Interest groups coordinate to achieve political goals. However, these groups are heterogeneous, and the division of labor within these coalitions varies. We explore the presence of distinct roles in coalitions of environmental interest groups, and analyse which factors predict if an organization takes on a particular role. To model these latent dynamics, we introduce the ego-ERGM. We find that ...

Models of dynamic networks - networks that evolve over time - have manifold applications. We develop a discrete-time generative model for social network evolution that inherits the richness and flexibility of the class of exponential-family random graph models. The model - a Separable Temporal ERGM (STERGM) - facilitates separable modeling of the tie duration distributions and the structural dy...

Gossip is informal talking about colleagues. Taking a social network perspective, we argue that group boundaries and social status in the informal workplace network determine who the objects of positive and negative gossip are. Gossip networks were collected among 36 employees in a public child care organization, and analyzed using exponential random graph modeling (ERGM). As hypothesized, both...

Interest groups struggle to build reputations as influential actors in the policy process and to discern the influence exercised by others. This study conceptualizes influence reputation as a relational variable that varies locally throughout a network. Drawing upon interviews with 168 interest group representatives in the United States health policy domain, this research examines the effects o...

The ergm package supports the statistical analysis and simulation of network data. It anchors statnet suite packages for in R introduced a special issue Journal Statistical Software 2008. This article provides an overview new functionality 2021 release version 4. These include more flexible handling nodal covariates, term operators that extend simplify model specification, models networks with ...

A complete survey of a network in a large population may be prohibitively difficult and costly. So it is important to estimate models for networks using data from various network sampling designs, such as link-tracing designs. We focus here on snowball sampling designs, designs in which the members of an initial sample of network members are asked to nominate their network partners, their netwo...

We study the U.S. Operations Research/Industrial-Systems Engineering (ORIE) faculty hiring network, consisting of 1,179 faculty origin and destination data together with attribute data from 83 ORIE departments. A social network analysis of faculty hires can reveal important patterns in an academic field, such as the existence of a hierarchy or sociological aspects such as the presence of commun...

This package vignette is designed as a hands-on tutorial for estimating temporal exponential random graph models (TERGMs) (Desmarais and Cranmer 2010, 2012b; Hanneke et al. 2010) and assessing goodness of fit and predictive performance (Cranmer and Desmarais 2011; Leifeld and Cranmer 2014) using the xergm package (Leifeld et al. 2014) for the statistical computing environment R (R Core Team 201...

Exponential-family random graph models (ERGMs) represent a powerful and flexible class of models for the statistical analysis of networks. statnet is a suite of software packages that implement these models. This paper details how the capabilities for ERGM modeling can be expanded and customized by programming additional network statistics that may be included in ERGMs. We describe a template R...