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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 16: Using Bloom Filters to Speed Up HITS-Like Ranking Algorithms
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