Journal of Statistical Software · 2008 · 1.3K citations · 35 references
BusinessBiostatisticsComputer NetworksKnowledge NetworksStochastic ModelingGraph TheoryNetwork AnalysisNetwork InferenceNetwork ModelingNetwork StructureStochastic NetworksExponential Family DistributionsGraph Theory In NetworksGraph-theoretic Network AnalysisMarkov Chain Monte Carlo InferenceMaximum Likelihood EstimatorRandom Graph ModelsRandom Graph TheoryStochastic Network Models
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.
35
Statistical mechanics of complex networks
Réka Albert, Albert-Ĺaszló Barabási · Reviews of Modern Physics · 2002 · 20.6K citations · Full text
New Specifications for Exponential Random Graph Models
Tom A. B. Snijders, Philippa Pattison, Garry Robins et al. · Sociological Methodology · 2006 · 1.5K citations