article

PageRank revisited

Author:

Michael BrinkmeierAuthors Info & Claims

ACM Transactions on Internet Technology (TOIT), Volume 6, Issue 3

Pages 282 - 301

https://doi.org/10.1145/1151087.1151090

Published: 01 August 2006 Publication History

Abstract

PageRank, one part of the search engine Google, is one of the most prominent link-based rankings of documents in the World Wide Web. Usually it is described as a Markov chain modeling a specific random surfer. In this article, an alternative representation as a power series is given. Nonetheless, it is possible to interpret the values as probabilities in a random surfer setting, differing from the usual one.Using the new description we restate and extend some results concerning the convergence of the standard iteration used for PageRank. Furthermore we take a closer look at sinks and sources, leading to some suggestions for faster implementations.

References

[1]

Arasu, A., Novak, J., Tomkins, A., and Tomlin, J. 2001. Pagerank computation and the structure of the Web: Experiments and algorithms. citeseer.ist.psu.edu/arasu02pagerank.html.

[2]

Bianchini, M., Gori, M., and Scarselli, F. 2005. Inside PageRank. ACM Trans. Internet Tech. 5, 92--128.

[3]

Brin, S. and Page, L. 1998. The anatomy of a large-scale hypertextual Web search engine. In Proceedings of the 7th World Wide Web Conference (WWW7).

[4]

Brin, S., Page, L., Motwani, R., and Winograd, T. 1999. The PageRank citation ranking: Bringing order to the Web. Tech. rep. 1999-66, Stanford Digital Library Technologies Project. http://dbpubs.stanford.edu:8090/pub/1999-66.

[5]

Brinkmeier, M. 2005. Distributed calculation of Pagerank using strongly connected components. In Proceedings of Innovative Internet Computing Systems (IICS). Lecture Notes in Computer Science, vol. 3908. A. Bui, M. Bui, T. Böhme, and H. Unger, Eds. Springer, 29--40.

[6]

Fogaras, D. 2003. Where to start browsing the Web&quest; In Proceedings of Innovative Internet Computing Systems (IICS). Lecture Notes in Computer Science, vol. 2877. T. Böhme, G. Heyer, and H. Unger, Eds. Springer, 65--79.

[7]

Haveliwala, T. and Kamvar, S. 2003. The second eigenvalue of the Google matrix. Tech. rep. 2003-20, Stanford University. http://dbpubs.stanford.edu/pub/2003--20.

[8]

Haveliwala, T., Kamvar, S., and Jeh, G. 2003. An analytical comparison of approaches to personalizing pagerank. Tech. rep., Stanford University.

[9]

Haveliwala, T. H. 2002. Topic-sensitive pagerank. In Proceedings of the 11th WWW Conference (WWW11). 517--526.

[10]

Haveliwala, T. H. 2003. Topic-sensitive pagerank: A context-sensitive ranking algorithm for Web search. IEEE Trans. Knowl. Data Eng. 15, 4, 784--796.

[11]

Jeh, G. and Widom, J. 2002. SimRank : A measure of structural-context similarity. In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.

[12]

Jeh, G. and Widom, J. 2003. Scaling personalized Web search. In Proceedings of the 12th International WWW Conference.

[13]

Kamvar, S., Haveliwala, T., and Golub, G. 2003. Adaptive methods for the computation of Pagerank. Tech. rep., Stanford University.

[14]

Kamvar, S. D., Haveliwala, T. H., Manning, C. D., and Golub, G. H. 2003a. Exploiting the block structure of the web for computing pagerank. Tech. Rep. 2003-17, Stanford University. http://dbpubs.stanford.edu:8090/pub/2003--17.

[15]

Kamvar, S. D., Haveliwala, T. H., Manning, C. D., and Golub, G. H. 2003b. Extrapolation methods for accelerating Pagerank computations. In WWW. 261--270.

[16]

Webgraph. Webgraph. University of Milano, http://webgraph.dsi.unimi.it/.

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PageRank revisited

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Published In

cover image ACM Transactions on Internet Technology

ACM Transactions on Internet Technology Volume 6, Issue 3

August 2006

109 pages

ISSN:1533-5399

EISSN:1557-6051

DOI:10.1145/1151087

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Copyright © 2006 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 August 2006

Published in TOIT Volume 6, Issue 3

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https://doi.org/10.1109/ICCSI58851.2023.10303849
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