Details
| Title | Google's PageRank and beyond: the science of search engine rankings |
|---|---|
| Creators | Langville Amy N. ; Meyer (Carl Dean) |
| Imprint | Princeton [N.J.]: Princeton University Press, [c2012] |
| Electronic publication | (Norwood, Mass. : Books24x7.com [generator]) |
| Collection | Электронные книги зарубежных издательств ; Общая коллекция |
| Subjects | Web sites — Ratings and rankings — Mathematics. ; Web search engines. ; Internet searching — Mathematics. ; World Wide Web — Subject access — Mathematics. ; LANGUAGE ARTS & DISCIPLINES / Library & Information Science / General ; EBSCO eBooks |
| Document type | Other |
| File type | |
| Language | English |
| Rights | Доступ по паролю из сети Интернет (чтение, печать, копирование) |
| Record key | ocn815649065 |
| Record create date | 9/21/2012 |
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- Cover
- Contents
- Preface
- Chapter 1. Introduction to Web Search Engines
- 1.1 A Short History of Information Retrieval
- 1.2 An Overview of Traditional Information Retrieval
- 1.3 Web Information Retrieval
- Chapter 2. Crawling, Indexing, and Query Processing
- 2.1 Crawling
- 2.2 The Content Index
- 2.3 Query Processing
- Chapter 3. Ranking Webpages by Popularity
- 3.1 The Scene in 1998
- 3.2 Two Theses
- 3.3 Query-Independence
- Chapter 4. The Mathematics of Google’s PageRank
- 4.1 The Original Summation Formula for PageRank
- 4.2 Matrix Representation of the Summation Equations
- 4.3 Problems with the Iterative Process
- 4.4 A Little Markov Chain Theory
- 4.5 Early Adjustments to the Basic Model
- 4.6 Computation of the PageRank Vector
- 4.7 Theorem and Proof for Spectrum of the Google Matrix
- Chapter 5. Parameters in the PageRank Model
- 5.1 The α Factor
- 5.2 The Hyperlink Matrix H
- 5.3 The Teleportation Matrix E
- Chapter 6. The Sensitivity of PageRank
- 6.1 Sensitivity with respect to α
- 6.2 Sensitivity with respect to H
- 6.3 Sensitivity with respect to v[sup(T)]
- 6.4 Other Analyses of Sensitivity
- 6.5 Sensitivity Theorems and Proofs
- Chapter 7. The PageRank Problem as a Linear System
- 7.1 Properties of (I – αS)
- 7.2 Properties of (I – αH)
- 7.3 Proof of the PageRank Sparse Linear System
- Chapter 8. Issues in Large-Scale Implementation of PageRank
- 8.1 Storage Issues
- 8.2 Convergence Criterion
- 8.3 Accuracy
- 8.4 Dangling Nodes
- 8.5 Back Button Modeling
- Chapter 9. Accelerating the Computation of PageRank
- 9.1 An Adaptive Power Method
- 9.2 Extrapolation
- 9.3 Aggregation
- 9.4 Other Numerical Methods
- Chapter 10. Updating the PageRank Vector
- 10.1 The Two Updating Problems and their History
- 10.2 Restarting the Power Method
- 10.3 Approximate Updating Using Approximate Aggregation
- 10.4 Exact Aggregation
- 10.5 Exact vs. Approximate Aggregation
- 10.6 Updating with Iterative Aggregation
- 10.7 Determining the Partition
- 10.8 Conclusions
- Chapter 11. The HITS Method for Ranking Webpages
- 11.1 The HITS Algorithm
- 11.2 HITS Implementation
- 11.3 HITS Convergence
- 11.4 HITS Example
- 11.5 Strengths and Weaknesses of HITS
- 11.6 HITS’s Relationship to Bibliometrics
- 11.7 Query-Independent HITS
- 11.8 Accelerating HITS
- 11.9 HITS Sensitivity
- Chapter 12. Other Link Methods for Ranking Webpages
- 12.1 SALSA
- 12.2 Hybrid Ranking Methods
- 12.3 Rankings based on Traffic Flow
- Chapter 13. The Future of Web Information Retrieval
- 13.1 Spam
- 13.2 Personalization
- 13.3 Clustering
- 13.4 Intelligent Agents
- 13.5 Trends and Time-Sensitive Search
- 13.6 Privacy and Censorship
- 13.7 Library Classification Schemes
- 13.8 Data Fusion
- Chapter 14. Resources for Web Information Retrieval
- 14.1 Resources for Getting Started
- 14.2 Resources for Serious Study
- Chapter 15. The Mathematics Guide
- 15.1 Linear Algebra
- 15.2 Perron–Frobenius Theory
- 15.3 Markov Chains
- 15.4 Perron Complementation
- 15.5 Stochastic Complementation
- 15.6 Censoring
- 15.7 Aggregation
- 15.8 Disaggregation
- Chapter 16. Glossary
- Bibliography
- Index
- A
- B
- C
- D
- E
- F
- G
- H
- I
- J
- K
- L
- M
- N
- O
- P
- Q
- R
- S
- T
- U
- V
- W
- Z
