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Editorial Reviews
Review
"If I were taking, or teaching, a course in linear algebra today, this book would be a godsend". -- Ed Gerstner, Nature Physics
"It is an utterly engaging book, especially for one that depends so heavily on linear algebra to drive the plot". -- William J. Satzer, MAA Online
"They successfully introduce such web-searching principles as crawling, linking, and indexing together with basic algebraic ideas". -- Ian D. Gordon, Library Journal
"It is an utterly engaging book, especially for one that depends so heavily on linear algebra to drive the plot". -- William J. Satzer, MAA Online
"They successfully introduce such web-searching principles as crawling, linking, and indexing together with basic algebraic ideas". -- Ian D. Gordon, Library Journal
Review
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 )
If I were taking, or teaching, a course in linear algebra today, this book would be a godsend.
(Ed Gerstner Nature Physics )
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 )
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 )
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 )
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 )
...for anyone who wants to delve deeply into just how Google's PageRank works, I recommend Google's PageRank and Beyond.
(Stephen H. Wildstrom BusinessWeek )
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 Fernandez Mathematical Intelligencer )
(Jonathan Bowen Times Higher Education Supplement )
If I were taking, or teaching, a course in linear algebra today, this book would be a godsend.
(Ed Gerstner Nature Physics )
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 )
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 )
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 )
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 )
...for anyone who wants to delve deeply into just how Google's PageRank works, I recommend Google's PageRank and Beyond.
(Stephen H. Wildstrom BusinessWeek )
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 Fernandez Mathematical Intelligencer )
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First Sentence:
Today we have museums for everythingthe museum of hasehall, of hasehall players, of crazed fans of hasehall players, museums for world wars, national battles, legal fights, and family feuds. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
hyperlink matrix, nondangling nodes, link analysis model, ranking webpages, spectral circle, linear system formulation, hub matrix, personalization vector, many outlinks, crawler module, theorem for positive matrices, authority vector, hub vector, random surfer, intelligent surfer, nonnegative irreducible matrix, popularity score, aggregated chain, surfer model, hub scores, subdominant eigenvalue, censored distribution, ranking module, quadratic extrapolation, ergodic class Key Phrases - Capitalized Phrases (CAPs): (learn more)
Google Dance, World Wide Web, Internet Archive, Alta Vista, Google Hacks, Taher Haveliwala, Andy Pressman, Bob Massa, Deep Web, Google Markov, New York City, Numerical Computing New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Today we have museums for everythingthe museum of hasehall, of hasehall players, of crazed fans of hasehall players, museums for world wars, national battles, legal fights, and family feuds. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
hyperlink matrix, nondangling nodes, link analysis model, ranking webpages, spectral circle, linear system formulation, hub matrix, personalization vector, many outlinks, crawler module, theorem for positive matrices, authority vector, hub vector, random surfer, intelligent surfer, nonnegative irreducible matrix, popularity score, aggregated chain, surfer model, hub scores, subdominant eigenvalue, censored distribution, ranking module, quadratic extrapolation, ergodic class Key Phrases - Capitalized Phrases (CAPs): (learn more)
Google Dance, World Wide Web, Internet Archive, Alta Vista, Google Hacks, Taher Haveliwala, Andy Pressman, Bob Massa, Deep Web, Google Markov, New York City, Numerical Computing New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 29
books:
See all 29 books this book cites
- Numerical Computing with Matlab by Cleve B. Moler on page 77, page 150, and Back Matter
- Information Storage and Retrieval by Robert R. Korfhage on page 8, and Back Matter
- It Must Be Beautiful: Great Equations of Modern Science by Graham Farmelo on page 31, and Back Matter
- Understanding Search Engines: Mathematical Modeling and Text Retrieval (Software, Environments, Tools), Second Edition by Michael W. Berry and Murray Browne on page 7, and Back Matter
- Linked: How Everything Is Connected to Everything Else and What It Means by Albert-Laszlo Barabasi on page 30, and Back Matter
See all 29 books this book cites
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