bibliography.bib

@Manual{Csardi2015,
    title = {igraphdata: A Collection of Network Data Sets for the 'igraph' Package},
    author = {Gabor Csardi},
    year = {2015},
    note = {R package version 1.0.1},
    url = {https://CRAN.R-project.org/package=igraphdata},
  }

@Manual{Handcock2018,
  author = {Mark S. Handcock and David R. Hunter and Carter T. Butts and Steven M. Goodreau and Pavel N. Krivitsky and Martina Morris},
  title = {ergm: Fit, Simulate and Diagnose Exponential-Family Models for
Networks},
  organization = {The Statnet Project (\url{http://www.statnet.org})},
  year = {2018},
  note = {R package version 3.9.4},
  url = {https://CRAN.R-project.org/package=ergm},
}

@Article{hunter2008,
  title = {ergm: A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks},
  author = {David R. Hunter and Mark S. Handcock and Carter T. Butts and Steven M. Goodreau and Martina Morris},
  journal = {Journal of Statistical Software},
  year = {2008},
  volume = {24},
  number = {3},
  pages = {1--29},
}

@article{Handcock2003,
author = {Handcock, Mark S.},
doi = {10.1.1.81.5086},
file = {:home/vegayon/Dropbox/papers/h/Handcock (Technical report 2003).pdf:pdf},
isbn = {0816638934 9780816638932 0816638942 9780816638949},
issn = {1936900X},
journal = {Working Paper No. 39},
number = {39},
pages = {33--50},
pmid = {25671011},
title = {{Assessing Degeneracy in Statistical Models of Social Networks}},
url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.81.5086},
volume = {76},
year = {2003}
}

@article{Geyer1992,
 ISSN = {00359246},
 URL = {http://www.jstor.org/stable/2345852},
 abstract = {Maximum likelihood estimates (MLEs) in autologistic models and other exponential family models for dependent data can be calculated with Markov chain Monte Carlo methods (the Metropolis algorithm or the Gibbs sampler), which simulate ergodic Markov chains having equilibrium distributions in the model. From one realization of such a Markov chain, a Monte Carlo approximant to the whole likelihood function can be constructed. The parameter value (if any) maximizing this function approximates the MLE. When no parameter point in the model maximizes the likelihood, the MLE in the closure of the exponential family may exist and can be calculated by a two-phase algorithm, first finding the support of the MLE by linear programming and then finding the distribution within the family conditioned on the support by maximizing the likelihood for that family. These methods are illustrated by a constrained autologistic model for DNA fingerprint data. MLEs are compared with maximum pseudolikelihood estimates (MPLEs) and with maximum conditional likelihood estimates (MCLEs), neither of which produce acceptable estimates, the MPLE because it overestimates dependence, and the MCLE because conditioning removes the constraints.},
 author = {Charles J. Geyer and Elizabeth A. Thompson},
 journal = {Journal of the Royal Statistical Society. Series B (Methodological)},
 number = {3},
 pages = {657--699},
 publisher = {[Royal Statistical Society, Wiley]},
 title = {Constrained Monte Carlo Maximum Likelihood for Dependent Data},
 volume = {54},
 year = {1992}
}

@Manual{R,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2018},
    url = {https://www.R-project.org/},
  }

@Manual{rmarkdown,
  title = {rmarkdown: Dynamic Documents for R},
  author = {JJ Allaire and Yihui Xie and Jonathan McPherson and Javier Luraschi and Kevin Ushey and Aron Atkins and Hadley Wickham and Joe Cheng and Winston Chang and Richard Iannone},
  year = {2018},
  note = {R package version 1.11},
  url = {https://rmarkdown.rstudio.com},
}

@Manual{knitr,
  title = {knitr: A General-Purpose Package for Dynamic Report Generation in R},
  author = {Yihui Xie},
  year = {2018},
  note = {R package version 1.21},
  url = {https://yihui.name/knitr/},
}

@article{Wasserman1996,
author = {Wasserman, Stanley and Pattison, Philippa},
doi = {10.1007/BF02294547},
file = {:home/vegayon/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Wasserman, Pattison - 1996 - Logit models and logistic regressions for social networks I. An introduction to Markov graphs andp.pdf:pdf},
isbn = {0033-3123, Print},
issn = {0033-3123},
journal = {Psychometrika},
keywords = {categorical data analysis,random graphs,social network analysis},
number = {3},
pages = {401--425},
pmid = {10613111},
title = {{Logit models and logistic regressions for social networks: I. An introduction to Markov graphs andp}},
volume = {61},
year = {1996}
}

