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Google's PageRank and Beyond: The Science of Search Engine Rankings

17 customer reviews
ISBN-13: 978-0691122021
ISBN-10: 0691122024
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Editorial Reviews


Honorable Mention for the 2006 Award for Best Professional/Scholarly Book in Computer & Information Science, Association of American Publishers

"[F]or anyone who wants to delve deeply into just how Google's PageRank works, I recommend Google's PageRank and Beyond."--Stephen H. Wildstrom, BusinessWeek

"This is a worthwhile book. It offers a comprehensive and erudite presentation of PageRank and related search-engine algorithms, and it is written in an approachable way, given the mathematical foundations involved."--Jonathan Bowen, Times Higher Education Supplement

"This book should be at the top of anyone's list as a must-read for those interested in how search engines work and, more specifically how Google is to meet the needs of so many people in so many ways."--Michael W. Berry, SIAM Review

"Amy N. Langville and Carl D. Meyer examine the logic, mathematics, and sophistication behind Google's PageRank and other Internet search engine ranking programs. . . . It is an excellent work."--Ian D. Gordon, Library Journal

"If I were taking, or teaching, a course in linear algebra today, this book would be a godsend."--Ed Gerstner, Nature Physics

"Langville and Meyer present the mathematics in all its detail. . . . But they vary the math with discussions of the many issues involved in building search engines, the 'wars' between search engine developers and those trying to artificially inflate the position of their pages, and the future of search-engine development. . . . Google's PageRank and Beyond makes good reading for anyone, student or professional, who wants to understand the details of search engines."--James Hendler, Physics Today

"This book is written for people who are curious about new science and technology as well as for those with more advanced background in matrix theory.... Much of the book can be easily followed by general readers, while understanding the remaining part requires only a good first course in linear algebra. It can be a reference book for people who want to know more about the ideas behind the currently popular search engines, and it provides an introductory text for beginning researchers in the area of information retrieval."--Jiu Ding, Mathemathical Reviews

"The book is very attractively and clearly written. The authors succeed to manage in an optimal way the presentation of both basic and more sophisticated concepts involved in the analysis of Google's PageRank, such that the book serves both audiences: the general and the technical scientific public."--Constantin Popa, Zentralblatt MATH

"The book under review is excellently written, with a fresh and engaging style. The reader will particularly enjoy the 'Asides' interspersed throughout the text. They contain all kind of entertaining stories, practical tips, and amusing quotes. . . . The book also contains some useful resources for computation."--Pablo Fernández, Mathematical Intelligencer

"Google's PageRank and Beyond describes the link analysis tool called PageRank, puts it in the context of web search engines and information retrieval, and describes competing methods for ranking webpages. It is an utterly engaging book."--Bill Satzer,

From the Inside Flap

"Comprehensive and engagingly written. This book should become an important resource for many audiences: applied mathematicians, search industry professionals, and anyone who wants to learn more about how search engines work."--Jon Kleinberg, Cornell University

"I don't think there are any competitive books in print with the same depth and breadth on the topic of search engine ranking. The content is well-organized and well-written."--Michael Berry, University of Tennessee

--This text refers to an out of print or unavailable edition of this title.

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Product Details

  • Hardcover: 240 pages
  • Publisher: Princeton University Press (July 23, 2006)
  • Language: English
  • ISBN-10: 0691122024
  • ISBN-13: 978-0691122021
  • Product Dimensions: 10 x 7.4 x 0.8 inches
  • Shipping Weight: 8 ounces
  • Average Customer Review: 4.1 out of 5 stars  See all reviews (17 customer reviews)
  • Amazon Best Sellers Rank: #539,166 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

41 of 43 people found the following review helpful By W Boudville HALL OF FAMETOP 1000 REVIEWERVINE VOICE on August 16, 2006
Format: Hardcover
Langville and Meyer have done a superb job describing both Google's technical foundations, and the broader subject of how search engines rank pages. Over half the book is devoted to explaining the maths and rationales behind PageRank. The level of maths is understandable to those who have done some university level courses on linear algebra (i.e. matrices).

