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}
}

@techreport{handcock2003,
  title={Assessing degeneracy in statistical models of social networks},
  author={Handcock, Mark S},
  year={2003}
}

@article{Schweinberger2015,
author = {Schweinberger, Michael and Handcock, Mark S.},
title = {Local dependence in random graph models: characterization, properties and statistical inference},
journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
volume = {77},
number = {3},
pages = {647-676},
year=2015,
keywords = {Exponential families, Local dependence, M-dependence, Model degeneracy, Social networks, Weak dependence},
doi = {10.1111/rssb.12081},
url = {https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/rssb.12081},
eprint = {https://rss.onlinelibrary.wiley.com/doi/pdf/10.1111/rssb.12081},
abstract = {Summary Dependent phenomena, such as relational, spatial and temporal phenomena, tend to be characterized by local dependence in the sense that units which are close in a well-defined sense are dependent. In contrast with spatial and temporal phenomena, though, relational phenomena tend to lack a natural neighbourhood structure in the sense that it is unknown which units are close and thus dependent. Owing to the challenge of characterizing local dependence and constructing random graph models with local dependence, many conventional exponential family random graph models induce strong dependence and are not amenable to statistical inference. We take first steps to characterize local dependence in random graph models, inspired by the notion of finite neighbourhoods in spatial statistics and M-dependence in time series, and we show that local dependence endows random graph models with desirable properties which make them amenable to statistical inference. We show that random graph models with local dependence satisfy a natural domain consistency condition which every model should satisfy, but conventional exponential family random graph models do not satisfy. In addition, we establish a central limit theorem for random graph models with local dependence, which suggests that random graph models with local dependence are amenable to statistical inference. We discuss how random graph models with local dependence can be constructed by exploiting either observed or unobserved neighbourhood structure. In the absence of observed neighbourhood structure, we take a Bayesian view and express the uncertainty about the neighbourhood structure by specifying a prior on a set of suitable neighbourhood structures. We present simulation results and applications to two real world networks with ‘ground truth’.}
}


@article{robbins1951,
author = "Robbins, Herbert and Monro, Sutton",
doi = "10.1214/aoms/1177729586",
fjournal = "The Annals of Mathematical Statistics",
journal = "Ann. Math. Statist.",
month = "09",
number = "3",
pages = "400--407",
publisher = "The Institute of Mathematical Statistics",
title = "A Stochastic Approximation Method",
url = "https://doi.org/10.1214/aoms/1177729586",
volume = "22",
year = "1951"
}

@article{Frank1986,
author = { Ove   Frank  and  David   Strauss },
title = {Markov Graphs},
journal = {Journal of the American Statistical Association},
volume = {81},
number = {395},
pages = {832-842},
year  = {1986},
publisher = {Taylor & Francis},
doi = {10.1080/01621459.1986.10478342},

URL = { 
        https://www.tandfonline.com/doi/abs/10.1080/01621459.1986.10478342
    
},
eprint = { 
        https://www.tandfonline.com/doi/pdf/10.1080/01621459.1986.10478342
    
}

}

@article{ROBINS2007a,
title = "Recent developments in exponential random graph (p*) models for social networks",
journal = "Social Networks",
volume = "29",
number = "2",
pages = "192 - 215",
year = "2007",
note = "Special Section: Advances in Exponential Random Graph (p*) Models",
issn = "0378-8733",
doi = "https://doi.org/10.1016/j.socnet.2006.08.003",
url = "http://www.sciencedirect.com/science/article/pii/S0378873306000384",
author = "Garry Robins and Tom Snijders and Peng Wang and Mark Handcock and Philippa Pattison",
keywords = "Exponential random graph models, * models, Statistical models for social networks",
abstract = "This article reviews new specifications for exponential random graph models proposed by Snijders et al. [Snijders, T.A.B., Pattison, P., Robins, G.L., Handcock, M., 2006. New specifications for exponential random graph models. Sociological Methodology] and demonstrates their improvement over homogeneous Markov random graph models in fitting empirical network data. Not only do the new specifications show improvements in goodness of fit for various data sets, but they also help to avoid the problem of near-degeneracy that often afflicts the fitting of Markov random graph models in practice, particularly to network data exhibiting high levels of transitivity. The inclusion of a new higher order transitivity statistic allows estimation of parameters of exponential graph models for many (but not all) cases where it is impossible to estimate parameters of homogeneous Markov graph models. The new specifications were used to model a large number of classical small-scale network data sets and showed a dramatically better performance than Markov graph models. We also review three current programs for obtaining maximum likelihood estimates of model parameters and we compare these Monte Carlo maximum likelihood estimates with less accurate pseudo-likelihood estimates. Finally, we discuss whether homogeneous Markov random graph models may be superseded by the new specifications, and how additional elaborations may further improve model performance."
}

