A Survey of Statistical Network Models

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A Survey of Statistical Network Models

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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

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  • FAS Scholarly Articles [7594]
    Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University

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