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The PageRank Citation Ranking: Bringing Order to the Web

Written for the Research library (claude-opus-5-5) from the paper text, in our own words: the license "All rights reserved — free to read at the source" does not allow redistributing the text itself. Original — web.archive.org/web/20200101020038id_/http://ilpubs.stanford.edu:8090/422/1/1999-66.pdf

Summary

Ranking web pages by importance is difficult. The Web is huge and uneven in quality, nobody reviews its content, and it is open to manipulation, so simple backlink counting borrowed from bibliometrics works poorly.

The authors propose PageRank, which scores every page using only the hyperlink graph. A page ranks highly when highly ranked pages link to it, and each page's score is split evenly across its outgoing links. Mathematically, the ranks form the dominant eigenvector of a link matrix extended with a rank-source vector E. This vector stops rank from accumulating in closed loops and models a "random surfer" who occasionally jumps to an arbitrary page. Scores are computed by power iteration, and links to pages with no outlinks are set aside until convergence.

Using the Stanford WebBase crawl, the authors computed PageRank over hundreds of millions of links. They applied it in the Google prototype search engine, a link-annotating proxy and crawl ordering. The method matters because it supplies a scalable, content-independent quality signal that is harder to inflate than raw citation counts.

Key points

  • The crawlable Web was estimated at about 150 million pages and 1.7 billion links. PageRank was computed efficiently over a graph of up to 518 million hyperlinks.
  • The Stanford WebBase repository held 24 million pages referencing over 75 million unique URLs. Of these, 51 million were not yet downloaded and were treated as dangling links.
  • Rank sinks are loops that absorb rank without passing it on. The source vector E prevents them, and choosing a non-uniform E yields personalized PageRank.
  • In crawling experiments [CGMP98] on the Stanford web, PageRank predicted eventual citation counts better than the partially known citation counts did.
  • Applications include ranking results in the Google search engine, a PageRank proxy that shows each link's log-scaled score, ordering backlinks, and traffic estimation.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Computing & GPUs

Cited by

380 papers of our library cite it (of the 140673 whose bibliographies we read); 6 of them have a page here, 374 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work.

@inproceedings{page1999pagerank,
  title = {{The PageRank Citation Ranking: Bringing Order to the Web}},
  author = {Page, Lawrence and Brin, Sergey and Motwani, Rajeev and Winograd, Terry},
  booktitle = {Stanford InfoLab Technical Report 1999-66},
  year = {1999},
  url = {http://ilpubs.stanford.edu:8090/422/}
}

References

17 references, 2 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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  2. Junghoo Cho, Hector Garcia-Molina, and Lawrence Page. Ecient crawling through url ordering. In To Appear: Proceedings of the Seventh International Web Conference (WWW 98) , 1998. find
  3. Eugene Gar eld. New international professional society signals the maturing of scientometrics and informetrics. The Scientist , 9(16), Aug 1995. http://www.the-scientist. library.upenn.edu/yr1995/august/issi\_950821.ht%ml . ↗
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  16. Hope N. Tillman. Evaluating quality on the net. http://www.tiac.net/users/hope/findqual.html . ↗
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