bibliography.bib

@article{hoff2002,
author = {Peter D Hoff and Adrian E Raftery and Mark S Handcock},
title = {Latent Space Approaches to Social Network Analysis},
journal = {Journal of the American Statistical Association},
volume = {97},
number = {460},
pages = {1090-1098},
year  = {2002},
publisher = {Taylor & Francis},
doi = {10.1198/016214502388618906},

URL = { 
        https://doi.org/10.1198/016214502388618906
    
},
eprint = { 
        https://doi.org/10.1198/016214502388618906
    
}

}


@article{Hunter2008,
  author = {Hunter, David R. and Handcock, Mark S. and Butts, Carter T. and Goodreau, Steven M. and Morris, Martina},
  doi = {10.18637/jss.v024.i03},
  issn = {1548-7660},
  journal = {Journal of Statistical Software},
  number = {3},
  title = {{ergm : A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks}},
  url = {http://www.jstatsoft.org/v24/i03/},
  volume = {24},
  year = {2008}
}

@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}
}

@book{lusher2012,
  title={Exponential random graph models for social networks: Theory, methods, and applications},
  author={Lusher, Dean and Koskinen, Johan and Robins, Garry},
  year={2012},
  publisher={Cambridge University Press}
}


@article{Snijders2010,
abstract = {Stochastic actor-based models are models for network dynamics that can represent a wide variety of influences on network change, and allow to estimate parameters expressing such influences, and test corresponding hypotheses. The nodes in the network represent social actors, and the collection of ties represents a social relation. The assumptions posit that the network evolves as a stochastic process 'driven by the actors', i.e., the model lends itself especially for representing theories about how actors change their outgoing ties. The probabilities of tie changes are in part endogenously determined, i.e., as a function of the current network structure itself, and in part exogenously, as a function of characteristics of the nodes ('actor covariates') and of characteristics of pairs of nodes ('dyadic covariates'). In an extended form, stochastic actor-based models can be used to analyze longitudinal data on social networks jointly with changing attributes of the actors: dynamics of networks and behavior. This paper gives an introduction to stochastic actor-based models for dynamics of directed networks, using only a minimum of mathematics. The focus is on understanding the basic principles of the model, understanding the results, and on sensible rules for model selection. Crown Copyright {\textcopyright} 2009.},
author = {Snijders, Tom A B and van de Bunt, Gerhard G. and Steglich, Christian E G},
doi = {10.1016/j.socnet.2009.02.004},
file = {:home/george/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Snijders, van de Bunt, Steglich - 2010 - Introduction to stochastic actor-based models for network dynamics(2).pdf:pdf},
isbn = {0378-8733},
issn = {03788733},
journal = {Social Networks},
keywords = {Agent-based model,Longitudinal,Markov chain,Peer influence,Peer selection,Statistical modeling},
number = {1},
pages = {44--60},
title = {{Introduction to stochastic actor-based models for network dynamics}},
volume = {32},
year = {2010}
}

@article{Snijders2002,
  title={Markov chain Monte Carlo estimation of exponential random graph models},
  author={Snijders, Tom AB},
  journal={Journal of Social Structure},
  volume=3,
  year={2002}
}

@article{Wang2009,
  title = "Exponential random graph (p*) models for affiliation networks",
  journal = "Social Networks",
  volume = "31",
  number = "1",
  pages = "12 - 25",
  year = "2009",
  issn = "0378-8733",
  doi = "https://doi.org/10.1016/j.socnet.2008.08.002",
  url = "http://www.sciencedirect.com/science/article/pii/S0378873308000403",
  author = "Peng Wang and Ken Sharpe and Garry L. Robins and Philippa E. Pattison",
  keywords = "Exponential random graph () models, Affiliation networks, MCMC MLE, Partial conditional dependence assumption"
}

@techreport{admiraal2006,
  title={Sequential importance sampling for bipartite graphs with applications to likelihood-based inference},
  author={Admiraal, Ryan and Handcock, Mark S},
  year={2006},
  institution={Department of Statistics, University of Washington}
}