@article{ROBINS2007b,
title = "An introduction to exponential random graph (p*) models for social networks",
journal = "Social Networks",
volume = "29",
number = "2",
pages = "173 - 191",
year = "2007",
note = "Special Section: Advances in Exponential Random Graph (p*) Models",
issn = "0378-8733",
doi = "https://doi.org/10.1016/j.socnet.2006.08.002",
url = "http://www.sciencedirect.com/science/article/pii/S0378873306000372",
author = "Garry Robins and Pip Pattison and Yuval Kalish and Dean Lusher",
keywords = "Exponential random graph models, Statistical models for social networks,  models",
abstract = "This article provides an introductory summary to the formulation and application of exponential random graph models for social networks. The possible ties among nodes of a network are regarded as random variables, and assumptions about dependencies among these random tie variables determine the general form of the exponential random graph model for the network. Examples of different dependence assumptions and their associated models are given, including Bernoulli, dyad-independent and Markov random graph models. The incorporation of actor attributes in social selection models is also reviewed. Newer, more complex dependence assumptions are briefly outlined. Estimation procedures are discussed, including new methods for Monte Carlo maximum likelihood estimation. We foreshadow the discussion taken up in other papers in this special edition: that the homogeneous Markov random graph models of Frank and Strauss [Frank, O., Strauss, D., 1986. Markov graphs. Journal of the American Statistical Association 81, 832–842] are not appropriate for many observed networks, whereas the new model specifications of Snijders et al. [Snijders, T.A.B., Pattison, P., Robins, G.L., Handock, M. New specifications for exponential random graph models. Sociological Methodology, in press] offer substantial improvement."
}


@article{Moran1950,
author = {Moran, P. A. P.},
doi = {10.2307/2332142},
file = {:home/vegayon/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Moran - 1950 - Notes on Continuous Stochastic Phenomena.pdf:pdf},
issn = {00063444},
journal = {Biometrika},
month = {jun},
number = {1/2},
pages = {17},
title = {{Notes on Continuous Stochastic Phenomena}},
url = {http://www.jstor.org/stable/2332142?origin=crossref},
volume = {37},
year = {1950}
}

@article{Hunter2006,
author = {Hunter, David R and Handcock, Mark S},
doi = {10.1198/106186006X133069},
issn = {1061-8600},
journal = {Journal of Computational and Graphical Statistics},
month = {sep},
number = {3},
pages = {565--583},
title = {{Inference in Curved Exponential Family Models for Networks}},
url = {http://www.tandfonline.com/doi/abs/10.1198/106186006X133069},
volume = {15},
year = {2006}
}

@book{bivand2008,
  title={Applied spatial data analysis with R},
  author={Bivand, Roger S and Pebesma, Edzer J and Gomez-Rubio, Virgilio and Pebesma, Edzer Jan},
  volume={747248717},
  year={2008},
  publisher={Springer}
}

@article{ripley2011,
  title={Manual for RSIENA},
  author={Ripley, Ruth M and Snijders, Tom AB and Boda, Zsofia and V{\"o}r{\"o}s, Andr{\'a}s and Preciado, Paulina},
  journal={University of Oxford, Department of Statistics, Nuffield College},
  volume={1},
  year={2011}
}

