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 ...

The exponential random graph model (ERGM) is an increasingly popular method for the statistical analysis of networks that can be used to flexibly analyze the processes by which policy actors organize into a network. Often times, interpretation of ERGM results is conducted at the network level, such that effects are related to overall frequencies of network structures (e.g., the number of closed...

The exponential random graph model (ERGM) is a well-established statistical approach to modelling social network data. However, Monte Carlo estimation of ERGM parameters is a computationally intensive procedure that imposes severe limits on the size of full networks that can be fitted. We demonstrate the use of snowball sampling and conditional estimation to estimate ERGM parameters for large n...

In this thesis we investigate the international arms trade network of major conventional weapons (MCW) between 1950 and 2013. After an introduction to the network theory and some descriptive analysis of the data we will model the arms trade network with the popular and well-known exponential random graph model (ERGM). However, we find that in order to guarantee a good model fit, the ERGM has to...

Methods for descriptive network analysis have reached statistical maturity and general acceptance across the social sciences in recent years. However, methods for statistical inference with network data remain fledgling by comparison. We introduce and evaluate a general model for inference with network data, the Exponential Random Graph Model (ERGM) and several of its recent extensions. The ERG...

Exponential-family random graph models (ERGMs) are probabilistic network models that are parametrized by sufficient statistics based on structural (i.e., graph-theoretic) properties. The ergm package for the R statistical computing system is a collection of tools for the analysis of network data within an ERGM framework. Many different network properties can be employed as sufficient statistics...

statnet is a suite of software packages for statistical network analysis. The packages implement recent advances in network modeling based on exponential-family random graph models (ERGM). The components of the package provide a comprehensive framework for ERGM-based network modeling, including tools for model estimation, model evaluation, model-based network simulation, and network visualizati...

A comprehensive understanding of the relational processes that constitute policy networks requires both an analysis of exogenous (i.e. covariates) determinants of the link structures in the network and a study of how the relationships that form the network depend upon each other, the endogenous determinants of the network. The Exponential Random Graph Model (ERGM) is an increasingly popular met...

The statistical modeling of social network data needs much effort  because of the complex dependence structure of the tie variables. In order to formulate such dependences, the statistical exponential families of distributions can provide a flexible structure. In this regard, the statistical characteristics of the network is provided to be encapsulated within an Exponential Random Graph Model (...

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 ...