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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.
About the author
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.
Summary
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.
Additional text
From the reviews:
“…Accessible and easy to read…strikes a balance between concepts and mathematical detail. …This book is a superb introduction to a fascinating area.” (International Statistical Review, 2010, 78, 1, 134-159) “Many disciplines are nowadays involved in network modeling, but it appears as if a common methodological foundation is lacking. The objective of this book is to provide a first attempt at defining such a common methodological foundation from a statistical point of view. … The style of the writing is excellent. … ample references allow quick access to further literature. I can recommend this book to anyone with a serious statistical interest in networks.” (Fred van Eeuwijk, VOC Nieuwsbrief, Issue 44, May, 2010)
“Any reader interested in networks and wanting a perspective beyond that of any single discipline should acquire this book. … Researchers will also appreciate the many points in the book where important open problems are identified. The book can also serve readily and flexibly as the main textbook for either a graduate-level seminar course or for an informally organized reading group. … This book sets itself the challenge of addressing statistics for network science broadly, and in the many ways already noted, it is successful.” (Michael Frey, Technometrics, Vol. 54 (1), February, 2012)
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From the reviews:
"...Accessible and easy to read...strikes a balance between concepts and mathematical detail. ...This book is a superb introduction to a fascinating area." (International Statistical Review, 2010, 78, 1, 134-159) "Many disciplines are nowadays involved in network modeling, but it appears as if a common methodological foundation is lacking. The objective of this book is to provide a first attempt at defining such a common methodological foundation from a statistical point of view. ... The style of the writing is excellent. ... ample references allow quick access to further literature. I can recommend this book to anyone with a serious statistical interest in networks." (Fred van Eeuwijk, VOC Nieuwsbrief, Issue 44, May, 2010)
"Any reader interested in networks and wanting a perspective beyond that of any single discipline should acquire this book. ... Researchers will also appreciate the many points in the book where important open problems are identified. The book can also serve readily and flexibly as the main textbook for either a graduate-level seminar course or for an informally organized reading group. ... This book sets itself the challenge of addressing statistics for network science broadly, and in the many ways already noted, it is successful." (Michael Frey, Technometrics, Vol. 54 (1), February, 2012)
