Web Communities

by Zhang, Yanchun; Yu, Jeffrey Xu; Hou, Jingyu

• ISBN13: 9783540277378
• ISBN10: 3540277374
• Format: Hardcover
• Copyright: 2006-01-15
• Publisher: Springer-Verlag New York Inc

Summary

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.

Author Biography

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.

Table of Contents

Preface	xi
Introduction	1	(6)
Background	1	(3)
Web Community	4	(1)
Outline of the Book	5	(1)
Audience of the Book	6	(1)
Preliminaries	7	(10)
Expression of Hyperlinks	7	(2)
Eigenvalue and Eigenvector of the Matrix	9	(1)
Matrix Norms and the Lipschitz Continuous Function	10	(1)
Singular Value Decomposition (SVD) of a Matrix	11	(3)
Similarity in Vector Space Models	14	(1)
Graph Theory Basics	14	(1)
Introduction to the Markov Model	15	(2)
Hits and Related Algorithms	17	(32)
Original Hits	17	(3)
The Stability Issues	20	(2)
Randomized Hits	22	(1)
Subspace Hits	23	(1)
Weighted Hits	24	(3)
The Vector Space Model (Vsm)	27	(2)
Cover Density Ranking (Cdr)	29	(2)
In-depth Analysis of Hits	31	(4)
Htts Improvement	35	(3)
Noise Page Elimination Algorithm Based on Svd	38	(5)
Salsa (Stochastic algorithm)	43	(6)
Page Rank Related Algorithms	49	(36)
The Original PageRank Algorithm	49	(4)
Probabilistic Combination of Link and Content Information	53	(3)
Topic-Sensitve PageRank	56	(2)
Quadratic Extrapolation	58	(2)
Exploring the Block Structure of the Web for Computing PageRank	60	(4)
Web Page Scoring Systems (WPSS)	64	(7)
The Voting Model	71	(4)
Using Non-Affliated Experts to Rank Popular Topics	75	(4)
A Latent Linkage Information (LLI) Algorithm	79	(6)
Affinity and Co-Citation Analysis Approaches	85	(26)
Web Page Similarity Measurement	85	(10)
Page Source Construction	85	(2)
Page Weight Definition	87	(2)
Page Correlation Matrix	89	(3)
Page Similarity	92	(3)
Hierarchical Web Page Clustering	95	(2)
Matrix-Based Clustering Algorithms	97	(7)
Similarity Matrix Permutation	97	(2)
Clustering Algorithm from a Matrix Partition	99	(2)
Cluster-Overlapping Algorithm	101	(3)
Co-Citation Algorithms	104	(7)
Citation and Co-Citation Analysis	104	(2)
Extended Co-Citation Algorithms	106	(5)
Building a Web Community	111	(34)
Web Community	111	(2)
Small World Phenomenon on the Web	113	(2)
Trawling the Web	115	(3)
Finding Web Communities Based on Complete Directed Bipartite Graphs	117	(1)
From Complete Bipartite Graph to Dense Directed Bipartite Graph	118	(5)
The Algorithm	119	(4)
Maximum Flow Approaches	123	(10)
Maximum Flow and Minimum Cut	124	(1)
Flg Approach	125	(4)
Ik Approach	129	(4)
Web Community Charts	133	(5)
The Algorithm	135	(3)
From Web Community Chart to Web Community Evolution	138	(3)
Uniqueness of a Web Community	141	(4)
Web Community Related Techniques	145	(24)
Web Community and Web Usage Mining	145	(2)
Discovering Web Communities Using Co-occurrence	147	(2)
Finding High-Level Web Communities	149	(2)
Web Community and Formal Concept Analysis	151	(4)
Formal Concept Analysis	152	(1)
From Concepts to Web Communities	152	(3)
Generating Web Graphs with Embedded Web Communities	155	(2)
Modeling Web Communities Using Graph Grammars	157	(1)
Geographical Scopes of Web Resources	158	(3)
Two Conditions: Fraction and Uniformity	159	(2)
Geographical Scope Estimation	161	(1)
Discovering Unexpected Information from Competitors	161	(3)
Probabilistic Latent Semantic Analysis Approach	164	(5)
Usage Data and the PLSA model	165	(2)
Discovering Usage-Based Web Page Categories	167	(2)
Conclusions	169	(4)
Summary	169	(2)
Future Directions	171	(2)
References	173	(8)
Index	181	(4)
About the Authors	185