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2013 | 2 | 1 | 73-84
Article title

EVALUATION OF THE PAGERANK ALGORITHM EFFECTIVENESS

Content
Title variants
Languages of publication
EN
Abstracts
EN
In this paper the challenges in building good search engines are discussed. Many of the search engines use well-known information retrieval algorithms and techniques. They use Web crawlers to maintain their index databases amortizing the cost of crawling and indexing over the millions of queries received by them. Web crawlers are programs that exploit the graph structure of the Web to move from page to page. Paper analyses the PageRank algorithm one of these Web crawlers. The results of the impact of the PageRank parameter value on the effectiveness of determining the so-called PageRank vector are considered in the paper. Investigations are illustrated by means of the results of a some simulation experiments to analyze the PageRank algorithm efficiency for different density graph (representing analyzed part of www) coefficient values.
Year
Volume
2
Issue
1
Pages
73-84
Physical description
Dates
published
2013
Contributors
  • The Faculty of Cybernetics, Military University of Technology
  • The Faculty of Cybernetics, Military University of Technology
References
  • Arasu A., Cho J., Garcia-Molina H., Paepcke A., Raghavan S. (2001) Searching the Web. ACM Transactions on Internet technology, 1 (1): 2-43.
  • Blachman N., Fredricksen E. Schneider F. (2003) How to Do Everything with Google. McGraw-Hill.
  • Brin S., Page L. (1998) The anatomy of a large-scale hypertextual Web search engine. Comput. Netw. ISDN Syst. 30: 107–117.
  • Cho J., Garcia-Molina H. (2003) Estimating frequency of change. Journal ACM Transactions on Internet Technology, 3 (3): 256- 90.
  • Coughran B. (2005) Google’s index nearly doubles, Google Inc. http://googleblog.blogspot.com/2004/11/googles-index-nearly-doubles.html
  • Golub G., Van Loan C.F. (1989) Matrix Computations. 2nd ed. Johns Hopkins University Press, Baltimore.
  • Havelivala T. (1999) Efficient computation of PageRank. Tech. Rep. 1999-31. Computer Systems Laboratory, Stanford University, Stanford, CA. http://dbpubs.stanford.edu/ pub/1999-31
  • Kleinberg J.M. (1999) Authoritative Sources in a Hyperlinked Environment. Journal of ACM, 46(5): 604–632.
  • Langville A.N., Meyer C.D. (2004) The Use of the Linear Algebra by Web Search Engines, http://meyer.math.ncsu.edu/Meyer/PS_Files/IMAGE.pdf.
  • Lawrence S., Giles C. (1999) Accessibility of information on the web. Nature 400, 107–109.
  • Meyer C. D. (2000) Matrix Analysis and Applied Linear Algebra. The Society for Industrial and Applied Mathematics, Philadelphia: 490–693.
  • Page L., Brin S., Motwani R., Winograd T. (1998) The PageRank Citation Ranking: Bring-ing Order to the Web. Tech. Rep. Computer Systems Laboratory, Stanford University, Stanford, CA.
Document Type
Publication order reference
Identifiers
ISSN
2084-5537
YADDA identifier
bwmeta1.element.desklight-223714c8-8c85-45bf-b762-534c7f83abb6
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