A survey of statistical network models
A survey of statistical network models [electronic resource] /
Statistical network models.
Anna Goldenberg, Alice X. Zheng, Stephen E. Fienberg and Edoardo M. Airoldi.
- Hanover, Mass. : Now Publishers, c2010.
- 1 electronic text (p. [129]-233 : ill. (some col.)) : digital file.
- Foundations and trends in machine learning, v. 2, issue 2, p. 129-233 1935-8245 ; .
- Foundations and trends in machine learning (Online), v. 2, issue 2, p. 129-233. .
Title from PDF (viewed on March 2, 2010).
Includes bibliographical references (p. 212-233).
1. Introduction -- 2. Motivation and data-set examples -- 3. Static network models -- 4. Dynamic models for longitudinal data -- 5. Issues in network modeling -- 6. Summary -- Acknowledgments -- References.
Restricted to subscribers or individual document purchasers.
Google Scholar Google Book Search INSPEC Scopus ACM Computing Guide DBPLP Computer Science Bibliography Zentralblatt MATH Database AMS MathSciNet ACM Computing Reviews
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.
Anna Goldenberg, Alice X. Zheng, Stephen E. Fienberg and Edoardo M. Airoldi (2009) "A Survey of Statistical Network Models", Foundations and Trends in Machine Learning: Vol. 2: No 2, pp 129-233.
Mode of access: World Wide Web.
System requirements: Adobe Acrobat Reader.
9781601983213 (electronic)
10.1561/2200000005 doi
Computer networks--Statistical methods.
Network computers--Statistical methods.
System analysis--Statistical methods.
Statistics.
TK5105.5 / .S877 2010 QA402 / .S877 2010
004.6
Title from PDF (viewed on March 2, 2010).
Includes bibliographical references (p. 212-233).
1. Introduction -- 2. Motivation and data-set examples -- 3. Static network models -- 4. Dynamic models for longitudinal data -- 5. Issues in network modeling -- 6. Summary -- Acknowledgments -- References.
Restricted to subscribers or individual document purchasers.
Google Scholar Google Book Search INSPEC Scopus ACM Computing Guide DBPLP Computer Science Bibliography Zentralblatt MATH Database AMS MathSciNet ACM Computing Reviews
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.
Anna Goldenberg, Alice X. Zheng, Stephen E. Fienberg and Edoardo M. Airoldi (2009) "A Survey of Statistical Network Models", Foundations and Trends in Machine Learning: Vol. 2: No 2, pp 129-233.
Mode of access: World Wide Web.
System requirements: Adobe Acrobat Reader.
9781601983213 (electronic)
10.1561/2200000005 doi
Computer networks--Statistical methods.
Network computers--Statistical methods.
System analysis--Statistical methods.
Statistics.
TK5105.5 / .S877 2010 QA402 / .S877 2010
004.6