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

by and

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

 

Synopses & Reviews

Publisher Comments:

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

Review:

"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." Times Higher Education Supplement

Review:

"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

Review:

"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

Synopsis:

"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

Synopsis:

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

About the Author

Amy N. Langville is Assistant Professor of Mathematics at the College of Charleston in Charleston, South Carolina. She studies mathematical algorithms for information retrieval and text and data mining applications.

Carl D. Meyer is Professor of Mathematics at North Carolina State University. In addition to information retrieval, his research areas include numerical analysis, linear algebra, and Markov chains. He is the author of Matrix Analysis and Applied Linear Algebra.

Table of Contents

Preface ix

Chapter 1: Introduction to Web Search Engines 1

1.1 A Short History of Information Retrieval 1

1.2 An Overview of Traditional Information Retrieval 5

1.3 Web Information Retrieval 9

Chapter 2: Crawling, Indexing, and Query Processing 15

2.1 Crawling 15

2.2 The Content Index 19

2.3 Query Processing 21

Chapter 3: Ranking Webpages by Popularity 25

3.1 The Scene in 1998 25

3.2 Two Theses 26

3.3 Query-Independence 30

Chapter 4: The Mathematics of Google's PageRank 31

4.1 The Original Summation Formula for PageRank 32

4.2 Matrix Representation of the Summation Equations 33

4.3 Problems with the Iterative Process 34

4.4 A Little Markov Chain Theory 36

4.5 Early Adjustments to the Basic Model 36

4.6 Computation of the PageRank Vector 39

4.7 Theorem and Proof for Spectrum of the Google Matrix 45

Chapter 5: Parameters in the PageRank Model 47

5.1 The α Factor 47

5.2 The Hyperlink Matrix H 48

5.3 The Teleportation Matrix E 49

Chapter 6: The Sensitivity of PageRank 57

6.1 Sensitivity with respect to α 57

6.2 Sensitivity with respect to H 62

6.3 Sensitivity with respect to vT 63

6.4 Other Analyses of Sensitivity 63

6.5 Sensitivity Theorems and Proofs 66

Chapter 7: The PageRank Problem as a Linear System 71

7.1 Properties of (I — αS) 71

7.2 Properties of (I — αH) 72

7.3 Proof of the PageRank Sparse Linear System 73

Chapter 8: Issues in Large-Scale Implementation of PageRank 75

8.1 Storage Issues 75

8.2 Convergence Criterion 79

8.3 Accuracy 79

8.4 Dangling Nodes 80

8.5 Back Button Modeling 84

Chapter 9: Accelerating the Computation of PageRank 89

9.1 An Adaptive Power Method 89

9.2 Extrapolation 90

9.3 Aggregation 94

9.4 Other Numerical Methods 97

Chapter 10: Updating the PageRank Vector 99

10.1 The Two Updating Problems and their History 100

10.2 Restarting the Power Method 101

10.3 Approximate Updating Using Approximate Aggregation 102

10.4 Exact Aggregation 104

10.5 Exact vs. Approximate Aggregation 105

10.6 Updating with Iterative Aggregation 107

10.7 Determining the Partition 109

10.8 Conclusions 111

Chapter 11: The HITS Method for Ranking Webpages 115

11.1 The HITS Algorithm 115

11.2 HITS Implementation 117

11.3 HITS Convergence 119

11.4 HITS Example 120

11.5 Strengths and Weaknesses of HITS 122

11.6 HITS's Relationship to Bibliometrics 123

11.7 Query-Independent HITS 124

11.8 Accelerating HITS 126

11.9 HITS Sensitivity 126

Chapter 12: Other Link Methods for Ranking Webpages 131

12.1 SALSA 131

12.2 Hybrid Ranking Methods 135

12.3 Rankings based on Traffic Flow 136

Chapter 13: The Future of Web Information Retrieval 139

13.1 Spam 139

13.2 Personalization 142

13.3 Clustering 142

13.4 Intelligent Agents 143

13.5 Trends and Time-Sensitive Search 144

13.6 Privacy and Censorship 146

13.7 Library Classification Schemes 147

13.8 Data Fusion 148

Chapter 14: Resources for Web Information Retrieval 149

14.1 Resources for Getting Started 149

14.2 Resources for Serious Study 150

Chapter 15: The Mathematics Guide 153

15.1 Linear Algebra 153

15.2 Perron-Frobenius Theory 167

15.3 Markov Chains 175

15.4 Perron Complementation 186

15.5 Stochastic Complementation 192

15.6 Censoring 194

15.7 Aggregation 195

15.8 Disaggregation 198

Chapter 16: Glossary 201

Bibliography 207

Index 219

Product Details

ISBN:
9780691122021
Author:
Amy N. Langville and Carl D. Meyer
Publisher:
Princeton University Press
Author:
Langville, Amy N.
Author:
Meyer, Carl D.
Author:
Langville, Amy
Author:
Meyer, Carl
Location:
Princeton
Subject:
Internet - General
Subject:
Mathematics
Subject:
Internet - Web Site Directories
Subject:
Web sites
Subject:
Web search engines
Subject:
Web sites - Ratings - Mathematics
Subject:
Computer Science
Copyright:
Publication Date:
May 2006
Binding:
HARDCOVER
Grade Level:
College/higher education:
Language:
English
Illustrations:
11 halftones. 26 line illus.
Pages:
240
Dimensions:
10 x 7 in 24 oz

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Related Subjects

Computers and Internet » Internet » Directories
Computers and Internet » Internet » General
Computers and Internet » Internet » Information
Computers and Internet » Internet » Programming
Computers and Internet » Internet » Web Publishing

Google's PageRank and Beyond: The Science of Search Engine Rankings Used Hardcover
0 stars - 0 reviews
$25.00 In Stock
Product details 240 pages Princeton University Press - English 9780691122021 Reviews:
"Review" by , "They successfully introduce such web-searching principles as crawling, linking, and indexing together with basic algebraic ideas".
"Review" by , "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."
"Review" by , "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."
"Review" by , "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."
"Synopsis" by ,

"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

"Synopsis" by ,

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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