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A Survey and Comparative Study of Different PageRank Algorithms

by Tahseen A. Jilani, Ubaida Fatima, Mirza Mahmood Baig, Saba Mahmood
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 120 - Number 24
Year of Publication: 2015
Authors: Tahseen A. Jilani, Ubaida Fatima, Mirza Mahmood Baig, Saba Mahmood
10.5120/21410-4444

Tahseen A. Jilani, Ubaida Fatima, Mirza Mahmood Baig, Saba Mahmood . A Survey and Comparative Study of Different PageRank Algorithms. International Journal of Computer Applications. 120, 24 ( June 2015), 24-30. DOI=10.5120/21410-4444

@article{ 10.5120/21410-4444,
author = { Tahseen A. Jilani, Ubaida Fatima, Mirza Mahmood Baig, Saba Mahmood },
title = { A Survey and Comparative Study of Different PageRank Algorithms },
journal = { International Journal of Computer Applications },
issue_date = { June 2015 },
volume = { 120 },
number = { 24 },
month = { June },
year = { 2015 },
issn = { 0975-8887 },
pages = { 24-30 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume120/number24/21410-4444/ },
doi = { 10.5120/21410-4444 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:07:06.586989+05:30
%A Tahseen A. Jilani
%A Ubaida Fatima
%A Mirza Mahmood Baig
%A Saba Mahmood
%T A Survey and Comparative Study of Different PageRank Algorithms
%J International Journal of Computer Applications
%@ 0975-8887
%V 120
%N 24
%P 24-30
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Searching the World Wide Web is an NP complete problem with sparse hyperlink matrices. Thus searching the significant search results is a challenge. Google's PageRank attempted to solve this problem using computing of principle Eigenvalues termed as PageRank vector. After this, a number of techniques were developed to speed up the convergence patterns of pages in the PageRank algorithm. This is paper, we have reviewed a number of of PageRank computation techniques. The main objective of all these techniques is the convergence rate along with space and time complexities. In this paper, a comparative study is presented among Standard Power method, Adaptive Power Method and Aitken's method using SNAP Google web pages dataset.

References
  1. Wills S. R, Google's PageRank: The Math Behind the Search Engine,2006
  2. Lin, Shi and Wei, On computing PageRank via lumping the Google matrix, Journal of Computational and Applied Mathematics 2009; 224(2):702-708
  3. Kamvar, Haveliwala and Golub, Adaptive methods for the computation of PageRank, Linear Algebra and its applications,(2004) 51-65.
  4. Fu Hwai-Hui, Lin Dennis and Tsai Hsein-Tang, Damping Factor in Google page ranking, Applied Stochastic Models in Business and Industry,2006,Vol 22,431-444
  5. Ispen and Selee, PageRank computation, with special attention to Dangling Nodes, SIAM J. MATRIX ANAL. APPL. , (2007),1281-1296
  6. Kamvar et. al, Extrapolation Methods for Accelerating PageRank Computations, Twelfth International World Wide Web Conference (WWW 2003), May 20-24, 2003.
  7. Wu and Wei, Arnoldi versus GMRES for computing pageRank: A theoretical contribution to Google's PageRank problem, ACM Transactions on Information Systems (TOIS) ,(2010),28 (3), 11
  8. Yu, Miao, Wu and Wei, Lumping algorithms for computing Google's PageRank and its derivative, with attention to unreferenced nodes, Inf Retrieval (2012) 15:503–526
  9. Langville and Meyer, Google's PageRank and Beyond: The Science of Search Engine Rankings
  10. Zhu,Yu and Li, Distributed PageRank computation based on iterative aggregation-disaggregation method,CIKM '05 Proceedings of the 14th ACM international conference on Information and knowledge management, (2005),pages 578-585.
  11. Ipsen and Kirkland, Convergence Analysis of an Improved PageRank Algorithm
  12. http://mathfaculty. fullerton. edu/mathews/n2003/AitkenSteffensenMod. html
  13. http://ergodic. ugr. es/cphys/LECCIONES/FORTRAN/power_method. pdf
  14. Anton and Rorres, Elementary Linear Algebra, 7th edition.
  15. Pavel Berkhin, A survey on PageRank computing, Internet Mathematics 2 (2005), no. 1, 73–120.
Index Terms

Computer Science
Information Sciences

Keywords

Google PageRank Aitken's PageRank Power method Adaptive PageRank

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