Research Article
A Survey and Comparative Study of Different PageRank Algorithms
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 120 - Issue 24 |
| Published: June 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 },
year = { 2015 },
volume = { 120 },
number = { 24 },
pages = { 24-30 },
doi = { 10.5120/21410-4444 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2015
%A Tahseen A. Jilani
%A Ubaida Fatima
%A Mirza Mahmood Baig
%A Saba Mahmood
%T A Survey and Comparative Study of Different PageRank Algorithms%T
%J International Journal of Computer Applications
%V 120
%N 24
%P 24-30
%R 10.5120/21410-4444
%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
- Wills S. R, Google's PageRank: The Math Behind the Search Engine,2006
- Lin, Shi and Wei, On computing PageRank via lumping the Google matrix, Journal of Computational and Applied Mathematics 2009; 224(2):702-708
- Kamvar, Haveliwala and Golub, Adaptive methods for the computation of PageRank, Linear Algebra and its applications,(2004) 51-65.
- 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
- Ispen and Selee, PageRank computation, with special attention to Dangling Nodes, SIAM J. MATRIX ANAL. APPL. , (2007),1281-1296
- Kamvar et. al, Extrapolation Methods for Accelerating PageRank Computations, Twelfth International World Wide Web Conference (WWW 2003), May 20-24, 2003.
- 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
- 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
- Langville and Meyer, Google's PageRank and Beyond: The Science of Search Engine Rankings
- 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.
- Ipsen and Kirkland, Convergence Analysis of an Improved PageRank Algorithm
- http://mathfaculty. fullerton. edu/mathews/n2003/AitkenSteffensenMod. html
- http://ergodic. ugr. es/cphys/LECCIONES/FORTRAN/power_method. pdf
- Anton and Rorres, Elementary Linear Algebra, 7th edition.
- Pavel Berkhin, A survey on PageRank computing, Internet Mathematics 2 (2005), no. 1, 73–120.
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