This book is useful both to people in the statistics community and in the various communities in which networks currently are heavily used, such as parts of biology, computer science, engineering, physics, and sociology.

Unified presentation of statistical models and methods from across the variety of disciplines engaged in 'network science' Balanced presentation of concepts and mathematics Examples, including extended case studies, drawn widely from applications in the literature Includes supplementary material: sn.pub/extras

Autorentext

Eric D. Kolaczyk is a professor of statistics and a data science faculty fellow at Boston University, in the Department of Mathematics and Statistics, where he also is an affiliated faculty member in the Bioinformatics Program, the Division of Systems Engineering, and the Center for Systems Neuroscience. Currently, he serves as the director of Boston University's Hariri Institute for Computing. His publications on network-based topics, beyond the development of statistical methodology and theory, include work on applications ranging from the detection of anomalous traffic patterns in computer networks to the prediction of biological function in networks of interacting proteins to the characterization of influence of groups of actors in social networks. He is an elected fellow of the American Association for the Advancement of Science (AAAS), the American Statistical Association (ASA), and the Institute of Mathematical Statistics, an elected member of the International Statistical Institute (ISI), and an elected senior member of the Institute of Electrical and Electronics Engineers (IEEE). Gábor Csárdi is a software engineer at RStudio, where he works on R infrastructure packages. He holds a PhD in Computer Science from Eötvös University, Hungary, and he has done postdocs at the Swiss Institute of Bioinformatics, the University of Lausanne, and Harvard University.

Klappentext

In the past decade, the study of networks has increased dramatically. Researchers from across the sciencesincluding biology and bioinformatics, computer science, economics, engineering, mathematics, physics, sociology, and statisticsare more and more involved with the collection and statistical analysis of network-indexed data. As a result, statistical methods and models are being developed in this area at a furious pace, with contributions coming from a wide spectrum of disciplines.

This book provides an up-to-date treatment of the foundations common to the statistical analysis of network data across the disciplines. The material is organized according to a statistical taxonomy, although the presentation entails a conscious balance of concepts versus mathematics. In addition, the examplesincluding extended cases studiesare drawn widely from the literature. This book should be of substantial interest both to statisticians and to anyone else working in the area of 'network science.'

The coverage of topics in this book is broad, but unfolds in a systematic manner, moving from descriptive (or exploratory) methods, to sampling, to modeling and inference. Specific topics include network mapping, characterization of network structure, network sampling, and the modeling, inference, and prediction of networks, network processes, and network flows. This book is the first such resource to present material on all of these core topics in one place.

Eric Kolaczyk is a professor of statistics, and Director of the Program in Statistics, in the Department of Mathematics and Statistics at Boston University, where he also is an affiliated faculty member in the Center for Biodynamics, the Program in Bioinformatics, and the Division of Systems Engineering. His publications on network-based topics include work ranging from the detection of anomalous traffic patterns in computer networks to the prediction of biological function in networks ofinteracting proteins to the characterization of influence of groups of actors in social networks.

Inhalt
and Overview.- Preliminaries.- Mapping Networks.- Descriptive Analysis of Network Graph Characteristics.- Sampling and Estimation in Network Graphs.- Models for Network Graphs.- Network Topology Inference.- Modeling and Prediction for Processes on Network Graphs.- Analysis of Network Flow Data.- Graphical Models.