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

by Amy N. Langville, Carl D. Meyer
4.1 out of 5 stars  See all reviews (17 customer reviews)

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Book Description

July 23, 2006 0691122024 978-0691122021

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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Google's PageRank and Beyond: The Science of Search Engine Rankings + Pay-Per-Click Search Engine Marketing Handbook: Low Cost Strategies for Attracting New Customers Using Google, MSN, Yahoo & Other Search Engines
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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 Fernndez, 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

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 (View shipping rates and policies)
  • Average Customer Review: 4.1 out of 5 stars  See all reviews (17 customer reviews)
  • Amazon Best Sellers Rank: #857,576 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews
39 of 41 people found the following review helpful
5.0 out of 5 stars surveys search techniques August 16, 2006
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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11 of 12 people found the following review helpful
5.0 out of 5 stars practical and fun January 18, 2007
By jim
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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14 of 17 people found the following review helpful
3.0 out of 5 stars The maths of google September 24, 2007
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
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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4 of 4 people found the following review helpful
5.0 out of 5 stars truly pagerank and beyond March 9, 2007
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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6 of 8 people found the following review helpful
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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Most Recent Customer Reviews
3.0 out of 5 stars For Serious Math Geeks ONly
This book is for very serious mathematicians only. It is highly technical in nature, filled with complex formulas. Difficult to move through
Published 5 months ago by John K. Mauney Jr
4.0 out of 5 stars Good
i am working on page summarisation and i actually needed a mathematical graph method to apply and this book worked.
I like the aside. Read more
Published 8 months ago by Theresa Omodunbi
5.0 out of 5 stars The first author on this book is Amy, not Carl.
yet the 'short summaries' that (sp)Amazon brings up when listing this book as a recommendation or offering to let you review it lists the *male* SECOND author as the SOLE author. Read more
Published on May 17, 2011 by Cheryl Fillekes
4.0 out of 5 stars Google's PageRank and Beyond: The Science of Search Engine Rankings
The book is good at explaining the Google's pageRanking, and it try to present rigorious math proof to demonstrate the idea. Read more
Published on February 16, 2009 by Xin Chen
5.0 out of 5 stars Great book...
Great book. It's nice to have all the recent work done in trust metrics all in one place.
Published on May 6, 2008 by Kevin A. Burton
4.0 out of 5 stars Good balance
The book strikes a good balance between the novice and the highly experienced math junkie
Published on February 23, 2008 by M. Grant
2.0 out of 5 stars More a math textbook than anything else
You need a degree in math to comprehend this book - if that is what you are looking for great. If not this book is not for web professionals like myself.
Published on October 29, 2007 by Denise Magic
4.0 out of 5 stars Good review
This book is a good review of the mathematics behind PageRank and other algorithms, such as HITS. It can be used as an auxilliary text in bth graduate and undergraduate... Read more
Published on May 7, 2007 by Geraldo XEXEO
3.0 out of 5 stars A bit dissapoited
I've read Langville's papers as part of my study on link-based ranking techniques. However, the book is only intended to be a very gentle introduction for people with good maths... Read more
Published on March 25, 2007 by Truyen Tran
5.0 out of 5 stars Google's PageRank and Beyond
"Google's PageRank and Beyond: The Science of Search Engine Rankings" by Amy N. Langville and Carl D. Meyer is a foremost book presenting the captivating mystery of Google. Read more
Published on March 11, 2007 by Semyon Berkovich
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Topic From this Discussion
For scientist or for normal reader?
The math is linear algebra based; without it you'll be pretty perplexed.
Dec 6, 2006 by kitode |  See all 4 posts
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