In this Book

summary

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

Table of Contents

  1. Cover
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  1. Fontpage
  2. pp. iii-iv
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  1. Contents
  2. pp. v-viii
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  1. Preface
  2. pp. ix-x
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  1. Chapter 1. Introduction to Web Search Engines
  2. pp. 1-14
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  1. Chapter 2. Crawling, Indexing, and Query Processing
  2. pp. 15-24
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  1. Chapter 3. Ranking Webpages by Popularity
  2. pp. 25-30
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  1. Chapter 4. The Mathematics of Google’s PageRank
  2. pp. 31-46
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  1. Chapter 5. Parameters in the PageRank Model
  2. pp. 47-56
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  1. Chapter 6. The Sensitivity of PageRank
  2. pp. 57-70
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  1. Chapter 7. The PageRank Problem as a Linear System
  2. pp. 71-74
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  1. Chapter 8. Issues in Large-Scale Implementation of PageRank
  2. pp. 75-88
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  1. Chapter 9. Accelerating the Computation of PageRank
  2. pp. 89-98
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  1. Chapter 10. Updating the PageRank Vector
  2. pp. 99-114
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  1. Chapter 11. The HITS Method for Ranking Webpages
  2. pp. 115-130
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  1. Chapter 12. Other Link Methods for Ranking Webpages
  2. pp. 131-138
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  1. Chapter 13. The Future of Web Information Retrieval
  2. pp. 139-148
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  1. Chapter 14. Resources for Web Information Retrieval
  2. pp. 149-152
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  1. Chapter 15. The Mathematics Guide
  2. pp. 153-200
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  1. Chapter 16. Glossary
  2. pp. 201-206
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  1. Bibliography
  2. pp. 207-218
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  1. Index
  2. pp. 219-224
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Additional Information

ISBN
9781400830329
Related ISBN
9780691152660
MARC Record
OCLC
607859970
Pages
240
Launched on MUSE
2016-01-01
Language
English
Open Access
No
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