@article{Handcock2006,
author = {Handcock, Mark and Wang, Peng and Robins, Garry and Snijders, Tom and Pattison, Philippa},
doi = {10.1016/j.socnet.2006.08.003},
file = {:home/vegayon/Dropbox/papers/r/Robins, Snijders, Wang, Handcock and Pattison (Social Networks 2007).pdf:pdf},
issn = {03788733},
journal = {Social Networks},
keywords = {1986,1991,1996,1999,exponential random graph models,frank,frank and strauss,graph models for,in recent years,interest in exponential random,models,networks,p,pattison and wasserman,random graph,robins et al,see also,social networks,statistical models for social,the class of exponential,there has been growing,wasserman and pattison},
number = {2},
pages = {192--215},
title = {{Recent developments in exponential random graph (p*) models for social networks}},
volume = {29},
year = {2006}
}

@article{Milo2004,
archivePrefix = {arXiv},
arxivId = {cond-mat/0312028},
author = {Milo, R and Kashtan, N and Itzkovitz, S and Newman, M E J and Alon, U},
eprint = {0312028},
file = {:home/vegayon/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Milo et al. - 2004 - On the uniform generation of random graphs with prescribed degree sequences.pdf:pdf},
journal = {Arxiv preprint condmat0312028},
pages = {1--4},
primaryClass = {cond-mat},
title = {{On the uniform generation of random graphs with prescribed degree sequences}},
url = {http://arxiv.org/abs/cond-mat/0312028},
volume = {cond-mat/0},
year = {2004}
}

@article{broido2019,
  title={Scale-free networks are rare},
  author={Broido, Anna D and Clauset, Aaron},
  journal={Nature communications},
  volume={10},
  number={1},
  pages={1017},
  year={2019},
  publisher={Nature Publishing Group}
}

@article{Holme2019,
abstract = {Are scale-free networks rare or universal? Important or not? We present the recent research about degree distributions of networks. This is a controversial topic, but, we argue, with some adjustments of the terminology, it does not have to be.},
author = {Holme, Petter},
doi = {10.1038/s41467-019-09038-8},
issn = {2041-1723},
journal = {Nature Communications},
month = {dec},
number = {1},
pages = {1016},
title = {{Rare and everywhere: Perspectives on scale-free networks}},
url = {https://doi.org/10.1038/s41467-019-09038-8 http://www.nature.com/articles/s41467-019-09038-8},
volume = {10},
year = {2019}
}

@inproceedings{Kim2017,
 author = {Kim, Young Ji and Engel, David and Woolley, Anita Williams and Lin, Jeffrey Yu-Ting and McArthur, Naomi and Malone, Thomas W.},
 title = {What Makes a Strong Team?: Using Collective Intelligence to Predict Team Performance in League of Legends},
 booktitle = {Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing},
 series = {CSCW '17},
 year = {2017},
 isbn = {978-1-4503-4335-0},
 location = {Portland, Oregon, USA},
 pages = {2316--2329},
 numpages = {14},
 url = {http://doi.acm.org/10.1145/2998181.2998185},
 doi = {10.1145/2998181.2998185},
 acmid = {2998185},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {collective intelligence, online collaboration, online games, team performance, virtual teams},
} 

@article{sampson1969novitiate,
  title={A novitiate in a period of change: An experimental and case study of social relationships.},
  author={Sampson, Samuel F},
  year={1969}
}

@Manual{vegayon2018,
  title = {{{ergmito: Exponential Random Graph Models for Small Networks}}},
  author = {George {Vega Yon}},
  year = {2018},
  note = {R package version 0.1-0},
  url = {https://github.com/muriteams/ergmito},
}

@Misc{vegayon2019,
  title = {{Exponential Random Graph models for Little Networks}},
  author = {George {Vega Yon} and Kayla {de la Haye}},
  year = {{2019}},
  eprint = {{arXiv:1904.10406}},
}

@article{Hayes1996, 
author={Hayes,A.},
year={1996},
month={06},
title={Permutation test is not distribution-free: testing H\+v-v\: rho = 0},
journal={Psychological methods},
volume={1},
number={2},
pages={184-198},
note={Date revised - 2001-08-07; Document feature - il. tables. refs; Last updated - 2016-09-27; SubjectsTermNotLitGenreText - Permutations; Errors},
abstract={Four simulations were conducted to compare the validity of the permutation test to the t test for testing the hypothesis that a population correlation equals zero. When sampling from nonnormal populations, the t test and the permutation test performed similarly except when extreme nonnormality was combined with a small sample size. Considers the situations in which the permutation test can be recommended. (Original abstract-amended)},
keywords={Permutations; Errors},
isbn={1082-989X, 1082-989X},
language={English},
url={http://libproxy.usc.edu/login?url=https://search-proquest-com.libproxy1.usc.edu/docview/57591004?accountid=14749},
}