@ARTICLE{Chandrasekhar2012,
   author = {{Chandrasekhar}, A.~G. and {Jackson}, M.~O.},
    title = "{Tractable and Consistent Random Graph Models}",
  journal = {ArXiv e-prints},
archivePrefix = "arXiv",
   eprint = {1210.7375},
 primaryClass = "physics.soc-ph",
 keywords = {Physics - Physics and Society, Computer Science - Social and Information Networks},
     year = 2012,
    month = oct,
   adsurl = {http://adsabs.harvard.edu/abs/2012arXiv1210.7375C},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@article{Shalizi2011,
abstract = {The authors consider processes on social networks that can potentially involve three factors: homophily, or the formation of social ties due to matching individual traits; social contagion, also known as social influence; and the causal effect of an individual's covariates on his or her behavior or other measurable responses. The authors show that generically, all of these are confounded with each other. Distinguishing them from one another requires strong assumptions on the parametrization of the social process or on the adequacy of the covariates used (or both). In particular the authors demonstrate, with simple examples, that asymmetries in regression coefficients cannot identify causal effects and that very simple models of imitation (a form of social contagion) can produce substantial correlations between an individual's enduring traits and his or her choices, even when there is no intrinsic affinity between them. The authors also suggest some possible constructive responses to these results.},
archivePrefix = {arXiv},
arxivId = {1004.4704},
author = {Shalizi, Cosma Rohilla and Thomas, Andrew C},
doi = {10.1177/0049124111404820},
eprint = {1004.4704},
isbn = {0049-1241 (Print)$\backslash$r0049-1241 (Linking)},
issn = {0049-1241},
journal = {Sociological methods {\&} research},
number = {2},
pages = {211--239},
pmid = {22523436},
title = {{Homophily and Contagion Are Generically Confounded in Observational Social Network Studies.}},
url = {http://arxiv.org/abs/1004.4704},
volume = {40},
year = {2011}
}

@article{LeSage2008,
abstract = {An introduction to spatial econometric models and methods is provided that discusses spatial autoregressive processes that can be used to extend conventional regression models. Estimation and interpretation of these models are illustrated with an applied example that examines the relationship between commuting to work times and transportation mode choice for a sample of 3,110 US counties in the year 2000. These extensions to conventional regression models are useful when modeling cross-sectional regional observations or and panel data samples collected from regions over both space and time can be easily implemented using publicly available software. Use of these models for the case of non-spatial structured dependence is also discussed.},
author = {LeSage, James P.},
doi = {10.4000/rei.3887},
isbn = {978-1420064247},
issn = {0154-3229},
journal = {Revue d'{\'{e}}conomie industrielle},
keywords = {Spatial Autoregressive Processes,Spatial Dependence,Spatial Econometrics,d{\'{e}}pendance spatiale,processus spatial autor{\'{e}}gressif,{\'{e}}conom{\'{e}}trie spatiale},
number = {123},
pages = {19--44},
pmid = {578345366},
title = {{An Introduction to Spatial Econometrics}},
url = {http://rei.revues.org/3887},
volume = {123},
year = {2008}
}

@article{Aral2009,
abstract = {Node characteristics and behaviors are often correlated with the structure of social networks over time. While evidence of this type of assortative mixing and temporal clustering of behaviors among linked nodes is used to support claims of peer influence and social contagion in networks, homophily may also explain such evidence. Here we develop a dynamic matched sample estimation framework to distinguish influence and homophily effects in dynamic networks, and we apply this framework to a global instant messaging network of 27.4 million users, using data on the day-by-day adoption of a mobile service application and users' longitudinal behavioral, demographic, and geographic data. We find that previous methods overestimate peer influence in product adoption decisions in this network by 300-700{\%}, and that homophily explains {\textgreater}50{\%} of the perceived behavioral contagion. These findings and methods are essential to both our understanding of the mechanisms that drive contagions in networks and our knowledge of how to propagate or combat them in domains as diverse as epidemiology, marketing, development economics, and public health.},
archivePrefix = {arXiv},
arxivId = {arXiv:1408.1149},
author = {Aral, Sinan and Muchnik, Lev and Sundararajan, Arun},
doi = {10.1073/pnas.0908800106},
eprint = {arXiv:1408.1149},
isbn = {00278424},
issn = {0027-8424},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
number = {51},
pages = {21544--21549},
pmid = {20007780},
title = {{Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks.}},
volume = {106},
year = {2009}
}


