The distribution of pageRank follows a power-law only for particular values of the damping factor

Subscribe (Full Service)


	Search: The ACM Digital Library The Guide

	Feedback

The distribution of pageRank follows a power-law only for particular values of the damping factor

Full text

Pdf (124 KB)

Source

International World Wide Web Conference archive
Proceedings of the 15th international conference on World Wide Web table of contents

Edinburgh, Scotland

POSTER SESSION: Browsers and UI, web engineering, hypermedia & multimedia, security, and accessibility table of contents

Pages: 941 - 942

Year of Publication: 2006

ISBN:1-59593-323-9

Authors

Luca Becchetti	Università di Roma "La Sapienza", Rome, Italy
Carlos Castillo	Università di Roma "La Sapienza", Rome, Italy

Sponsors

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
ACM: Association for Computing Machinery

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 5, Downloads (12 Months): 45, Citation Count: 3

Additional Information:

abstract references cited by index terms collaborative colleagues

Tools and Actions:	Review this Article Save this Article to a Binder Display Formats: BibTex EndNote ACM Ref

DOI Bookmark:	Use this link to bookmark this Article: http://doi.acm.org/10.1145/1135777.1135955 What is a DOI?

ABSTRACT

We show that the empirical distribution of the PageRank values in a large set of Web pages does not follow a power-law except for some particular choices of the damping factor. We argue that for a graph with an in-degree distribution following a power-law with exponent between 2.1 and 2.2, choosing a damping factor around 0.85 for PageRank yields a power-law distribution of its values. We suggest that power-law distributions of PageRank in Web graphs have been observed because the typical damping factor used in practice is between 0.85 and 0.90.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	E. Efthimiadis and C. Castillo. Charting the Greek Web. In Proc. of ASIST, Providence, Rhode Island, USA, Nov. 2004.
	2	B. M. Hill. A simple general approach to inference about the tail of a distribution. The Annals of Statistics, 3:1163--1174, 1975.
	3	Jun Hirai , Sriram Raghavan , Hector Garcia-Molina , Andreas Paepcke, WebBase: a repository of Web pages, Computer Networks: The International Journal of Computer and Telecommunications Networking, v.33 n.1-6, p.277-293, June 2000
	4	M. E. J. Newman. Power laws, pareto distributions and zipf's law. Contemporary Physics, 46:323--351, December 2005.
	5	L. Page, S. Brin, R. Motwani, and T. Winograd. The PageRank citation ranking: bringing order to the Web. Technical report, Stanford Digital Library Technologies Project, 1998.
	6	Gopal Pandurangan , Prabhakar Raghavan , Eli Upfal, Using PageRank to Characterize Web Structure, Proceedings of the 8th Annual International Conference on Computing and Combinatorics, p.330-339, August 15-17, 2002

CITED BY 3

	Luciana S. Buriol , Carlos Castillo , Debora Donato , Stefano Leonardi , Stefano Millozzi, Temporal Analysis of the Wikigraph, Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence, p.45-51, December 18-22, 2006
	Prasad Chebolu , Páll Melsted, PageRank and the random surfer model, Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms, p.1010-1018, January 20-22, 2008, San Francisco, California
	Junghoo Cho , Uri Schonfeld, RankMass crawler: a crawler with high personalized pagerank coverage guarantee, Proceedings of the 33rd international conference on Very large data bases, September 23-27, 2007, Vienna, Austria