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is a linear combination with the PageRank vector itself. Nevertheless, such a finding is disappointing. 3.5 discussion These results are mixed. For some graphs, the derivative is successful at predicting rank changes; and for others, it is not. Considering the magnitude of the derivative along with its sign could improve these results. For two graphs, the magnitude of PageRank and its derivative appear strongly correlated. Whether this result holds for a large set of graphs is not addressed, but our tacit experience from working with the codes and algorithms indicates that it does. The derivative, then, does not seem to be helpful as an additional ranking vector. But, there may be other uses for it. In the next chapter, we see that using information from the derivative helps a web spam identification task (section 4.8.4), and in the future work discussion (section 7.2), we propose using it to accelerate a computation from the next chapter. 3.5 ⋅ discussion 53PDF Image | MODELS AND ALGORITHMS FOR PAGERANK SENSITIVITY
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