Statistical Analysis of Network Data - Methods and Models - Grand Format

In the past decade, the study of networks has increased dramatically. Researchers from across the sciences, including biology and bioinformatics, computer science, economics, engineering, mathematics, physics, sociology, and statistics, are 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 axonomy, although the presentation entails a conscious balance of concepts versus mathematics. In addition, the examples, including extended cases studies, are drawn widely from the literature. This book should be of substantial interest both to statisticians and to anyone ele working in the area ofneovork science.
The coverage of topics 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 network, network processes, and network Flows. This look is the first such resource to present material on all of these core topics in one place.