The book also has considerable value in analysing what other organisations (like search engines) and researchers have cobbled together. It gives a useful summation of the state of the research, circa 2006. Essentially, everyone seems to focus on link analysis, after Google revolutionised the industry in 1998 by using this. It blew away the previous leader, AltaVista.

It is true, as the authors point out, that most of the material here has already been published. But as discrete events, scattered through various scientific journals and websites. You can certainly get explanations of PageRank on several websites. But the mathematical depth and reliability of those discussions can vary with the site. The book is far handier.

It is a good starting point, if you are interesting in devising your own search methods.
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12 of 13 people found the following review helpful By jim on January 18, 2007
Format: Hardcover
Great work! I wish I read it before I start my Ph.D. study.


1) Precise and intuitive description of the search algorithm

2) Plenty of interesting stories making mathematics fully applicable in practice

3) Sample Matlab code available


This is actually a perfect book. But one needs to have basic linear algebra to appreciate its value. If you are looking for "SEO", you are in a wrong spot.

But if anyone wonder how Page and Brin turn math into treasure, read it!
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7 of 7 people found the following review helpful By Man Kam Tam on December 8, 2008
Format: Hardcover
A web search engine has six major components. The components are (1) crawler module, (2) page repository, (3) indexing module, (4) indexes, (5) query module, and (6) ranking module. The ranking module takes the set of relevant pages and ranks them according to both the content score and the popularity score. The popularity score is the focus of Amy N. Langville and Carl D. Meyer's "Google's PageRank and Beyond: The Science of Search Engine Rankings." The popularity score of a web page is determined by Web pages' hyperlink structure.

Brin and Page`s PagerRank philosophy is that a page with more recommendations must be more important than a page with a few links. Or a web page is more important if it is pointed to by other important page. Brin and Page then build a normalized hyperlink matrix (H). With the adjustments named stochasticity and primitivity, a Google matrix (G) is obtained, which is, in fact, a probability transition matrix of a Markov chain. The desired ranking of the web pages is the stationary vector of the matrix G or the solution of the corresponding linear homogeneous system.

To calculate the ranking vector is not an easy task, for the matrix G has 8.1 billion rows and 8.1 billions columns. The matrix is growing everyday as the number of web pages grows everyday. The book consider several major large-scale implementation issues such as storage, convergence criterion, accuracy, dangling nodes, and back button modeling. Accelerating methods are presented as well. They are the adaptive power method, extrapolation, and aggregation. Once the ranking vector is calculated, it has to be updated periodically. However, there is no effective and efficient update method available other than calculating from scratch.
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14 of 17 people found the following review helpful By Carl Cerecke on September 24, 2007
Format: Hardcover
The subtitle "The science of search engine rankings" is a misnomer. This book is primarily about the *mathematics* of pagerank. For non-mathematicians, such as a computer scientist like myself (though I do have undergrad maths), it was pretty slow going and just plain boring.

I wanted algorithm examples for pagerank calculation of largish (10M) data sets. Not matlab code. Matlab might be great for people who love matrices and don't mind being locked-in to a proprietary language, but it is hardly a sensible choice for a production implementation of the pagerank algorithm. And an algorithm using matrix manipulation, while it might be mathematically nice, is difficult to implement efficiently without fancy matrix compression tricks (as far as I can tell).

In the end, I discarded the book, and wrote my own shorter, simpler, non-matrix implementation in python, verified it produced the same results, and then rewrote it in C. It is quite fast enough for 10M pages even without any fancy optimisations. Not a matrix in sight. Yay.

For mathematicians, this book might deserve more than 3 stars. For computer scientists though, I wouldn't recommend it.
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4 of 4 people found the following review helpful By M. Clements on March 9, 2007
Format: Hardcover
Great book describing the algorithms that made current search engines so useful and popular. The book describes the math behind the pagerank and HITS algorithms, supported by MATLAB code. Wonderfully written!

Do not buy this book if you want to know how to use search engines, only if you want to understand them!
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Format: Hardcover Verified Purchase
The authors subdivide the book into two main sections: the first few chapters, which are conversational in the manner in which they address pagerank and similar algorithms, and the subsequent chapters, which grow increasingly mathematical. Both authors have strong backgrounds in mathematics, hence that focus. Understanding that, the book is very approachable, lucid and useful in understanding the treated subject matter.
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