@article{Stadtfeld2017,
author = {Christoph Stadtfeld and James Hollway and Per Block},
title ={Dynamic Network Actor Models: Investigating Coordination Ties through Time},
journal = {Sociological Methodology},
volume = {47},
number = {1},
pages = {1-40},
year = {2017},
doi = {10.1177/0081175017709295},

URL = { 
        https://doi.org/10.1177/0081175017709295
    
},
eprint = { 
        https://doi.org/10.1177/0081175017709295
    
}
,
    abstract = { Important questions in the social sciences are concerned with the circumstances under which individuals, organizations, or states mutually agree to form social network ties. Examples of these coordination ties are found in such diverse domains as scientific collaboration, international treaties, and romantic relationships and marriage. This article introduces dynamic network actor models (DyNAM) for the statistical analysis of coordination networks through time. The strength of the models is that they explicitly address five aspects about coordination networks that empirical researchers will typically want to take into account: (1) that observations are dependent, (2) that ties reflect the opportunities and preferences of both actors involved, (3) that the creation of coordination ties is a two-sided process, (4) that data might be available in a time-stamped format, and (5) that processes typically differ between tie creation and dissolution (signed processes), shorter and longer time windows (windowed processes), and initial and repeated creation of ties (weighted processes). Two empirical case studies demonstrate the potential impact of DyNAM models: The first is concerned with the formation of romantic relationships in a high school over 18 months, and the second investigates the formation of international fisheries treaties from 1947 to 2010. }
}


@article{Desmarais2012,
    author = {Desmarais, Bruce A. AND Cranmer, Skyler J.},
    journal = {PLOS ONE},
    publisher = {Public Library of Science},
    title = {Statistical Inference for Valued-Edge Networks: The Generalized Exponential Random Graph Model},
    year = {2012},
    month = {01},
    volume = {7},
    url = {https://doi.org/10.1371/journal.pone.0030136},
    pages = {1-12},
    abstract = {Across the sciences, the statistical analysis of networks is central to the production of knowledge on relational phenomena. Because of their ability to model the structural generation of networks based on both endogenous and exogenous factors, exponential random graph models are a ubiquitous means of analysis. However, they are limited by an inability to model networks with valued edges. We address this problem by introducing a class of generalized exponential random graph models capable of modeling networks whose edges have continuous values (bounded or unbounded), thus greatly expanding the scope of networks applied researchers can subject to statistical analysis.},
    number = {1},
    doi = {10.1371/journal.pone.0030136}
}

@article{Snijders2011,
abstract = {Statistical models for social networks as dependent variables must represent the typical network dependencies between tie variables such as reciprocity, homophily, transitivity, etc. This review first treats models for single (cross-sectionally observed) networks and then for network dynamics. For single networks, the older literature concentrated on conditionally uniform models. Various types of latent space models have been developed: for discrete, general metric, ultrametric, Euclidean, and partially ordered spaces. Exponential random graph models were proposed long ago but now are applied more and more thanks to the non-Markovian social circuit specifications that were recently proposed. Modeling network dynamics is less complicated than modeling single network observations because dependencies are spread out in time. For modeling network dynamics, continuous-time models are more fruitful. Actor-oriented models here provide a model that can represent many dependencies in a flexible way. Strong model d...},
author = {Snijders, Tom A. B.},
doi = {10.1146/annurev.soc.012809.102709},
isbn = {0360-0572$\backslash$r1545-2115},
issn = {0360-0572},
journal = {Annual Review of Sociology},
keywords = {inference,social networks,statistical modeling},
number = {1},
pages = {131--153},
pmid = {18981063},
title = {{Statistical Models for Social Networks}},
volume = {37},
year = {2011}
}

