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@article{Stadtfeld2017,
author = {Stadtfeld, Christoph and Block, Per},
doi = {10.15195/v4.a14},
file = {:home/george/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Stadtfeld, Block - 2017 - Interactions , Actors , and Time Dynamic Network Actor Models for Relational Events.pdf:pdf},
issn = {23306696},
journal = {Sociological Science},
keywords = {are at the,conversations,core of major social,dynam,empirical research to date,however,interactions,mostly focuses on the,network dynamics,network theories,relational event model,relations,social interactions,social networks,study of durable interpersonal,such as meetings and,such as perceived},
pages = {318--352},
title = {{Interactions, Actors, and Time: Dynamic Network Actor Models for Relational Events}},
url = {https://www.sociologicalscience.com/articles-v4-14-318/},
volume = {4},
year = {2017}
}
@article{Butts2008,
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.},
author = {Butts, Carter T.},
doi = {10.1111/j.1467-9531.2008.00203.x},
file = {:home/george/Dropbox/papers/b/Butts (Sociological Methodology, 2008).pdf:pdf},
issn = {0081-1750},
journal = {Sociological Methodology},
month = {aug},
number = {1},
pages = {155--200},
title = {{A Relational Event Framework for Social Action}},
url = {http://journals.sagepub.com/doi/10.1111/j.1467-9531.2008.00203.x},
volume = {38},
year = {2008}
}
@article{Snijders2010intro,
author = {Snijders, Tom A B and van de Bunt, Gerhard G. and Steglich, Christian E G},
doi = {10.1016/j.socnet.2009.02.004},
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{Robins2007,
author = {Robins, Garry and Pattison, Pip and Kalish, Yuval and Lusher, Dean},
doi = {10.1016/j.socnet.2006.08.002},
journal = {Social Networks},
keywords = {Exponential random graph models,Statistical models for social networks,p* models},
number = {2},
pages = {173--191},
pmid = {18449326},
title = {{An introduction to exponential random graph (p*) models for social networks}},
volume = {29},
year = {2007}
}
@article{Holland1981,
author = {Holland, Paul W. and Leinhardt, Samuel},
doi = {10.2307/2287037},
journal = {Journal of the American Statistical Association},
keywords = {generalized iterative scaling,networks,random digraphs,sociome-,try},
number = {373},
pages = {33--50},
title = {{An exponential family of probability distributions for directed graphs}},
volume = {76},
year = {1981}
}
@article{Frank1986,
author = {Frank, O and Strauss, David},
doi = {10.2307/2289017},
journal = {Journal of the American Statistical Association},
keywords = {log-linear network model,markov field},
mendeley-groups = {network dependence,ergms},
number = {395},
pages = {832--842},
pmid = {7439394},
title = {{Markov graphs}},
url = {http://amstat.tandfonline.com/doi/abs/10.1080/01621459.1986.10478342},
volume = {81},
year = {1986}
}
@article{Wasserman1996,
author = {Wasserman, Stanley and Pattison, Philippa},
doi = {10.1007/BF02294547},
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 and p*}},
volume = {61},
year = {1996}
}
@article{Snijders2006,
author = {Snijders, Tom A B and Pattison, Philippa E and Robins, Garry L and Handcock, Mark S},
doi = {10.1111/j.1467-9531.2006.00176.x},
issn = {0081-1750},
journal = {Sociological Methodology},
month = {12},
number = {1},
pages = {99--153},
title = {{New specifications for exponential random graph models}},
url = {http://www.jstor.org/stable/25046693 http://smx.sagepub.com/lookup/doi/10.1111/j.1467-9531.2006.00176.x},
volume = {36},
year = {2006}
}
@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},
file = {:home/george/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/LeSage - 2008 - An Introduction to Spatial Econometrics.pdf:pdf},
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}
}
@incollection{Elhorst2014,
address = {Berlin, Heidelberg},
author = {Elhorst, J Paul},
booktitle = {Spatial Econometrics: From Cross-Sectional Data to Spatial Panels},
doi = {10.1007/978-3-642-40340-8_3},
