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Algorithms and Models for the Web-Graph

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Cover of 'Algorithms and Models for the Web-Graph'

Table of Contents

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    Book Overview
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    Chapter 1 Bias Reduction in Traceroute Sampling – Towards a More Accurate Map of the Internet
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    Chapter 2 Distribution of PageRank Mass Among Principle Components of the Web
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    Chapter 3 Finding a Dense-Core in Jellyfish Graphs
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    Chapter 4 A Geometric Preferential Attachment Model of Networks II
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    Chapter 5 Clustering Social Networks
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    Chapter 6 Manipulation-Resistant Reputations Using Hitting Time
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    Chapter 7 Using Polynomial Chaos to Compute the Influence of Multiple Random Surfers in the PageRank Model
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    Chapter 8 A Spatial Web Graph Model with Local Influence Regions
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    Chapter 9 Determining Factors Behind the PageRank Log-Log Plot
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    Chapter 10 Approximating Betweenness Centrality
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    Chapter 11 Random Dot Product Graph Models for Social Networks
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    Chapter 12 Local Computation of PageRank Contributions
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    Chapter 13 Local Partitioning for Directed Graphs Using PageRank
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    Chapter 14 Stochastic Kronecker Graphs
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    Chapter 15 Deterministic Decentralized Search in Random Graphs
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    Chapter 16 Using Bloom Filters to Speed Up HITS-Like Ranking Algorithms
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    Chapter 17 Parallelizing the Computation of PageRank
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    Chapter 18 Giant Component and Connectivity in Geographical Threshold Graphs
Attention for Chapter 17: Parallelizing the Computation of PageRank
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Citations

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Chapter title
Parallelizing the Computation of PageRank
Chapter number 17
Book title
Algorithms and Models for the Web-Graph
Published by
Springer, Berlin, Heidelberg, December 2007
DOI 10.1007/978-3-540-77004-6_17
Book ISBNs
978-3-54-077003-9, 978-3-54-077004-6
Authors

John Wicks, Amy Greenwald, Wicks, John, Greenwald, Amy

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 100%
Readers by discipline Count As %
Computer Science 1 100%

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