@article{Steglich2010,
author = {Steglich, Christian and Snijders, Tom A. B. and Pearson, Michael},
doi = {10.1111/j.1467-9531.2010.01225.x},
issn = {0081-1750},
journal = {Sociological Methodology},
month = {aug},
number = {1},
pages = {329--393},
title = {{8. Dynamic Networks and Behavior: Separating Selection from Influence}},
url = {http://journals.sagepub.com/doi/10.1111/j.1467-9531.2010.01225.x},
volume = {40},
year = {2010}
}

@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},
file = {:home/vegayon/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Snijders - 2011 - Statistical Models for Social Networks.pdf:pdf},
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{LeSage2014,
abstract = {There is near universal agreement that estimates and inferences from spatial regression models are sensitive to particular specifications used for the spatial weight structure in these models. We find little theoretical basis for this commonly held belief, if estimates and inferences are based on the true partial derivatives for a well-specified spatial regression model. We conclude that this myth may have arisen from past applied work that incorrectly interpreted the model coefficients as if they were partial derivatives, or from use of mis-specified models.},
author = {LeSage, James P. and Pace, R Kelley},
doi = {10.2139/ssrn.1725503},
file = {:home/vegayon/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/LeSage, Pace - 2014 - The Biggest Myth in Spatial Econometrics.pdf:pdf},
issn = {1556-5068},
journal = {Econometrics},
keywords = {direct and indirect effects estimates,sensitivity to spatial weights},
number = {4},
pages = {217--249},
title = {{The Biggest Myth in Spatial Econometrics}},
volume = {2},
year = {2014}
}

@article{Krivitsky2014,
abstract = {Models of dynamic networks --- networks that evolve over time --- have manifold applications. We develop a discrete-time generative model for social network evolution that inherits the richness and flexibility of the class of exponential-family random graph models. The model --- a Separable Temporal ERGM (STERGM) --- facilitates separable modeling of the tie duration distributions and the structural dynamics of tie formation. We develop likelihood-based inference for the model, and provide computational algorithms for maximum likelihood estimation. We illustrate the interpretability of the model in analyzing a longitudinal network of friendship ties within a school.},
archivePrefix = {arXiv},
arxivId = {1011.1937},
author = {Krivitsky, Pavel N. and Handcock, Mark S.},
doi = {10.1111/rssb.12014},
eprint = {1011.1937},
file = {:home/vegayon/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Krivitsky, Handcock - 2014 - A separable model for dynamic networks.pdf:pdf},
issn = {13697412},
journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
keywords = {carlo methods,exponential random-graph model,longitudinal network,markov chain monte,maximum likelihood estimation,social networks},
month = {jan},
number = {1},
pages = {29--46},
title = {{A separable model for dynamic networks}},
url = {http://arxiv.org/abs/1011.1937 http://dx.doi.org/10.1111/rssb.12014 http://doi.wiley.com/10.1111/rssb.12014},
volume = {76},
year = {2014}
}

@book{elhorst2013,
  title={Spatial Econometrics: From Cross-Sectional Data to Spatial Panels},
  author={Elhorst, J.P.},
  isbn={9783642403408},
  lccn={2013946223},
  series={SpringerBriefs in Regional Science},
  url={https://books.google.cl/books?id=mu25BAAAQBAJ},
  year={2013},
  publisher={Springer Berlin Heidelberg}
}

@article{Kelejian2010,
abstract = {This study develops a methodology of inference for a widely used Cliff-Ord type spatial model containing spatial lags in the dependent variable, exogenous variables, and the disturbance terms, while allowing for unknown heteroskedasticity in the innovations. We first generalize the GMM estimator suggested in Kelejian and Prucha (1998, 1999) for the spatial autoregressive parameter in the disturbance process. We also define IV estimators for the regression parameters of the model and give results concerning the joint asymptotic distribution of those estimators and the GMM estimator. Much of the theory is kept general to cover a wide range of settings. ?? 2009 Elsevier B.V. All rights reserved.},
archivePrefix = {arXiv},
arxivId = {NIHMS150003},
author = {Kelejian, Harry H. and Prucha, Ingmar R.},
doi = {10.1016/j.jeconom.2009.10.025},
eprint = {NIHMS150003},
file = {:home/vegayon/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Kelejian, Prucha - 2010 - Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturba.pdf:pdf},
isbn = {0304-4076},
issn = {03044076},
journal = {Journal of Econometrics},
keywords = {Asymptotics,Cliff-Ord model,Generalized moments estimation,Heteroskedasticity,Spatial dependence,Two-stage least squares},
mendeley-groups = {Network probit},
number = {1},
pages = {53--67},
pmid = {20577573},
publisher = {Elsevier B.V.},
title = {{Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances}},
url = {http://dx.doi.org/10.1016/j.jeconom.2009.10.025},
volume = {157},
year = {2010}
}

