A Survey of Statistical Network Models

A Survey of Statistical Network Models

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A Survey of Statistical Network Models

dc.contributor.author	Airoldi, Edoardo Maria
dc.contributor.author	Goldenberg, Anna
dc.contributor.author	Zheng, Alice
dc.contributor.author	Fienberg, Stephen
dc.date.accessioned	2011-01-06T15:35:27Z
dc.date.issued	2009
dc.identifier.citation	Goldenberg, Anna, Alice X. Zheng, Stephen E. Fienberg, and Edoardo M. Airoldi. A survey of statistical network models. 2009. Machine Learning 2(2): 129-233.	en_US
dc.identifier.issn	0885-6125	en_US
dc.identifier.uri	http://nrs.harvard.edu/urn-3:HUL.InstRepos:4645865
dc.description.abstract	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 liter- ature in the 1970s. This effort moved into the statistical literature in the late 1970s and 1980s, and the past decade has seen a burgeoning net- work literature in statistical physics and computer science. The growthof the World Wide Web and the emergence of online “networking com- munities” 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 for- mal 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.	en_US
dc.description.sponsorship	Statistics	en_US
dc.language.iso	en_US	en_US
dc.publisher	Springer Verlag	en_US
dc.relation.isversionof	doi:10.1561/2200000005	en_US
dc.relation.hasversion	http://arxiv.org/abs/0912.5410/	en_US
dash.license	OAP
dc.title	A Survey of Statistical Network Models	en_US
dc.type	Journal Article	en_US
dc.description.version	Accepted Manuscript	en_US
dc.relation.journal	Machine Learning -Boston-	en_US
dash.depositing.author	Airoldi, Edoardo Maria
dc.date.available	2011-01-06T15:35:27Z