---
title: "The PageRank Citation Ranking — Page 1999"
description: "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…"
authors: ["Lawrence Page", "Sergey Brin", "Rajeev Motwani", "Terry Winograd"]
year: 1999
venue: "Stanford InfoLab Technical Report 1999-66"
source: "http://ilpubs.stanford.edu:8090/422/"
licence: "All rights reserved — free to read at the source"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/page-1999-pagerank-citation-ranking-bringing/"
---
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# The PageRank Citation Ranking: Bringing Order to the Web

**Authors:** [Lawrence Page](https://md.research.elamur.ai/free/en/authors/lawrence-page/), [Sergey Brin](https://md.research.elamur.ai/free/en/authors/sergey-brin/), Rajeev Motwani, Terry Winograd · **Year:** 1999 · **Venue:** Stanford InfoLab Technical Report 1999-66 · **Licence:** All rights reserved — free to read at the source

**Links:** [Publisher's page](http://ilpubs.stanford.edu:8090/422/) · [PDF](https://web.archive.org/web/20200101020038id_/http://ilpubs.stanford.edu:8090/422/1/1999-66.pdf) · [OpenAlex](https://openalex.org/W1854214752)

**Citations:** 12311 citations (openalex-snapshot · 2026-10-14) · [380](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## 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](https://md.research.elamur.ai/free/en/shelves/computing/)

## Cited by

Cited by

380 papers of our library cite it (of the 140884 whose bibliographies we read); 12 of them have a page here, 368 not yet.

- [The Anatomy of a Large-Scale Hypertextual Web Search Engine](https://md.research.elamur.ai/free/en/papers/brin-1998-anatomy-large-scale-hypertextual-web/)
- [A New Entity Salience Task with Millions of Training Examples](https://md.research.elamur.ai/free/en/papers/dunietz-2014-new-entity-salience-task-millions/)
- [Concept-Map-Based Multi-Document Summarization using Concept Coreference Resolution and Global Importance Optimization](https://md.research.elamur.ai/free/en/papers/falke-2017-concept-map-based-multi-document/)
- [Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies](https://md.research.elamur.ai/free/en/papers/grusky-2018-newsroom-dataset-1-3-million/)
- [Inductive Representation Learning on Large Graphs](https://md.research.elamur.ai/free/en/papers/hamilton-2017-inductive-representation-learning-large-graphs/)
- [Knowledge Graphs](https://md.research.elamur.ai/free/en/papers/hogan-2020-knowledge-graphs/)
- [Semantic Class Learning from the Web with Hyponym Pattern Linkage Graphs](https://md.research.elamur.ai/free/en/papers/kozareva-2008-semantic-class-learning-web-hyponym/)
- [Generating Wikipedia by Summarizing Long Sequences](https://md.research.elamur.ai/free/en/papers/liu-2018-generating-wikipedia-summarizing-long-sequences/)
- [Answers Unite! Unsupervised Metrics for Reinforced Summarization Models](https://md.research.elamur.ai/free/en/papers/scialom-2019-answers-unite-unsupervised-metrics-reinforced/)
- [Bangla Key2Text: Text Generation from Keywords for a Low Resource Language](https://md.research.elamur.ai/free/en/papers/talukder-2026-bangla-key2text-text-generation-keywords/)
- [Abstractive Document Summarization with a Graph-Based Attentional Neural Model](https://md.research.elamur.ai/free/en/papers/tan-2017-abstractive-document-summarization-graph-based/)
- [BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models](https://md.research.elamur.ai/free/en/papers/thakur-2021-beir-heterogenous-benchmark-zero/)

## Cite

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

```bibtex
@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/}
}
```

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## 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.

