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Asymptotic analysis for personalized Web search

Part of: Inversion theorems Theory of data Operations research and management science

Published online by Cambridge University Press:  01 July 2016

Yana Volkovich  and
Nelly Litvak
Yana Volkovich*
Affiliation:
University of Twente
Nelly Litvak*
Affiliation:
University of Twente
*
∗ Current address: Barcelona Media - Innovation Center, Av. Diagonal 177, 9th Floor, 08018 Barcelona, Spain. Email address: [email protected]
∗∗ Postal address: Faculty of EEMCS, University of Twente, 7500 AE Enschede, The Netherlands. Email address: [email protected]
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Abstract

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PageRank with personalization is used in Web search as an importance measure for Web documents. The goal of this paper is to characterize the tail behavior of the PageRank distribution in the Web and other complex networks characterized by power laws. To this end, we model the PageRank as a solution of a stochastic equation where the Ris are distributed as R. This equation is inspired by the original definition of the PageRank. In particular, N models the number of incoming links to a page, and B stays for the user preference. Assuming that N or B are heavy tailed, we employ the theory of regular variation to obtain the asymptotic behavior of R under quite general assumptions on the involved random variables. Our theoretical predictions show good agreement with experimental data.

Keywords

PageRankWebregular variationstochastic equationTauberian theorem

MSC classification

Primary: 68P10: Searching and sorting 90B15: Network models, stochastic
Secondary: 40E05: Tauberian theorems, general
Type
General Applied Probability
Information
Advances in Applied Probability , Volume 42 , Issue 2 , June 2010 , pp. 577 - 604
Copyright
Copyright © Applied Probability Trust 2010 

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