Google's PageRank and Beyond: The Science of Search Engine Rankings
Abstract
Why doesn't your home page appear on the first page of search results, even when you query your own name? How do other web pages always appear at the top? What creates these powerful rankings? And how? The first book ever about the science of web page rankings, Google's PageRank and Beyond supplies the answers to these and other questions and more. The book serves two very different audiences: the curious science reader and the technical computational reader. The chapters build in mathematical sophistication, so that the first five are accessible to the general academic reader. While other chapters are much more mathematical in nature, each one contains something for both audiences. For example, the authors include entertaining asides such as how search engines make money and how the Great Firewall of China influences research. The book includes an extensive background chapter designed to help readers learn more about the mathematics of search engines, and it contains several MATLAB codes and links to sample web data sets. The philosophy throughout is to encourage readers to experiment with the ideas and algorithms in the text. Any business seriously interested in improving its rankings in the major search engines can benefit from the clear examples, sample code, and list of resources provided. Many illustrative examples and entertaining asides MATLAB code Accessible and informal style Complete and self-contained section for mathematics review
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Cites
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Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
D is not the same random variable as an out-degree of a random page because the additional information that a page has a link to i alters the out-degree distribution.
“Note that D is not the same random variable as an out-degree of a random page since the additional information that a page has a link to i alters the out-degree distribution. This phenomenon is known as the inspection paradox [36].”
From Asymptotic analysis for personalized Web search (2010) · cites this paper as [36]
The number of out-links from a page containing a random link is stochastically larger than an out-degree of a random page.
“Thus, the number of out-links from a page containing a random link is stochastically larger than an out-degree of a random page.”
From Asymptotic analysis for personalized Web search (2010) · cites this paper as [36]
Cites
A Mathematical Theory of Communication
Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb01338.x · Cited by 88,486 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Top 10 algorithms in data mining
Wu, X., Kumar, V., Ross Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G. J., Ng, A., Liu, B., Yu, P. S., et al. (2008) · Knowledge and Information Systems · doi:10.1007/s10115-007-0114-2 · Cited by 5,667 · see citing works
Albash, T., & Lidar, D. A. (2018) · Reviews of Modern Physics · doi:10.1103/revmodphys.90.015002 · Cited by 1,546 · see citing works
Adiabatic Quantum Algorithm for Search Engine Ranking
Garnerone, S., Zanardi, P., & Lidar, D. A. (2012) · Physical Review Letters · doi:10.1103/physrevlett.108.230506 · Cited by 102 · see citing works
Asymptotic analysis for personalized Web search
Volkovich, Y., & Litvak, N. (2010) · Advances in Applied Probability · doi:10.1239/aap/1275055243 · Cited by 69 · see citing works
Algorithmic memory and the right to be forgotten on the web
Esposito, E. (2017) · Big Data & Society · doi:10.1177/2053951717703996 · Cited by 61 · see citing works
Distribution of PageRank Mass Among Principle Components of the Web
Avrachenkov, K., Litvak, N., & Pham, K. S. (2007) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-77004-6_2 · Cited by 29 · see citing works
Thermodynamic formalism for dissipative quantum walks
Garnerone, S. (2012) · Physical Review A · doi:10.1103/physreva.86.032342 · Cited by 23 · see citing works
- Authors
- Langville, A. N., & Meyer, C. D.
- ORCID
- Langville, Amy N.
- Venue
- Princeton University Press eBooks
- Publisher
- Princeton University Press
- Published
- 2006-12-31
- OpenAlex
- W2271295020
- Cited by
- 814
- References
- 1
- Citations by year
- 2026: 19 · 2025: 39 · 2024: 35 · 2023: 29 · 2022: 37 · 2021: 29 · 2020: 24 · 2019: 31 · 2018: 41 · 2017: 41 · 2016: 35 · 2015: 44 · 2014: 57 · 2013: 68 · 2012: 61
- FWCI
- 51.62
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Princeton University Press eBooks (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Physical Sciences › Computer Science › Information Systems › Web visibility and informetrics
- Physical Sciences › Computer Science › Information Systems › Web Data Mining and Analysis
- Keywords
- PageRank, web page ranking, search engines, Google, link analysis, ranking algorithms, search engine optimization, Great Firewall of China
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