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Calculating PageRank in a changing network with added or removed edges
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
2017 (English)In: AIP Conference Proceedings, Volume 1798 / [ed] Seenith Sivasundaram, American Institute of Physics (AIP), 2017, Vol. 1798, 020052-1-020052-8 p., 020052Conference paper, Published paper (Refereed)
Abstract [en]

PageRank was initially developed by S. Brinn and L. Page in 1998 to rank homepages on the Internet using the stationary distribution of a Markov chain created using the web graph. Due to the large size of the web graph and many other real worldnetworks fast methods to calculate PageRank is needed and even if the original way of calculating PageRank using a Power iterations is rather fast, many other approaches have been made to improve the speed further. In this paper we will consider the problem of recalculating PageRank of a changing network where the PageRank of a previous version of the network is known. In particular we will consider the special case of adding or removing edges to a single vertex in the graph or graph component

Place, publisher, year, edition, pages
American Institute of Physics (AIP), 2017. Vol. 1798, 020052-1-020052-8 p., 020052
Keyword [en]
PageRank, Random walk, graph
National Category
Computational Mathematics
Research subject
Mathematics/Applied Mathematics
Identifiers
URN: urn:nbn:se:mdh:diva-33457DOI: 10.1063/1.4972644ISI: 000399203000052Scopus ID: 2-s2.0-85013661500ISBN: 9780735414648 (print)OAI: oai:DiVA.org:mdh-33457DiVA: diva2:1039453
Conference
11th International Conference on Mathematical Problems in Engineering, Aerospace and Sciences, ICNPAA 2016; University of La RochelleLa Rochelle; France; 4 July 2016 through 8 July 2016
Available from: 2016-10-24 Created: 2016-10-24 Last updated: 2017-09-03Bibliographically approved
In thesis
1. PageRank in Evolving Networks and Applications of Graphs in Natural Language Processing and Biology
Open this publication in new window or tab >>PageRank in Evolving Networks and Applications of Graphs in Natural Language Processing and Biology
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis is dedicated to the use of graph based methods applied to ranking problems on the Web-graph and applications in natural language processing and biology.

Chapter 2-4 of this thesis is about PageRank and its use in the ranking of home pages on the Internet for use in search engines. PageRank is based on the assumption that a web page should be high ranked if it is linked to by many other pages and/or by other important pages. This is modelled as the stationary distribution of a random walk on the Web-graph.

Due to the large size and quick growth of the Internet it is important to be able to calculate this ranking very efficiently. One of the main topics of this thesis is how this can be made more efficiently, mainly by considering specific types of subgraphs and how PageRank can be calculated or updated for those type of graph structures. In particular we will consider the graph partitioned into strongly connected components and how this partitioning can be utilized.

Chapter 5-7 is dedicated to graph based methods and their application to problems in Natural language processing. Specifically given a collection of texts (corpus) we will compare different clustering methods applied to Pharmacovigilance terms (5), graph based models for the identification of semantic relations between biomedical words (6) and modifications of CValue for the annotation of terms in a corpus.

In Chapter 8-9 we look at biological networks and the application of graph centrality measures for the identification of cancer genes. Specifically in (8) we give a review over different centrality measures and their application to finding cancer genes in biological networks and in (9) we look at how well the centrality of vertices in the true network is preserved in networks generated from experimental data.

Place, publisher, year, edition, pages
Västerås: Mälardalen University, 2016
Series
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 217
National Category
Mathematics
Research subject
Mathematics/Applied Mathematics
Identifiers
urn:nbn:se:mdh:diva-33459 (URN)978-91-7485-298-1 (ISBN)
Public defence
2016-12-08, Kappa, Mälardalens högskola, Västerås, 13:15 (English)
Opponent
Supervisors
Available from: 2016-10-24 Created: 2016-10-24 Last updated: 2016-11-23Bibliographically approved

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Publisher's full textScopushttp://aip.scitation.org/doi/abs/10.1063/1.4972644

Authority records BETA

Engström, ChristopherSilvestrov, Sergei

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