Specification of Exponential-Family Random Graph Models: Terms and Computational Aspects.

By Martina Morris, Mark S. Handcock, David R. Hunter. Journal of Statistical Software, 2008

404 citations recorded by the source corpus.

Exponential-family random graph models (ERGMs) represent the processes that govern the formation of links in networks through the terms selected by the user. The terms specify network statistics that are sufficient to represent the probability distribution over the space of networks of that size. Many classes of statistics can be used. In this article we describe the classes of statistics that are currently available in the ergm package. We also describe means for controlling the Markov chain Monte Carlo (MCMC) algorithm that the package uses for estimation. These controls a ect either the proposal distribution on the sample space used by the underlying Metropolis-Hastings algorithm or the constraints on the sample space itself. Finally, we describe various other arguments to core functions of the ergm package.

Subfields: Mathematical Physics, Statistical and Nonlinear Physics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/082b208013e987cbdd722b7351fb317c9c9b656d

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Citations in the corpus

statnet: Software Tools for the Representation, Visualization, Analysis and Simulation of Network Data. (2008, background): “Dyadic independent processes exhibit no direct dependence among dyads: An example is the related social concept that“birds of a feather ﬂock together”— if the two nodes in a dyad have similar attributes, the probability of a friendship tie is increased.”
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; ”
ergm: A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks. (2008, methodology): “Many other statnet terms eliminate (or can be made to eliminate) certain statistics in order to avoid linear dependencies; for examples, see all terms that use the base argument in the summary table of Appendix A in Morris et al. (2008).”
A Separable Model for Dynamic Networks (2010, background): “These principles mean that many of the vast array of network statistics developed for ERGMs (Morris et al., 2008, for example) can be readily adapted to STERGM modeling, retaining much of their interpretation.”
The Importance of Industry Links in Merger Waves (2012, neutral): “In particular, this section of the appendix borrows heavily from a number of prior papers, including Robins, Pattison, Kalish, and Lusher (2007), Robins and Morris (2007), Hunter, Handcock, Butts, Goodreau, and Morris (2008), Morris, Handcock, and Hunter (2008), and Johannes and Polson (2009).”
Understanding network formation in strategy research: Exponential random graph models (2016, background): “Mgmt. J., 37: 22–44 (2016) DOI: 10.1002/smj their models (Morris, Handcock, and Hunter, 2008, provide a discussion of ERGM terms related to this property).”
Culture, Cognition, and Collaborative Networks in Organizations (2011, background): “…well if it reproduces features of the network that were not used to construct it (for further information on the guidelines for fitting ERGMs, see Morris, Handcock, and Hunter 2008; O’Malley and Marsden 2008; Robins et al. 2007; Robins, Pattison, and Wang 2009; Snijders et al. 2006).13 (See Part…”
A statnet Tutorial. (2008, methodology): “A list of network statistics available for ERG models can be found in this issue (Morris, Handcock, and Hunter 2008); and for sna , in the package’s help ﬁles, via: R> help(package = "sna") One might also wish to compare degree distributions by sex: R> summary(fmh ~ degree(0:8, "Sex")) For more…”