@misc{goldenberg2009survey,
  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 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.},
  author = {Goldenberg, Anna and Zheng, Alice X and Fienberg, Stephen E and Airoldi, Edoardo M},
  interhash = {bab22de06306d84cf357aadf48982d87},
  intrahash = {5e341981218d7cd89416c3371d56c794},
  note = {cite arxiv:0912.5410Comment: 96 pages, 14 figures, 333 references},
  title = {A survey of statistical network models},
  url = {http://arxiv.org/abs/0912.5410},
  year = 2009
}

{"_id":"DqbM4HEqx6gp5BXsu","bibbaseid":"goldenberg-zheng-fienberg-airoldi-asurveyofstatisticalnetworkmodels-2009","downloads":0,"creationDate":"2017-10-06T04:25:50.926Z","title":"A survey of statistical network models","author_short":["Goldenberg, A.","Zheng, A. X","Fienberg, S. E","Airoldi, E. M"],"year":2009,"bibtype":"misc","biburl":"http://www.bibsonomy.org/bib/author/goldenberg?items=1000","bibdata":{"bibtype":"misc","type":"misc","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 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.","author":[{"propositions":[],"lastnames":["Goldenberg"],"firstnames":["Anna"],"suffixes":[]},{"propositions":[],"lastnames":["Zheng"],"firstnames":["Alice","X"],"suffixes":[]},{"propositions":[],"lastnames":["Fienberg"],"firstnames":["Stephen","E"],"suffixes":[]},{"propositions":[],"lastnames":["Airoldi"],"firstnames":["Edoardo","M"],"suffixes":[]}],"interhash":"bab22de06306d84cf357aadf48982d87","intrahash":"5e341981218d7cd89416c3371d56c794","note":"cite arxiv:0912.5410Comment: 96 pages, 14 figures, 333 references","title":"A survey of statistical network models","url":"http://arxiv.org/abs/0912.5410","year":"2009","bibtex":"@misc{goldenberg2009survey,\n abstract = {Networks are ubiquitous in science and have become a focal point for\r\ndiscussion in everyday life. Formal statistical models for the analysis of\r\nnetwork data have emerged as a major topic of interest in diverse areas of\r\nstudy, and most of these involve a form of graphical representation.\r\nProbability models on graphs date back to 1959. Along with empirical studies in\r\nsocial psychology and sociology from the 1960s, these early works generated an\r\nactive network community and a substantial literature in the 1970s. This effort\r\nmoved into the statistical literature in the late 1970s and 1980s, and the past\r\ndecade has seen a burgeoning network literature in statistical physics and\r\ncomputer science. The growth of the World Wide Web and the emergence of online\r\nnetworking communities such as Facebook, MySpace, and LinkedIn, and a host of\r\nmore specialized professional network communities has intensified interest in\r\nthe study of networks and network data. Our goal in this review is to provide\r\nthe reader with an entry point to this burgeoning literature. We begin with an\r\noverview of the historical development of statistical network modeling and then\r\nwe introduce a number of examples that have been studied in the network\r\nliterature. Our subsequent discussion focuses on a number of prominent static\r\nand dynamic network models and their interconnections. We emphasize formal\r\nmodel descriptions, and pay special attention to the interpretation of\r\nparameters and their estimation. We end with a description of some open\r\nproblems and challenges for machine learning and statistics.},\n author = {Goldenberg, Anna and Zheng, Alice X and Fienberg, Stephen E and Airoldi, Edoardo M},\n interhash = {bab22de06306d84cf357aadf48982d87},\n intrahash = {5e341981218d7cd89416c3371d56c794},\n note = {cite arxiv:0912.5410Comment: 96 pages, 14 figures, 333 references},\n title = {A survey of statistical network models},\n url = {http://arxiv.org/abs/0912.5410},\n year = 2009\n}\n\n","author_short":["Goldenberg, A.","Zheng, A. 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