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Using graph partitioning to calculate PageRank in a changing network
Mälardalen University, School of Education, Culture and Communication, Educational Sciences and Mathematics. (MAM)ORCID iD: 0000-0002-1624-5147
Mälardalen University, School of Education, Culture and Communication, Educational Sciences and Mathematics. (MAM)ORCID iD: 0000-0003-4554-6528
2016 (English)In: Proceedings of the 4th Stochastic Modeling Techniques and DataAnalysis International Conference with Demographics Workshop (SMTDA2016) / [ed] Christos H Skiadas, 2016, 155-164 p.Conference paper, Published paper (Refereed)
Abstract [en]

PageRank was rst dened by S. Brinn and L. Page in 1998 in order torank homepages on the Internet for use in search engines using a random walk on theweb graph. While the original way to calculate PageRank is fast, due to the hugesize and growth of the web there have been many attempts at improving upon thecalculation speed of PageRank through various means. In this article we will lookat a slightly dierent but equally important problem, namely how to improve thecalculation speed of PageRank in a changing network where PageRank of an earlierstage of the network is available.In particular we consider two types of changes in the graph, the change in rank afterchanging the personalization vector used in calculating PageRank as well as addedor removed edges between dierent strongly connected components in the network.

Place, publisher, year, edition, pages
2016. 155-164 p.
Keyword [en]
PageRank, random walk, strongly connected component, network
National Category
Computational Mathematics
Research subject
Mathematics/Applied Mathematics
Identifiers
URN: urn:nbn:se:mdh:diva-36582OAI: oai:DiVA.org:mdh-36582DiVA: diva2:1145888
Conference
4th Stochastic Modeling Techniques and Data Analysis International Conference with Demographics Workshop
Available from: 2017-09-30 Created: 2017-09-30 Last updated: 2017-10-03Bibliographically approved

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CiteExportLink to record
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  • apa
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Language
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