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

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ISBN-10: 0691122024

ISBN-13: 9780691122021

Edition: 2006

Authors: Amy N. Langville, Carl D. Meyer

List price: $57.50
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Description:

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…    
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Book details

List price: $57.50
Copyright year: 2006
Publisher: Princeton University Press
Publication date: 7/23/2006
Binding: Hardcover
Pages: 240
Size: 7.00" wide x 10.00" long x 1.97" tall
Weight: 1.430
Language: English

Preface ix
Introduction to Web Search Engines
A Short History of Information Retrieval
An Overview of Traditional Information Retrieval
Web Information Retrieval
Crawling, Indexing, and Query Processing
Crawling
x The Content Index
Query Processing
Ranking Webpages by Popularity
The Scene in 1998
Two Theses
Query-Independence
The Mathematics of Google's PageRank
The Original Summation Formula for PageRank
Matrix Representation of the Summation Equations
Problems with the Iterative Process
A Little Markov Chain Theory
Early Adjustments to the Basic Model
Computation of the PageRank Vector
Theorem and Proof for Spectrum of the Google Matrix
Parameters in the PageRank Model
The alpha; Factor
The Hyperlink Matrix H
The Teleportation Matrix E
The Sensitivity of PageRank
Sensitivity with respect to alpha;
Sensitivity with respect to H
Sensitivity with respect to v T
Other Analyses of Sensitivity
Sensitivity Theorems and Proofs
The PageRank Problem as a Linear System
Properties of (I -- alhpa;S)
Properties of (I -- alpha;H)
Proof of the PageRank Sparse Linear System
Issues in Large-Scale Implementation of PageRank
Storage Issues
Convergence Criterion
Accuracy
Dangling Nodes
Back Button Modeling
Accelerating the Computation of PageRank
An Adaptive Power Method
Extrapolation
Aggregation
Other Numerical Methods
Updating the PageRank Vector
The Two Updating Problems and their History
Restarting the Power Method
Approximate Updating Using Approximate Aggregation
Exact Aggregation
Exact vs. Approximate Aggregation
Updating with Iterative Aggregation
Determining the Partition
Conclusions
The HITS Method for Ranking Webpages 115
The HITS Algorithm
HITS Implementation
HITS Convergence
HITS Example
Strengths and Weaknesses of HITS
HITS's Relationship to Bibliometrics
Query-Independent HITS
Accelerating HITS
HITS Sensitivity
Other Link Methods for Ranking Webpages
SALSA
Hybrid Ranking Methods
Rankings based on Traffic Flow
The Future of Web Information Retrieval
Spam
Personalization
Clustering
Intelligent Agents
Trends and Time-Sensitive Search
Privacy and Censorship
Library Classification Schemes
Data Fusion
Resources for Web Information Retrieval
Resources for Getting Started
Resources for Serious Study
The Mathematics Guide
Linear Algebra
Perron-Frobenius Theory
Markov Chains
Perron Complementation
Stochastic Complementation
Censoring
Aggregation
Disaggregation
Chapter 16: Glossary
Bibliography
Index