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

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

Amy N. Langville , Carl D. Meyer

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Review

[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, MathDL.maa.org

Book Description

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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Amazon.com: 4.1 out of 5 stars (15 customer reviews)

38 of 40 people found the following review helpful
5.0 out of 5 stars surveys search techniques, Aug 16 2006
By W Boudville - Published on Amazon.com
This review is from: Google's PageRank and Beyond: The Science of Search Engine Rankings (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.

10 of 11 people found the following review helpful
5.0 out of 5 stars practical and fun, Jan 18 2007
By jim - Published on Amazon.com
This review is from: Google's PageRank and Beyond: The Science of Search Engine Rankings (Hardcover)
Great work! I wish I read it before I start my Ph.D. study.

Pros:

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

Cons:

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!

12 of 14 people found the following review helpful
3.0 out of 5 stars The maths of google, Sep 24 2007
By Carl Cerecke - Published on Amazon.com
This review is from: Google's PageRank and Beyond: The Science of Search Engine Rankings (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.
 Go to Amazon.com to see all 15 reviews  4.1 out of 5 stars 

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