@article{Butts2008,
author = {Carter T. Butts},
title ={4. A Relational Event Framework for Social Action},
journal = {Sociological Methodology},
volume = {38},
number = {1},
pages = {155-200},
year = {2008},
doi = {10.1111/j.1467-9531.2008.00203.x},

URL = { 
        https://doi.org/10.1111/j.1467-9531.2008.00203.x
    
},
eprint = { 
        https://doi.org/10.1111/j.1467-9531.2008.00203.x
    
}
,
    abstract = { Social behavior over short time scales is frequently understood in terms of actions, which can be thought of as discrete events in which one individual emits a behavior directed at one or more other entities in his or her environment (possibly including himself or herself). Here, we introduce a highly flexible framework for modeling actions within social settings, which permits likelihood-based inference for behavioral mechanisms with complex dependence. Examples are given for the parameterization of base activity levels, recency, persistence, preferential attachment, transitive/cyclic interaction, and participation shifts within the relational event framework. Parameter estimation is discussed both for data in which an exact history of events is available, and for data in which only event sequences are known. The utility of the framework is illustrated via an application to dynamic modeling of responder radio communications during the early hours of the World Trade Center disaster. }
}


@ARTICLE{Daraganova2013,
author={Daraganova, G. and Robins, G.},
title={Autologistic actor attribute models},
journal={Exponential Random Graph Models for Social Networks: Theory, Methods and Applications},
year={2013},
pages={102-114},
note={cited By 13},
source={Scopus},
}

@article{Kashima2013,
title = "The acquisition of perceived descriptive norms as social category learning in social networks",
journal = "Social Networks",
volume = "35",
number = "4",
pages = "711 - 719",
year = "2013",
issn = "0378-8733",
doi = "https://doi.org/10.1016/j.socnet.2013.06.002",
url = "http://www.sciencedirect.com/science/article/pii/S0378873313000531",
author = "Yoshihisa Kashima and Samuel Wilson and Dean Lusher and Leonie J. Pearson and Craig Pearson",
keywords = "Norm learning, Descriptive norms, Social networks, Social category, Category learning"
}

@book{lazega2015,
  title={Multilevel network analysis for the social sciences: theory, methods and applications},
  author={Lazega, Emmanuel and Snijders, Tom AB},
  volume={12},
  year={2015},
  publisher={Springer}
}

@article{Ripley2011,
  author = {Ripley, Ruth M. and Snijders, Tom AB and Preciado, Paulina and Others},
  journal = {University of Oxford: Department of Statistics, Nuffield College},
  number = {2007},
  title = {{Manual for RSIENA}},
  url = {https://www.uni-due.de/hummell/sna/R/RSiena{\_}Manual.pdf},
  year = {2011}
}

@article{Imbens2009,
Author = {Imbens, Guido W. and Wooldridge, Jeffrey M.},
Title = {Recent Developments in the Econometrics of Program Evaluation},
Journal = {Journal of Economic Literature},
Volume = {47},
Number = {1},
Year = {2009},
Month = {March},
Pages = {5-86},
DOI = {10.1257/jel.47.1.5},
URL = {http://www.aeaweb.org/articles?id=10.1257/jel.47.1.5}}

@article{sekhon2008neyman,
  title={The Neyman-Rubin model of causal inference and estimation via matching methods},
  author={Sekhon, Jasjeet S},
  journal={The Oxford handbook of political methodology},
  volume={2},
  year={2008},
  publisher={Oxford University Press Oxford}
}

@article{king2016propensity,
  title={Why propensity scores should not be used for matching},
  author={King, Gary and Nielsen, Richard},
  year={2016}
}