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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 Information Theoretic Comparison of Stochastic Graph Models: Some Experiments
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    Chapter 2 Approximating the Number of Network Motifs
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    Chapter 3 Finding Dense Subgraphs with Size Bounds
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    Chapter 4 The Giant Component in a Random Subgraph of a Given Graph
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    Chapter 5 Quantifying the Impact of Information Aggregation on Complex Networks: A Temporal Perspective
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    Chapter 6 A Local Graph Partitioning Algorithm Using Heat Kernel Pagerank
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    Chapter 7 Choose the Damping, Choose the Ranking?
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    Chapter 8 Characterization of Tail Dependence for In-Degree and PageRank
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    Chapter 9 Web Page Rank Prediction with PCA and EM Clustering
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    Chapter 10 Permuting Web Graphs
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    Chapter 11 A Dynamic Model for On-Line Social Networks
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    Chapter 12 TC-SocialRank: Ranking the Social Web
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    Chapter 13 Exploiting Positive and Negative Graded Relevance Assessments for Content Recommendation
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    Chapter 14 Cluster Based Personalized Search
Attention for Chapter 2: Approximating the Number of Network Motifs
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