1. Sergey Brin and Larry Page. Google search engine. [http://google.stanford.edu](http://google.stanford.edu) . [↗](http://google.stanford.edu)
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](https://scholar.google.com/scholar?q=Junghoo+Cho%2C+Hector+Garcia%2DMolina%2C+and+Lawrence+Page%2E+E%0Ecient+crawling+through+url+ordering%2E+In+To+Appear%3A+Proceedings+of+the+Seventh+International+Web+Conference+%28WWW+98%29+%2C+1998%2E)
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](http://www.the-scientist.library.upenn.edu/yr1995/august/issi_950821.ht%ml) . [↗](http://www.the-scientist.library.upenn.edu/yr1995/august/issi_950821.ht%ml)
4. William Go man. A mathematical method for analyzing the growth of a scienti c discipline. Journal of the ACM , 18(2):173{185, April 1971. [find](https://scholar.google.com/scholar?q=William+Go+man%2E+A+mathematical+method+for+analyzing+the+growth+of+a+scienti+c+discipline%2E+Journal+of+the+ACM+%2C+18%282%29%3A173%7B185%2C+April+1971%2E)
5. Jon Kleinberg. Authoritative sources in a hyperlinked environment. In Proceedings of the Nineth Annual ACM-SIAM Symposium on Discrete Algorithms. , 1998. [find](https://scholar.google.com/scholar?q=Jon+Kleinberg%2E+Authoritative+sources+in+a+hyperlinked+environment%2E+In+Proceedings+of+the+Nineth+Annual+ACM%2DSIAM+Symposium+on+Discrete+Algorithms%2E+%2C+1998%2E) [our card](https://md.research.elamur.ai/free/en/papers/kleinberg-1999-authoritative-sources-hyperlinked-environment/)
6. Massimo Marchiori. The quest for correct information on the web: Hyper search engines. In Proceedings of the Sixth International WWW Conference, Santa Claram USA, April, 1997 , 1997. [http://www6.nttlabs.com/HyperNews/get/PAPER222.html](http://www6.nttlabs.com/HyperNews/get/PAPER222.html) . [↗](http://www6.nttlabs.com/HyperNews/get/PAPER222.html)
7. Sougata Mukherjea and James D. Foley. Showing the context of nodes in the worldwide web. In Proceedings of ACM CHI'95 Conference on Human Factors in Computing Systems , volume 2 of Short Papers: Web Browsing , pages 326{327, 1995. [find](https://scholar.google.com/scholar?q=Sougata+Mukherjea+and+James+D%2E+Foley%2E+Showing+the+context+of+nodes+in+the+worldwide+web%2E+In+Proceedings+of+ACM+CHI%2795+Conference+on+Human+Factors+in+Computing+Systems+%2C+volume+2+of+Short+Papers%3A+Web+B)
8. Sougata Mukherjea, James D. Foley, and Scott Hudson. Visualizing complex hypermedia networks through multiple hierarchical views. In Proceedings of ACM CHI'95 Conference on Human Factors in Computing Systems , volume 1 of Papers: Creating Visualizations , pages 331{337, 1995. [find](https://scholar.google.com/scholar?q=Sougata+Mukherjea%2C+James+D%2E+Foley%2C+and+Scott+Hudson%2E+Visualizing+complex+hypermedia+networks+through+multiple+hierarchical+views%2E+In+Proceedings+of+ACM+CHI%2795+Conference+on+Human+Factors+in+Computing+)
9. R. Motwani and P. Raghavan. Randomized Algorithms . Cambridge University Press, 1995. [find](https://scholar.google.com/scholar?q=R%2E+Motwani+and+P%2E+Raghavan%2E+Randomized+Algorithms+%2E+Cambridge+University+Press%2C+1995%2E)
10. NLANR. A distributed testbed for national information provisioning. [http://ircache.nlanr.net/Cache/](http://ircache.nlanr.net/Cache/) . [↗](http://ircache.nlanr.net/Cache/)
11. Lawrence Page and Sergey Brin. The anatomy of a large-scale hypertextual web search engine. In To Appear: Proceedings of the Seventh International Web Conference (WWW 98) , 1998. [find](https://scholar.google.com/scholar?q=Lawrence+Page+and+Sergey+Brin%2E+The+anatomy+of+a+large%2Dscale+hypertextual+web+search+engine%2E+In+To+Appear%3A+Proceedings+of+the+Seventh+International+Web+Conference+%28WWW+98%29+%2C+1998%2E) [our card](https://md.research.elamur.ai/free/en/papers/brin-1998-anatomy-large-scale-hypertextual-web/)
12. James E. Pitkow. Characterizing World Wide Web Ecologies . PhD thesis, Georgia Institue of Technology, June 1997. [find](https://scholar.google.com/scholar?q=James+E%2E+Pitkow%2E+Characterizing+World+Wide+Web+Ecologies+%2E+PhD+thesis%2C+Georgia+Institue+of+Technology%2C+June+1997%2E)
13. Peter Pirolli, James Pitkow, and Ramana Rao. Silk from a sow's ear: Extracting usable structure from the web. In Michael J. Tauber, Victoria Bellotti, Robin Je ries, Jock D. Mackinlay, and Jakob Nielsen, editors, Proceedings of the Conference on Human Factors in Computing Systems : Common Ground , p… [find](https://scholar.google.com/scholar?q=Peter+Pirolli%2C+James+Pitkow%2C+and+Ramana+Rao%2E+Silk+from+a+sow%27s+ear%3A+Extracting+usable+structure+from+the+web%2E+In+Michael+J%2E+Tauber%2C+Victoria+Bellotti%2C+Robin+Je+ries%2C+Jock+D%2E+Mackinlay%2C+and+Jakob+Niels)
14. Neeraja Sankaran. Speculation in the biomedical community abounds over likely candidates for nobel. The Scientist , 9(19), Oct 1995. [http://www.the-scientist.library.upenn.edu/yr1995/oct/nobel_951002.html%](http://www.the-scientist.library.upenn.edu/yr1995/oct/nobel_951002.html%) . [↗](http://www.the-scientist.library.upenn.edu/yr1995/oct/nobel_951002.html%)
15. Ellen Spertus. Parasite: Mining structural information on the web. In Proceedings of the Sixth International WWW Conference, Santa Claram USA, April, 1997 , 1997. [http://www6.nttlabs.com/HyperNews/get/PAPER206.html](http://www6.nttlabs.com/HyperNews/get/PAPER206.html) . [↗](http://www6.nttlabs.com/HyperNews/get/PAPER206.html)
16. Hope N. Tillman. Evaluating quality on the net. [http://www.tiac.net/users/hope/findqual.html](http://www.tiac.net/users/hope/findqual.html) . [↗](http://www.tiac.net/users/hope/findqual.html)
17. Ron Weiss, Bienvenido Velez, Mark A. Sheldon, Chanathip Manprempre, Peter Szilagyi, Andrzej Duda, and David K. Gi ord. HyPursuit: A hierarchical network search engine that exploits content-link hypertext clustering. In Proceedings of the 7th ACM Conference on Hypertext , pages 180{193, New York, 16… [find](https://scholar.google.com/scholar?q=Ron+Weiss%2C+Bienvenido+V%13elez%2C+Mark+A%2E+Sheldon%2C+Chanathip+Manprempre%2C+Peter+Szilagyi%2C+Andrzej+Duda%2C+and+David+K%2E+Gi+ord%2E+HyPursuit%3A+A+hierarchical+network+search+engine+that+exploits+content%2Dlink+hyper)