: Yanchun Zhang, Jeffrey Xu Yu, Jingyu Hou
: Web Communities Analysis and Construction
: Springer-Verlag
: 9783540277392
: 1
: CHF 49.90
:
: Sonstiges
: English
: 193
: Wasserzeichen/DRM
: PC/MAC/eReader/Tablet
: PDF

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.

Re earchers 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.



Dr. Yanchun Zhang is Associate Professsor and the Head of Computing Discipline in the Department of Mathematics and Computing at the University of Southern Queensland. He obtained PhD degree in Computer Science from the University of Queensland in 1991. His research areas cover databases, electronic commerce, internet/web information systems, web data management, web search and web services. He has published over 100 research papers on these topics in international journals and conference proceedings, and edited over 10 books/proceedings and journal special issues. He is a co-founder and Co-Editor-In-Chief of World Wide Web: Internet and Web Information Systems and Co-Chairman of International Web Information Systems Engineering Society.

Dr. Jeffrey Xu Yu received his B.E., M.E. and Ph.D. in computer science, from the University of Tsukuba, Japan, in 1985, 1987 and 1990, respectively. Jeffrey Xu Yu was a faculty member in the Institute of Information Sciences and Electronics, University of Tsukuba, Japan, and was a Lecturer in the Department of Computer Science, The Australian National University. Currently, he is an Associate Professor in the Department of Systems Engineering and Engineering Management, the Chinese University of Hong Kong. His research areas cover databases, data warehouse and data mining. He has published over 100 research papers on these topics in international journals and conference proceedings. Jeffrey Xu Yu is a member of ACM, and a society affiliate of IEEE Computer Society.

Dr Jingyu Hou received his BSc in Computational Mathematics from Shanghai University of Science and Technology (1985) and his PhD in Computational Mathematics from Shanghai University (1995). He is now a Lecturer in the School of Information Technology at Deakin University, Australia. He has also completed a PhD in Computer Science in the Department of Mathematics and Computing at The University of Southern Queensland, Australia. His research interests include Web-Based Data Management and Information Retrieval, Web Databases, Internet Computing and Electronic Commerce, and Semi-Structured Data Models. He has extensively published in the areas of Web information retrieval and Web Communities.

Contents6
Preface9
1 Introduction10
1.1 Background10
1.2 Web Community13
1.3 Outline of the Book14
1.4 Audience of the Book15
2 Preliminaries16
2.1 Matrix Expression of Hyperlinks16
2.2 Eigenvalue and Eigenvector of the Matrix18
2.3 Matrix Norms and the Lipschitz Continuous Function19
2.4 Singular Value Decomposition (SVD) of a Matrix20
2.5 Similarity in Vector Space Models23
2.6 Graph Theory Basics23
2.7 Introduction to the Markov Model24
3 HITS and Related Algorithms26
3.1 Original HITS26
3.2 The Stability Issues29
3.3 Randomized HITS31
3.4 Subspace HITS32
3.5 Weighted HITS33
3.6 The Vector Space Model (VSM)36
3.7 Cover Density Ranking (CDR)38
3.8 In-depth Analysis of HITS40
3.9 HITS Improvement44
3.10 Noise Page Elimination Algorithm Based on SVD47
3.11 SALSA (Stochastic algorithm)52
4 PageRank Related Algorithms57
4.1 The Original PageRank Algorithm57
4.2 Probabilistic Combination of Link and Content Information61
4.3 Topic-Sensitve PageRank64
4.4 Quadratic Extrapolation66
4.5 Exploring the Block Structure of the Web for Computing PageRank68
4.6 Web Page Scoring Systems (WPSS)72
4.7 The Voting Model79
4.8 Using Non-Affliated Experts to Rank Popular Topics83
4.9 A Latent Linkage Information (LLI) Algorithm87
5 Affinity and Co-Citation Analysis Approaches93
5.1 Web Page Similarity Measurement93
5.2 Hierarchical Web Page Clustering103
5.3 Matrix-Based Clustering Algorithms105
5.4 Co-Citation Algorithms112
6 Building a Web Community119
6.1 Web Community119
6.2 Small World Phenomenon on the Web121
6.3 Trawling the Web123
6.4 From Complete Bipartite Graph to Dense Directed Bipartite Graph126
6.5 Maximum Flow Approaches131
6.6 Web Community Charts141
6.7 From Web Community Chart to Web Community Evolution146
6.8 Uniqueness of a Web Community149
7 Web Community Related Techniques152
7.1 Web Community and Web Usage Mining152
7.2 Discovering Web Communities Using Co-occurrence154
7.3 Finding High-Level Web Communities156
7.4 Web Community and Formal Concept Analysis158
7.5 Generating Web Graphs with Embedded Web Communities162
7.6 Modeling Web Communities Using Graph Grammars164
7.7 Geographical Scopes of Web Resources165
7.8 Discovering Unexpected Information from Competitors168
7.9 Probabilistic Latent Semantic Analysis Approach171
8 Conclusions176
8.1 Summary176
8.2 Future Directions178
References180
Index188
About the Authors191