Web Communities

Analysis and Construction

By: Yanchun Zhang, Jeffrey Xu Yu, Jingyu Hou

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Hardcover | 8 December 2005

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200 Pages

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23.39 x 15.6 x 1.27

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Due to the lack of a uniform schema for Web documents and the sheer amount and dynamics of Web data, both the effectiveness and the efficiency of information management and retrieval of Web data is often unsatisfactory when using conventional data management techniques.

Web community, defined as a set of Web-based documents with its own logical structure, is a flexible and efficient approach to support information retrieval and to implement various applications. Zhang and his co-authors explain how to construct and analyse Web communities based on information like Web document contents, hyperlinks, or user access logs. Their approaches combine results from Web search algorithms, Web clustering methods, and Web usage mining. They also detail the necessary preliminaries needed to understand the algorithms presented, and they discuss several successful existing applications.

Researchers and students in information retrieval and Web search find in this all the necessary basics and methods to create and understand Web communities. Professionals developing Web applications will additionally benefit from the samples presented for their own designs and implementations.

Industry Reviews

The book can be used by applied mathematicians, search industry professionals, and anyone who wants to learn more about how search engines work. I recommend it for any course on Web information retrieval. I firmly believe that this book and the book by Langville and Meyer are the top two books about the algorithmic aspects of modern search engines. (Yannis Manolopoulos, Aristotle University, Thessaloniki, Greece in ACM REVIEWS)

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Preface	p. XI
Introduction	p. 1
Background	p. 1
Web Community	p. 4
Outline of the Book	p. 5
Audience of the Book	p. 6
Preliminaries	p. 7
Matrix Expression of Hyperlinks	p. 7
Eigenvalue and Eigenvector of the Matrix	p. 9
Matrix Norms and the Lipschitz Continuous Function	p. 10
Singular Value Decomposition (SVD) of a Matrix	p. 11
Similarity in Vector Space Models	p. 14
Graph Theory Basics	p. 14
Introduction to the Markov Model	p. 15
HITS and Related Algorithms	p. 17
Original HITS	p. 17
The Stability Issues	p. 20
Randomized HITS	p. 22
Subspace HITS	p. 23
Weighted HITS	p. 24
The Vector Space Model (VSM)	p. 27
Cover Density Ranking (CDR)	p. 29
In-depth Analysis of HITS	p. 31
HITS Improvement	p. 35
Noise Page Elimination Algorithm Based on SVD	p. 38
SALSA (Stochastic algorithm)	p. 43
PageRank Related Algorithms	p. 49
The Original PageRank Algorithm	p. 49
Probabilistic Combination of Link and Content Information	p. 53
Topic-Sensitve PageRank	p. 56
Quadratic Extrapolation	p. 58
Exploring the Block Structure of the Web for Computing PageRank	p. 60
Web Page Scoring Systems (WPSS)	p. 64
The Voting Model	p. 71
Using Non-Affliated Experts to Rank Popular Topics	p. 75
A Latent Linkage Information (LLI) Algorithm	p. 79
Affinity and Co-Citation Analysis Approaches	p. 85
Web Page Similarity Measurement	p. 85
Page Source Construction	p. 85
Page Weight Definition	p. 87
Page Correlation Matrix	p. 89
Page Similarity	p. 92
Hierarchical Web Page Clustering	p. 95
Matrix-Based Clustering Algorithms	p. 97
Similarity Matrix Permutation	p. 97
Clustering Algorithm from a Matrix Partition	p. 99
Cluster-Overlapping Algorithm	p. 101
Co-Citation Algorithms	p. 104
Citation and Co-Citation Analysis	p. 104
Extended Co-Citation Algorithms	p. 106
Building a Web Community	p. 111
Web Community	p. 111
Small World Phenomenon on the Web	p. 113
Trawling the Web	p. 115
Finding Web Communities Based on Complete Directed Bipartite Graphs	p. 117
From Complete Bipartite Graph to Dense Directed Bipartite Graph	p. 118
The Algorithm	p. 119
Maximum Flow Approaches	p. 123
Maximum Flow and Minimum Cut	p. 124
FLG Approach	p. 125
IK Approach	p. 129
Web Community Charts	p. 133
The Algorithm	p. 135
From Web Community Chart to Web Community Evolution	p. 138
Uniqueness of a Web Community	p. 141
Web Community Related Techniques	p. 145
Web Community and Web Usage Mining	p. 145
Discovering Web Communities Using Co-occurrence	p. 147
Finding High-Level Web Communities	p. 149
Web Community and Formal Concept Analysis	p. 151
Formal Concept Analysis	p. 152
From Concepts to Web Communities	p. 152
Generating Web Graphs with Embedded Web Communities	p. 155
Modeling Web Communities Using Graph Grammars	p. 157
Geographical Scopes of Web Resources	p. 158
Two Conditions: Fraction and Uniformity	p. 159
Geographical Scope Estimation	p. 161
Discovering Unexpected Information from Competitors	p. 161
Probabilistic Latent Semantic Analysis Approach	p. 164
Usage Data and the PLSA Model	p. 165
Discovering Usage-Based Web Page Categories	p. 167
Conclusions	p. 169
Summary	p. 169
Future Directions	p. 171
References	p. 173
Index	p. 181
About the Authors	p. 185
Table of Contents provided by Ingram. All Rights Reserved.

Web Communities

Analysis and Construction

At a Glance

Hardcover

Industry Reviews

Shipping

How to return your order

Defective items

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