file = {:home/george/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Elhorst - 2014 - Spatial Panel Data Models.pdf:pdf},
isbn = {978-3-642-40340-8},
pages = {37--93},
publisher = {Springer Berlin Heidelberg},
title = {{Spatial Panel Data Models}},
url = {http://dx.doi.org/10.1007/978-3-642-40340-8{\_}3 http://link.springer.com/10.1007/978-3-642-40340-8{\_}3},
year = {2014}
}
@book{fischer2013handbook,
title={Handbook of Regional Science},
author={Fischer, M.M. and Nijkamp, P.},
isbn={9783642234293},
series={Handbook of Regional Science},
url={https://books.google.com/books?id=iwhDtQEACAAJ},
year={2013},
publisher={Springer Berlin Heidelberg}
}
@article{Tisue2004,
abstract = {NetLogo [Wilensky, 1999] is a multi-agent programming language and modeling environment for simulating complex phenomena. It is designed for both research and education and is used across a wide range of disci- plines and education levels. In this paper we focus on NetLogo as a tool for research and for teaching at the undergraduate level and higher. We outline the principles behind our design and describe recent and planned enhancements.},
author = {Tisue, Seth and Wilensky, Uri},
file = {:home/george/Dropbox/papers/t/Tisue et al (netlogo iccs, 2004).pdf:pdf},
isbn = {0769520723},
issn = {10639667},
journal = {Conference on Complex Systems},
pages = {1--10},
title = {{Netlogo: A simple environment for modeling complexity}},
url = {http://ccl.sesp.northwestern.edu/papers/netlogo-iccs2004.pdf},
year = {2004}
}
@article{Lazer2020,
author = {Lazer, David M.J. and Pentland, Alex and Watts, Duncan J. and Aral, Sinan and Athey, Susan and Contractor, Noshir and Freelon, Deen and Gonzalez-Bailon, Sandra and King, Gary and Margetts, Helen and Nelson, Alondra and Salganik, Matthew J. and Strohmaier, Markus and Vespignani, Alessandro and Wagner, Claudia},
doi = {10.1126/science.aaz8170},
file = {:home/george/Dropbox/papers/l/Lazer et al (Computational social science- Obstacles and opportunities).pdf:pdf},
issn = {10959203},
journal = {Science},
number = {6507},
pages = {1060--1062},
pmid = {32855329},
title = {{Computational social science: Obstacles and opportunities}},
volume = {369},
year = {2020}
}
@article{Hofman2021,
abstract = {Computational social science is more than just large repositories of digital data and the computational methods needed to construct and analyse them. It also represents a convergence of different fields with different ways of thinking about and doing science. The goal of this Perspective is to provide some clarity around how these approaches differ from one another and to propose how they might be productively integrated. Towards this end we make two contributions. The first is a schema for thinking about research activities along two dimensions—the extent to which work is explanatory, focusing on identifying and estimating causal effects, and the degree of consideration given to testing predictions of outcomes—and how these two priorities can complement, rather than compete with, one another. Our second contribution is to advocate that computational social scientists devote more attention to combining prediction and explanation, which we call integrative modelling, and to outline some practical suggestions for realizing this goal.},
author = {Hofman, Jake M. and Watts, Duncan J. and Athey, Susan and Garip, Filiz and Griffiths, Thomas L. and Kleinberg, Jon and Margetts, Helen and Mullainathan, Sendhil and Salganik, Matthew J. and Vazire, Simine and Vespignani, Alessandro and Yarkoni, Tal},
doi = {10.1038/s41586-021-03659-0},
file = {:home/george/Dropbox/papers/h/Hofman et al (Nature, 2021).pdf:pdf},
issn = {14764687},
journal = {Nature},
number = {7866},
pages = {181--188},
pmid = {34194044},
publisher = {Springer US},
title = {{Integrating explanation and prediction in computational social science}},
url = {http://dx.doi.org/10.1038/s41586-021-03659-0},
volume = {595},
year = {2021}
}
@article{Snijders1999,
