages in the graph from figure 4.2. The two lines at the right of each of these plots correspond to pages 5 and 6. Using these plots, we see the covariance structure iden- tified in section 4.2 in a different way. However, there is no hint of the bimodality for the Beta(−0.5, −0.5, [0.2, 0.7]) distribution.

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