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A Survey of Statistical Network Models
Contributor(s): Goldenberg, Anna (Author), Zheng, Alice X. (Author), Fienberg, Stephen E. (Author)

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 ISBN: 1601983204     ISBN-13: 9781601983206
Publisher: Now Publishers
OUR PRICE: $80.75  

Binding Type: Paperback - See All Available Formats & Editions
Published: January 2010
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Additional Information
BISAC Categories:
- Computers | Machine Theory
- Computers | Mathematical & Statistical Software
Dewey: 004.6
Series: Foundations and Trends(r) in Machine Learning
Physical Information: 0.25" H x 6.14" W x 9.21" L (0.39 lbs) 120 pages
 
Descriptions, Reviews, Etc.
Publisher Description:
Networks are ubiquitous in science and have become a focal point for discussion in everyday life. Formal statistical models for the analysis of network data have emerged as a major topic of interest in diverse areas of study, and most of these involve a form of graphical representation. Probability models on graphs date back to 1959. Along with empirical studies in social psychology and sociology from the 1960s, these early works generated an active network community and a substantial literature in the 1970s. This effort moved into the statistical literature in the late 1970s and 1980s, and the past decade has seen a burgeoning network literature in statistical physics and computer science. The growth of the World Wide Web and the emergence of online networking communities such as Facebook, MySpace, and LinkedIn, and a host of more specialized professional network communities has intensified interest in the study of networks and network data. Our goal in this review is to provide the reader with an entry point to this burgeoning literature. We begin with an overview of the historical development of statistical network modeling and then we introduce a number of examples that have been studied in the network literature. Our subsequent discussion focuses on a number of prominent static and dynamic network models and their interconnections. We emphasize formal model descriptions, and pay special attention to the interpretation of parameters and their estimation. We end with a description of some open problems and challenges for machine learning and statistics.
 
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