abstract = {Two procedures are proposed for calculating standard errors for network statistics. Both are based on resampling of vertices: the first follows the bootstrap approach, the second the jackknife approach. In addition, we demonstrate how to use these estimated standard errors to compare statistics using an approximate t-test and how statistics can also be compared by another bootstrap approach that is not based on approximate normality.},
author = {Snijders, Tom A B and Borgatti, Stephen P},
file = {:home/george/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Snijders, Borgatti - 1999 - Non-Parametric Standard Errors and Tests for Network Statistics.pdf:pdf;:home/george/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Snijders, Borgatti - 1999 - Non-Parametric Standard Errors and Tests for Network Statistics(2).pdf:pdf},
journal = {Connections},
keywords = {Non-parametric},
mendeley-tags = {Non-parametric},
number = {2},
pages = {1--10},
title = {{Non-Parametric Standard Errors and Tests for Network Statistics}},
url = {https://insna.org/PDF/Connections/v22/1999{\_}I-2{\_}61-70.pdf},
volume = {22},
year = {1999}
}
@article{Butts2008b,
author = {Butts, Carter T.},
doi = {10.1111/j.1467-839X.2007.00241.x},
file = {:home/george/Dropbox/papers/b/Butts (Asian Journal of Social Psychology, 2008).pdf:pdf},
issn = {1367-2223},
journal = {Asian Journal Of Social Psychology},
month = {mar},
number = {1},
pages = {13--41},
title = {{Social network analysis: A methodological introduction}},
url = {http://doi.wiley.com/10.1111/j.1467-839X.2007.00241.x},
volume = {11},
year = {2008}
}
@article{Krackhardt1988,
abstract = {This paper argues that the quadratic assignment procedure (QAP) is superior to OLS for testing hypothesis in both simple and multiple regression models based on dyadic data, such as found in network analysis. A model of autocorrelation is proposed that is consistent with the assumptions of dyadic data. Results of Monte Carlo simulations indicate that OLS analysis is statistically biased, with the degree of bias varying as a function of the amount of structural autocorrelation. On the other hand, the simulations demonstrate that QAP is relatively unbiased. The Sampson data are used to illustrate the QAP multiple regression procedure and a general method of testing whether the results are statistically biased.},
author = {Krackhardt, David},
doi = {10.1016/0378-8733(88)90004-4},
file = {:home/george/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Krackhardt - 1988 - Predicting with networks Nonparametric multiple regression analysis of dyadic data.pdf:pdf},
issn = {03788733},
journal = {Social Networks},
month = {dec},
number = {4},
pages = {359--381},
title = {{Predicting with networks: Nonparametric multiple regression analysis of dyadic data}},
url = {http://linkinghub.elsevier.com/retrieve/pii/0378873388900044 http://www.sciencedirect.com/science/article/pii/0378873388900044},
volume = {10},
year = {1988}
}
@article{Moran1950,
author = {Moran, P. A. P.},
doi = {10.2307/2332142},
file = {:home/george/.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{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},
file = {:home/george/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/LeSage - 2008 - An Introduction to Spatial Econometrics.pdf:pdf},
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{Valente2019,
abstract = {This paper examines whether country implementation of a public health treaty is influenced by the implementation behaviors of other countries to which they have network ties. We examine implementation of the Framework Convention on Tobacco Control (FCTC) adopted by the World Health Organization in 2003 and ratified by approximately 94{\%} of countries as of 2016. We constructed five networks: (1) geographic distance, (2) general trade, (3) tobacco trade, (4) GLOBALink referrals, and (5) GLOBALink co-subscriptions. Network exposure terms were constructed from these networks based on the implementation scores for six articles of the FCTC treaty. We estimate effects using a lagged Type 1 Tobit model. Results show that network effects were significant: (a) across all networks for article 6 (pricing and