Google's PageRank and beyond : the science of search engine rankings /

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Bibliographic Details
Author / Creator:	Langville, Amy N.
Imprint:	Princeton, N.J. : Princeton University Press, 2006.
Description:	x, 224 p. : ill. ; 26 cm.
Language:	English
Subject:	Google. Google. Web search engines. World Wide Web -- Subject access. Internet searching. Web search engines.
Format:	Print Book
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/6097478

Hidden Bibliographic Details
Varying Form of Title:	Science of search engine rankings Google page rank and beyond
Other authors / contributors:	Meyer, C. D. (Carl Dean)
ISBN:	0691122024
Notes:	Includes bibliographical references (p. [205]-217) and index.
Standard no.:	9780691122021

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
Chapter 2. Crawling, Indexing, and Query Processing
15 2.1. Crawling
15 2.1. x The Content Index
19 2.3. Query Processing
Chapter 3. Ranking Webpages by Popularity
25 3.1. The Scene in 1998
25 3.2. Two Theses
26 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 alpha; 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 alpha;
6.2. Sensitivity with respect to H
6.3. Sensitivity with respect to v 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 -- alhpa;S)
7.2. Properties of (I -- alpha;H)
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 115
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
3.1. Spam
3.2. Personalization
3.3. Clustering
3.4. Intelligent Agents
3.5. Trends and Time-Sensitive Search
3.6. Privacy and Censorship
3.7. Library Classification Schemes
3.8. 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. Chapter 16: Glossary
Bibliography
Index

Google's PageRank and beyond : the science of search engine rankings /

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