Goodness of Fit of Social Network Models

By David R. Hunter, Steven M. Goodreau, Mark S. Handcock. 2008

735 citations recorded by the source corpus.

Subfields: Experimental and Cognitive Psychology, Health, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/cef5615393527741596448d4d5eb828a8e32363a

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In Experimental and Cognitive Psychology

In Health

In Statistical and Nonlinear Physics

Citations in the corpus

Exponential random graph models for multilevel networks (2013, methodology): “The MCMC simulation also serves as a tool for PRESS ks xxx (2013) xxx– xxx 3 model goodness of fit (GOF) test, where simulated graph distributions are compared with the observed networks (Snijders, 2002; Hunter et al., 2008).”
A Survey of Statistical Network Models (2009, neutral): “[156], or not at all.”
Advances in exponential random graph (p*) models applied to a large social network (2007, methodology): “This approach is described in detail in Hunter et al. (2005) .”
Birds of a feather, or friend of a friend? using exponential random graph models to investigate adolescent social networks* (2009, methodology): “This approach has a clear interpretation and has been shown to work well in practice in overcoming model degeneracy and producing models that fi t a wide range of data well, including the data considered here (Goodreau 2007; Hunter et al. 2008).”
Curved exponential family models for social networks (2007, background): “their ability to fit social network data well. For example, both Hunter et al (2005)”
Information Exchange in Policy Networks (2012, background): “To support this claim, figure 1 plots three well-known network statistics of the original network (the thick black line) and the 100 simulated networks (the box plots): indegree, minimum geodesic distance, and edge-wise shared partners (for details, see Hunter, Goodreau, and Handcock 2008).”
Understanding network formation in strategy research: Exponential random graph models (2016, methodology): “We estimate our models using MCMC-MLE in the ERGM package, a part of the statnet suite of packages for R (Hunter et al., 2008b).”
Mapping Change in Large Networks (2008, background): “For example, statistical network models and stratified data make it possible to estimate global properties from the observation of sample networks [7–9].”
The analysis of social networks (2008, methodology): “See Hunter et al. (2008) for a detailed discussion of methods for assessing model fit.”
Discrete Temporal Models of Social Networks (2006, methodology): “We believe our temporal ERG models represent a useful new framework for modeling time-evolving social networks, and rewiring networks from other domains such as gene regulation circuitry, and communication networks.”
Formation and dissolution of inter-firm linkages in lengthy and stable networks in clusters (2015, methodology): “Nevertheless, this study relegates both AIC and BIC measures (Hunter et al., 2008).”
Local Private Hypothesis Testing: Chi-Square Tests (2017, background): “Hypothesis testing was initially developed for scientific and survey data, but today it is also an essential tool to test models over collections of social network, mobile, and crowdsourced data (American Statistical Association, 2014; Hunter et al., 2008; Steele et al., 2017).”
Universities and open innovation: the determinants of network centrality (2019, background): “Nevertheless, we acknowledge the edge and boundary effects this imposes on the analysis, as this is the case with any social network analysis whereby fixed boundaries are required to be enforced to aid meaningful study (Hunter et al. 2008).”
Closure, connectivity and degree distributions: Exponential random graph (p*) models for directed social networks (2009, background): “…also ignored cyclic processes, whereby losure may occur through the formation of directed three-cycles. ycles have often been interpreted as representing a form of genralized exchange (e.g., Bearman, 1997; Lazega and Pattison, 1999), nd so represent an important theme in social network theory.”
How Do They ‘End Up Together’? A Social Network Analysis of Self-Control, Homophily, and Adolescent Relationships (2011, background): “However, Markov chain Monte Carlo (MCMC) provides a means of approximating the likelihood (Snijders 2002) with the important implication that a sample of networks can be simulated from the algorithm providing a means of examining model fit (Hunter et al. 2008).”