taxation), (b) distance, general trade, GL referrals, and GL co-subscriptions for article 8 (second hand smoke), (c) distance, general trade, and GL co-subscriptions for article 11 (packaging and labeling), and (d) distance and GL co-subscription for article 13 (promotion and advertising), (e) tobacco trade and GL co-subscriptions for article 14 (cessation). These results indicate that diffusion effects were more prevalent for pricing and taxation as well as restrictions on smoking in public places and packaging and labeling. These results suggest that network influences are possible in domains that are amenable to control by national governments but unlikely to occur in domains established by existing regulatory systems. Implications for future studies of policy implementation are discussed.},
author = {Valente, Thomas W. and Wipfli, Heather and {Vega Yon}, George G.},
doi = {10.1016/j.socscimed.2019.01.008},
issn = {18735347},
journal = {Social Science and Medicine},
keywords = {Diffusion of innovations,Policy implementation,Social network analysis,Tobacco control},
title = {{Network influences on policy implementation: Evidence from a global health treaty}},
year = {2019}
}
@article{VegaYon2021,
abstract = {Statistical models for social networks have enabled researchers to study complex social phenomena that give rise to observed patterns of relationships among social actors and to gain a rich understanding of the interdependent nature of social ties and actors. Much of this research has focused on social networks within medium to large social groups. To date, these advances in statistical models for social networks, and in particular, of Exponential-Family Random Graph Models (ERGMS), have rarely been applied to the study of small networks, despite small network data in teams, families, and personal networks being common in many fields. In this paper, we revisit the estimation of ERGMs for small networks and propose using exhaustive enumeration when possible. We developed an R package that implements the estimation of pooled ERGMs for small networks using Maximum Likelihood Estimation (MLE), called “ergmito”. Based on the results of an extensive simulation study to assess the properties of the MLE estimator, we conclude that there are several benefits of direct MLE estimation compared to approximate methods and that this creates opportunities for valuable methodological innovations that can be applied to modeling social networks with ERGMs.},
archivePrefix = {arXiv},
arxivId = {1904.10406},
author = {{Vega Yon}, George G. and Slaughter, Andrew and de la Haye, Kayla},
doi = {10.1016/j.socnet.2020.07.005},
eprint = {1904.10406},
file = {:home/george/Dropbox/papers/v/Vega Yon, Slaughter, de la Haye (Social Networks 2020).pdf:pdf},
issn = {03788733},
journal = {Social Networks},
keywords = {Exact statistics,Exponential random graph models,Simulation study,Small networks,Teams},
number = {August 2020},
pages = {225--238},
publisher = {Elsevier},
title = {{Exponential random graph models for little networks}},
url = {https://doi.org/10.1016/j.socnet.2020.07.005},
volume = {64},
year = {2021}
}
@article{Caimo2014,
abstract = {In this paper we describe the main features of the Bergm package for the open-source R software which provides a comprehensive framework for Bayesian analysis of exponential random graph models: tools for parameter estimation, model selection and goodness-of- fit diagnostics. We illustrate the capabilities of this package describing the algorithms through a tutorial analysis of three network datasets.},
archivePrefix = {arXiv},
arxivId = {1201.2770},
author = {Caimo, Alberto and Friel, Nial},
doi = {10.18637/jss.v061.i02},
eprint = {1201.2770},
file = {:home/george/Dropbox/papers/c/Caimo and Friel (JSS 2014).pdf:pdf},
issn = {15487660},
journal = {Journal of Statistical Software},
keywords = {Bayesian inference,Bayesian model selection,Exponential random graph models,Markov chain Monte Carlo},
number = {2},
title = {{Bergm: Bayesian exponential random graphs in R}},
volume = {61},
year = {2014}
}