Exponential random graph models, or ERGMs, are a flexible and general class of models for
modeling dependent data. While the early literature has shown them to be powerful in …

Exponential random graph models, or ERGMs, are a flexible and general class of models for
modeling dependent data. While the early literature has shown them to be powerful in …

Exponential-family Random Graph Models (ERGMs) constitute a large statistical framework
for modeling dense and sparse random graphs with short-or long-tailed degree distributions …

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 …

Abstract Exponential-family Random Graph Models (ERGMs) have long been at the forefront
of the analysis of relational data. The exponential-family form allows complex network …

Exponential-family random graph models (ERGMs) are probabilistic network models that are
parametrized by sufficient statistics based on structural (ie, graph-theoretic) properties. The …

In most domains of network analysis researchers consider networks that arise in nature with
weighted edges. Such networks are routinely dichotomized in the interest of using available …

Exponential random graph models (ERGMs) are one of the most popular statistical methods
for analysing relational network structures. ERGMs represent generative statistical network …

The uncertainty underlying real-world phenomena has attracted attention toward statistical
analysis approaches. In this regard, many problems can be modeled as networks. Thus, the …

Graphs are the primary mathematical representation for networks, with nodes or vertices
corresponding to units (eg, individuals) and edges corresponding to relationships …