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

Amy N. Langville, Carl D. Meyer

  • 出版商: Princeton University
  • 出版日期: 2006-07-23
  • 售價: $1,540
  • 貴賓價: 9.5$1,463
  • 語言: 英文
  • 頁數: 240
  • 裝訂: Hardcover
  • ISBN: 0691122024
  • ISBN-13: 9780691122021

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"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

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 -- &alhpa;S) 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