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Monte Carlo methods in PageRank computation: When one iteration is sufficient
 

Summary: Monte Carlo methods in PageRank computation:
When one iteration is sufficient
K.Avrachenkov
, N. Litvak
, D. Nemirovsky
, N. Osipovaž
Abstract
PageRank is one of the principle criteria according to which Google
ranks Web pages. PageRank can be interpreted as a frequency of vis-
iting a Web page by a random surfer and thus it reflects the popularity
of a Web page. Google computes the PageRank using the power itera-
tion method which requires about one week of intensive computations.
In the present work we propose and analyze Monte Carlo type meth-
ods for the PageRank computation. There are several advantages of
the probabilistic Monte Carlo methods over the deterministic power
iteration method: Monte Carlo methods provide good estimation of
the PageRank for relatively important pages already after one itera-
tion; Monte Carlo methods have natural parallel implementation; and
finally, Monte Carlo methods allow to perform continuous update of
the PageRank as the structure of the Web changes.

  

Source: Avrachenkov, Konstantin - INRIA Sophia Antipolis
Litvak, Nelly - Department of Applied Mathematics, Universiteit Twente

 

Collections: Computer Technologies and Information Sciences; Engineering