@article{Piras2010,
abstract = {Urban ecology is a promising research field that could generate important information to be transferred into practical applications for urban landscape planning and management. However, the lack of homogeneity in technical terms used to describe urban-related sampling sites makes generalizations difficult to establish. After the substantial effort to standardize procedures for quantitatively determining major points along urban gradients using large scales ten years ago, recent studies have proposed novel definitions to define terms related to both habitat and landscape levels with the aim of describing specific study sites within urban systems. In this essay, I discuss the definition of several terms related to sites within urban systems (e.g., urban, suburban, peri-urban, non-urban, ex-urban, rural) and propose straightforward ways to standardize and accurately describe them. Undoubtedly, the use of well-defined terms in urban ecology studies will not only permit a better understanding of the nature of study sites across urban ecology studies and grant the possibility to perform robust comparisons among urban ecology studies, but could also aid policy makers and urban landscape planners and managers to enhance the ecological quality of urban systems around the globe.},
author = {Piras, Gianfranco},
doi = {10.1016/j.landurbplan.2011.01.013},
file = {:home/vegayon/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Piras - 2010 - sphet Spatial Models with Heteroskedastic Innovations in R.pdf:pdf},
isbn = {1548-7660},
issn = {0169-2046},
journal = {Journal of Statistical Software},
keywords = {computational methods,heteroskedasticity,kernel func-,r,semiparametric methods,spatial models,tions},
number = {4},
pages = {1--21},
title = {{sphet: Spatial Models with Heteroskedastic Innovations in R}},
url = {http://www.sciencedirect.com/science/article/pii/S0169204611000508{\%}5Cnhttp://pdn.sciencedirect.com/science?{\_}ob=MiamiImageURL{\&}{\_}cid=271853{\&}{\_}user=10{\&}{\_}pii=S0169204611000508{\&}{\_}check=y{\&}{\_}origin=article{\&}{\_}zone=toolbar{\&}{\_}coverDate=30-Apr-2011{\&}view=c{\&}originContentFamily},
volume = {35},
year = {2010}
}

@article{Milo2004,
abstract = {Complex biological, technological, and sociological networks can be of very different sizes and connectivities, making it difficult to compare their structures. Here we present an approach to systematically study similarity in the local structure of networks, based on the significance profile (SP) of small subgraphs in the network compared to randomized networks. We find several superfamilies of previously unrelated networks with very similar SPs. One superfamily, including transcription networks of microorganisms, represents "rate-limited" information-processing networks strongly constrained by the response time of their components. A distinct superfamily includes protein signaling, developmental genetic networks, and neuronal wiring. Additional superfamilies include power grids, protein-structure networks and geometric networks, World Wide Web links and social networks, and word-adjacency networks from different languages.},
author = {Milo, Ron and Itzkovitz, Shalev and Kashtan, Nadav and Levitt, Reuven and Shen-Orr, Shai and Ayzenshtat, Inbal and Sheffer, Michal and Alon, Uri},
doi = {10.1126/science.1089167},
file = {:home/vegayon/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Milo et al. - 2004 - Superfamilies of Evolved and Designed Networks.pdf:pdf},
isbn = {1095-9203 (Electronic)$\backslash$r0036-8075 (Linking)},
issn = {0036-8075},
journal = {Science},
month = {mar},
number = {5663},
pages = {1538--1542},
pmid = {15001784},
title = {{Superfamilies of Evolved and Designed Networks}},
url = {http://www.sciencemag.org/cgi/doi/10.1126/science.1089167},
volume = {303},
year = {2004}
}