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Generalized PageRank on directed configuration networks
Authors:Ningyuan Chen  Nelly Litvak  Mariana Olvera‐Cravioto
Institution:1. Department of Industrial Engineering and Operations ResearchColumbia University;2. Department of Industrial Engineering & Logistics ManagementHong Kong University of Science and Technology;3. Department of Applied MathematicsUniversity of Twente;4. Department of Industrial Engineering and Operations Research, University of California, Berkeley
Abstract:This paper studies the distribution of a family of rankings, which includes Google's PageRank, on a directed configuration model. In particular, it is shown that the distribution of the rank of a randomly chosen node in the graph converges in distribution to a finite random variable urn:x-wiley:10429832:media:rsa20700:rsa20700-math-0001 that can be written as a linear combination of i.i.d. copies of the attracting endogenous solution to a stochastic fixed‐point equation of the form where urn:x-wiley:10429832:media:rsa20700:rsa20700-math-0003 is a real‐valued vector with urn:x-wiley:10429832:media:rsa20700:rsa20700-math-0004, and the urn:x-wiley:10429832:media:rsa20700:rsa20700-math-0005 are i.i.d. copies of urn:x-wiley:10429832:media:rsa20700:rsa20700-math-0006, independent of urn:x-wiley:10429832:media:rsa20700:rsa20700-math-0007. Moreover, we provide precise asymptotics for the limit urn:x-wiley:10429832:media:rsa20700:rsa20700-math-0008, which when the in‐degree distribution in the directed configuration model has a power law imply a power law distribution for urn:x-wiley:10429832:media:rsa20700:rsa20700-math-0009 with the same exponent. © 2016 Wiley Periodicals, Inc. Random Struct. Alg., 51, 237–274, 2017
Keywords:PageRank  ranking algorithms  directed configuration model  complex networks  stochastic fixed‐point equations  weighted branching processes  power laws
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