Modeling the Internet and the Web: Probabilistic Methods and Algorithms / Edition 1

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Modeling the Internet and the Web covers the most important aspects of modeling the Web using a modern mathematical and probabilistic treatment. It focuses on the information and application layers, as well as some of the emerging properties of the Internet.

 Provides a comprehensive introduction to the modeling of the Internet and the Web at the information level.
 Takes a modern approach based on mathematical, probabilistic, and graphical modeling.
 Provides an integrated presentation of theory, examples, exercises and applications.
 Covers key topics such as text analysis, link analysis, crawling techniques, human behaviour, and commerce on the Web.

Interdisciplinary in nature, Modeling the Internet and the Web will be of interest to students and researchers from a variety of disciplines including computer science, machine learning, engineering, statistics, economics, business, and the social sciences.

"This book is fascinating!" - David Hand (Imperial College, UK)

"This book provides an extremely useful introduction to the intellectually stimulating problems of data mining electronic business." - Andreas S. Weigend (Chief Scientist,

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Editorial Reviews

From the Publisher
"…I congratulate the authors on a very well-researched and well-written publication." (Technometrics, August 2004, Vol. 46, No. 3)

“…fascinating …I highly recommend this book…” (Short Book Reviews, August 2004)

“…a very well-researched and well-written publication.” (Technometrics, August 2004) 

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Table of Contents


1 Mathematical Background.

1.1 Probability and Learning from a Bayesian Perspective.

1.2 Parameter Estimation from Data.

1.3 Mixture Models and the Expectation Maximization Algorithm.

1.4 Graphical Models.

1.5 Classification.

1.6 Clustering.

1.7 Power-Law Distributions.

1.8 Exercises.

2 Basic WWW Technologies.

2.1 Web Documents.

2.2 Resource Identifiers: URI, URL, and URN.

2.3 Protocols.

2.4 Log Files.

2.5 Search Engines.

2.6 Exercises.

3 Web Graphs.

3.1 Internet and Web Graphs.

3.2 Generative Models for the Web Graph and Other Networks.

3.3 Applications.

3.4 Notes and Additional Technical References.

3.5 Exercises.

4 Text Analysis.

4.1 Indexing.

4.2 Lexical Processing.

4.3 Content-Based Ranking.

4.4 Probabilistic Retrieval.

4.5 Latent Semantic Analysis.

4.6 Text Categorization.

4.7 Exploiting Hyperlinks.

4.8 Document Clustering.

4.9 Information Extraction.

4.10 Exercises.

5 Link Analysis.

5.1 Early Approaches to Link Analysis.

5.2 Nonnegative Matrices and Dominant Eigenvectors.

5.3 Hubs and Authorities: HITS.

5.4 PageRank.

5.5 Stability.

5.6 Probabilistic Link Analysis.

5.7 Limitations of Link Analysis.

6 Advanced Crawling Techniques.

6.1 Selective Crawling.

6.2 Focused Crawling.

6.3 Distributed Crawling.

6.4 Web Dynamics.

7 Modeling and Understanding Human Behavior on the Web.

7.1 Introduction.

7.2 Web Data and Measurement Issues.

7.3 Empirical Client-Side Studies of Browsing Behavior.

7.4 Probabilistic Models of Browsing Behavior.

7.5 Modeling and Understanding Search Engine Querying.

7.6 Exercises.

8 Commerce on the Web: Models and Applications.

8.1 Introduction.

8.2 Customer Data on theWeb.

8.3 Automated Recommender Systems.

8.4 Networks and Recommendations.

8.5 Web Path Analysis for Purchase Prediction.

8.6 Exercises.

Appendix A: Mathematical Complements.

A.1 Graph Theory.

A.2 Distributions.

A.3 Singular Value Decomposition.

A.4 Markov Chains.

A.5 Information Theory.

Appendix B: List of Main Symbols and Abbreviations.



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