Statistical Analysis of Network Data - Eric D. Kolaczyk

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Résumé :
In recent years there has been an explosion of network data ? that is, measu- ments that are either of or from a system conceptualized as a network ? from se- ingly all corners of science. The combination of an increasingly pervasive interest in scienti c analysis at a systems level and the ever-growing capabilities for hi- throughput data collection in various elds has fueled this trend. Researchers from biology and bioinformatics to physics, from computer science to the information sciences, and from economics to sociology are more and more engaged in the c- lection and statistical analysis of data from a network-centric perspective. Accordingly, the contributions to statistical methods and modeling in this area have come from a similarly broad spectrum of areas, often independently of each other. Many books already have been written addressing network data and network problems in speci c individual disciplines. However, there is at present no single book that provides a modern treatment of a core body of knowledge for statistical analysis of network data that cuts across the various disciplines and is organized rather according to a statistical taxonomy of tasks and techniques. This book seeks to ll that gap and, as such, it aims to contribute to a growing trend in recent years to facilitate the exchange of knowledge across the pre-existing boundaries between those disciplines that play a role in what is coming to be called ?network science.

Biographie:
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). G?bor Cs?rdi is a software engineer at RStudio, where he works on R infrastructure packages. He holds a PhD in Computer Science from E?tv?s University, Hungary, and he has done postdocs at the Swiss Institute of Bioinformatics, the University of Lausanne, and Harvard University....

Sommaire:
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.