Despite its haphazard growth, the Web hides powerful underlying regularities -- from the organization of its links to the patterns found in its use by millions of users. Probabilistic modelling allows many of these regularities to be predicted on the basis of theoretical models based on statistical methodology.

Preface. 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. References. Index.

The World Wide Web is growing in size at a remarkable rate.  It is a huge evolving system and its data are rife with uncertainties.  Probability and statistics are the fundamental mathematical tools that enable us to model, reason and infer meaningful results from such data.  Modelling 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 merging properties of the Internet. Provides a comprehensive introduction to the modeling of the Internet and Web at the information  level.Takes a modern approach based on mathematical, probabilistic and graphical modeling.Provides an integrated presentation of theory, examples, exercies 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.

"…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)