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PageRank and perturbed Markov chains
Mälardalen University, School of Education, Culture and Communication, Educational Sciences and Mathematics. Department of Mathematics, College of Natural and Applied Sciences, University of Dar es Salaam,Tanzania. (MAM)
Mälardalen University, School of Education, Culture and Communication, Educational Sciences and Mathematics. Department of Mathematics, School of Physical Sciences, Makerere University, Kampala, Uganda. (MAM)
Mälardalen University, School of Education, Culture and Communication, Educational Sciences and Mathematics. (MAM)ORCID iD: 0000-0002-1624-5147
Department of Mathematics, School of Physical Sciences, Makerere University, Kampala, Uganda.
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2019 (English)In: Proceedings of 18th Applied Stochastic Models and Data Analysis International Conference with the Demographics 2019 Workshop, Florence, Italy: 11-14 June, 2019 / [ed] Christos H. Skiadas, ISAST: International Society for the Advancement of Science and Technology , 2019, p. 233-247Conference paper, Published paper (Refereed)
Abstract [en]

PageRank is a widely-used hyperlink-based algorithm to estimate the relative importance of nodes in networks [11]. Since many real world networks are large sparse networks, this makes efficient calculation of PageRank complicated. Moreover, one needs to escape from dangling effects in some cases as well as slow convergence of the transition matrix. Primitivity adjustment with a damping (perturbation) parameter ε(0,ε0] (for fixed ε0.15) is one of the essential procedure that is known to ensure convergence of the transition matrix [24]. If ε is large, the transition matrix looses information due to shift of information to teleportation matrix [27]. In this paper, we formulate PageRank problem as the first and second order Markov chains perturbation problem. Using numerical experiments, we compare convergence rates for the two problems for different values of ε on different graph structures and investigate the difference in ranks for the two problems.

Place, publisher, year, edition, pages
ISAST: International Society for the Advancement of Science and Technology , 2019. p. 233-247
Keywords [en]
PageRank, Markov chains, Perturbation problem
National Category
Probability Theory and Statistics Computational Mathematics
Research subject
Mathematics/Applied Mathematics
Identifiers
URN: urn:nbn:se:mdh:diva-47086ISBN: 978-618-5180-33-1 (electronic)OAI: oai:DiVA.org:mdh-47086DiVA, id: diva2:1394757
Conference
ASMDA2019, 18th Applied Stochastic Models and Data Analysis International Conference
Funder
Sida - Swedish International Development Cooperation AgencyAvailable from: 2020-02-20 Created: 2020-02-20 Last updated: 2020-02-24Bibliographically approved

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http://www.asmda.es/

Authority records BETA

Engström, ChristopherSilvestrov, Sergei

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