We study the relation between PageRank and other parameters of information networks such as in-degree, out-degree, and the fraction of dangling nodes. We model this relation through a stochastic equation inspired by the original definition of PageRank. Further, we use the theory of regular variation to prove that PageRank and in-degree follow power laws with the same exponent. The difference between these two power laws is in a multiplicative constant, which depends mainly on the fraction of dangling nodes, average in-degree, the power law exponent, and the damping factor. The out-degree distribution has a minor effect, which we explicitly quantify. Finally, we propose a ranking scheme which does not depend on out-degrees. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Volkovich, Y., Litvak, N., & Donato, D. (2007). Determining factors behind the PageRank log-log plot. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4863 LNCS, pp. 108–123). Springer Verlag. https://doi.org/10.1007/978-3-540-77004-6_9
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