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Web Mining and Social Networking
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Guandong Xu • Yanchun Zhang • L
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VIIIPrefacefollowing characteristic
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Acknowledgements: We would like to
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XIVContents3.1.2 Basic Algorithms f
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XVIContentsPart III Social Networki
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Part IFoundation
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4 1 Introduction(3). Learning usefu
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6 1 Introductioncalled computationa
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8 1 Introduction• The data on the
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10 1 Introductionin a broad range t
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2Theoretical BackgroundsAs discusse
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2.2 Textual, Linkage and Usage Expr
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2.4 Eigenvector, Principal Eigenvec
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2.5 Singular Value Decomposition (S
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2.6 Tensor Expression and Decomposi
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2.7 Information Retrieval Performan
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2.8 Basic Concepts in Social Networ
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2.8 Basic Concepts in Social Networ
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30 3 Algorithms and TechniquesTable
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32 3 Algorithms and TechniquesSpeci
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36 3 Algorithms and TechniquesMetho
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40 3 Algorithms and TechniquesTable
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46 3 Algorithms and Techniques3.2 S
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48 3 Algorithms and TechniquesConce
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50 3 Algorithms and TechniquesNaive
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60 3 Algorithms and Techniquesstart
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62 3 Algorithms and TechniquesHere
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64 3 Algorithms and Techniques3.8.2
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66 3 Algorithms and Techniquesfor e
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68 3 Algorithms and Techniquesthat
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4Web Content MiningIn recent years
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score(q,d)=4.2 Web Search 73V(q) ·
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4.2 Web Search 75algorithm. The Web
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4.3 Feature Enrichment of Short Tex
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4.4 Latent Semantic Indexing 794.4
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Notation4.5 Automatic Topic Extract
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4.5 Automatic Topic Extraction from
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4.6 Opinion Search and Opinion Spam
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4.6 Opinion Search and Opinion Spam
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90 5 Web Linkage Mining5.2 Co-citat
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92 5 Web Linkage Mining{ /1 out deg
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94 5 Web Linkage Mininga =(a(1),·
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96 5 Web Linkage Mining5.4.1 Bipart
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98 5 Web Linkage MiningNext, consid
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100 5 Web Linkage Mining(5) Creatin
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102 5 Web Linkage Miningpower-law d
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104 5 Web Linkage MiningFig. 5.10.
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106 5 Web Linkage Miningbetween use
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6Web Usage MiningIn previous chapte
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6.1 Modeling Web User Interests usi
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6.2 Web Usage Mining using Probabil
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6.3 Finding User Access Pattern via
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6.4 Co-Clustering Analysis of weblo
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6.5 Web Usage Mining Applications 1
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Part IIISocial Networking and Web R
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146 7 Extracting and Analyzing Web
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