ergm: A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks.

By David R. Hunter, Mark S. Handcock, Carter T. Butts, Steven M. Goodreau, Martina Morris. Journal of Statistical Software, 2008

1,145 citations recorded by the source corpus.

We describe some of the capabilities of the ergm package and the statistical theory underlying it. This package contains tools for accomplishing three important, and interrelated, tasks involving exponential-family random graph models (ERGMs): estimation, simulation, and goodness of fit. More precisely, ergm has the capability of approximating a maximum likelihood estimator for an ERGM given a network data set; simulating new network data sets from a fitted ERGM using Markov chain Monte Carlo; and assessing how well a fitted ERGM does at capturing characteristics of a particular network data set.

Subfields: Economics and Econometrics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/56a44211b353f02b4f9dd31369edde8e3804b4d6

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Citations in the corpus

Comparing Brain Networks of Different Size and Connectivity Density Using Graph Theory (2010, methodology): “…iterative steps each with 10 5 networks that were randomly drawn from the distribution on the set of all networks; optimization was realized via a Monte Carlo Markov chain methods using the Metropolis-Hastings algorithm with 10 3 proposals, see the Statnet software package [68,69] for more details.”
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).”
Structural balance in the social networks of a wild mammal (2013, methodology): “In the ERGM models, gwesp and gwnsp are geometrically weighted terms, which were found to be effective at overcoming the degeneracy problems (Hunter et al. 2008).”
How Do They ‘End Up Together’? A Social Network Analysis of Self-Control, Homophily, and Adolescent Relationships (2011, methodology): “The ergm ( Handcock et al. 2008 ) package allows the user to specify terms in the model related to missing data (e.g., constraints on the number of nominations) and to simulate from the conditional distribution using MCMC.”
A statnet Tutorial. (2008, methodology): “As explained in Hunter et al. (2008b), such models are generally easier to ﬁt than others, and the ﬁtting process itself is not usually aﬀected if the model is inappropriate for the data.”
Specification of Exponential-Family Random Graph Models: Terms and Computational Aspects. (2008, methodology): “Examples, given in Hunter, Handcock, Butts, Goodreau, and Morris (2008b), include dyadic independent, dyadic dependent, and curved exponential-family (CEF) terms.”
A Survey of Statistical Network Models (2009, methodology): “, by full MCMC methods for mixed membership and exponential random graph models, or do we need to resort to approximations such as those involved in the variational approximation employed by [8; 9]? For ERGM models a newly updated suite of programs and documentation is now available [138; 157; 224; ”
Inferential Network Analysis with Exponential Random Graph Models (2011, methodology): “…in the distribution of some statistic Γ( · ) deﬁned on the network, an approximation to this distribution is easily obtainable by simulating a large number of networks based on the ERGM parameters and computing Γ i ( · ) for each network (Morris, Handcock and Hunter 2008; Handcock et al. 2008).”
Social Structure of Facebook Networks (2011, neutral): “In particular, given the dependence on system size inherent in this measure, one should not overinterpret the relative values of z-scores from different institutions.”
Birds of a feather, or friend of a friend? using exponential random graph models to investigate adolescent social networks* (2009, background): “Both are implemented in the statnet package for R (http://www.statnetproject.org), which introduces numerous features that make estimation fast and robust for the relatively large networks analyzed here (Handcock et al. 2008).”