The presence of unobserved node-specific heterogeneity in exponential random graph models (ERGM) is a general concern, both with respect to model validity as well as estimation instability. We, therefore, include node-specific random effects in the ERGM that account for unobserved heterogeneity in the network. This leads to a mixed model with parametric as well as random coefficients, labelled as mixed ERGM. Estimation is carried out by iterating between approximate pseudolikelihood estimation for the random effects and maximum likelihood estimation for the remaining parameters in the model. This approach provides a stable algorithm, which allows to fit nodal heterogeneity effects even for large scale networks. We also propose model selection based on the Akaike Information Criterion to check for node-specific heterogeneity.

This study investigates how venture capital firms (VCs) choose syndication partners. Exponential random graph models of Chinese VC syndication networks from 2006 to 2013 show that the homophily mechanism does not always determine VCs’ partner selection. In selecting partners, VCs have to strike a balance between reducing uncertainty and mobilizing heterogeneous resources. Therefore, decisions about partners depend on institutional uncertainty and VCs’ investment preferences. While VCs that focus on traditional business in an immature market are more likely to form homogeneous syndications, their peers that prefer to invest in innovative companies and that can rely on a stable market tend to syndicate with heterogeneous partners.

We explore mediated representations of parties' campaign interactions in multi-party systems. Actors in multi-party systems can engage with different actors on multiple issues. One crucial aspect of such engagement is the element of negativity—voicing criticisms of other actors' actions and policy proposals. This contribution argues that the media systematically exaggerate patterns of negativity based on issue ownership structures, such that attacks originating from or targeting issue owners are significantly more likely to be covered. We analyze a broad sample of news content from the 2013 Austrian national election campaign with generalized exponential random graph models to capture the complexities of mediated campaign negativity in a multi-party system while controlling for non-mediated campaign negativity. The results show that issue owners are more likely to be featured as attackers and targets in owned policy domains, suggesting a violation of the normative ideal of a fair representation of campaign interactions.

In policymaking, actors are likely to take the preferences of others into account when strategically positioning themselves. However, there is a lack of research that conceives of policy preferences as an interdependent system. In order to analyse interdependencies, we link actors to their policy preferences in water protection, which results in an actor-instrument network. As actors exhibit multiple preferences, a complex two-mode network between actors and policies emerges. We analyse whether actors exhibit interdependent preference profiles given shared policy objectives or social interactions among them. By fitting an exponential random graph model to the actor-instrument network, we find considerable clustering, meaning that actors tend to exhibit preferences for multiple policy instruments in common. Actors tend to exhibit interdependent policy preferences when they are interconnected, that is, they collaborate with each other. By contrast, actors are less likely to share policy preferences when a conflict line divides them.

Monitoring the implementation process in domestic settings of multi-level policies like the EU gender directives is dependent on interactions among a diverse set of policy stakeholders. However, there is no clear understanding of which factors determine the structure of these monitoring networks and what benefits effective exchange. Drawing on insights from social network theory, literature on information politics in transnational networks and policy network analysis, this study analyses what drives information exchange among actors in the monitoring network of women’s groups in the Netherlands. Using Exponential Random Graph Models, the analysis reveals distinctive structural features facilitating efficient information exchange and significant brokerage in the monitoring network. Moreover, the results demonstrate that interactions that serve the purpose of monitoring EU policy implementation occur among a diverse set of actors in a multi-level structure. These findings indicate that effective monitoring requires resourceful and broad-based information exchange to increase transparency in the implementation process.

We compare and contrast the network formation of interest groups across industry and issue area. We focus on membership interest groups, which by virtue of representing the interests of voluntary members face particular organizational and maintenance constraints. To reveal their cooperative behavior we build a network dataset based on cosigner status to United States Supreme Court amicus curiae briefs and analyze it with exponential random graph models and multidimensional scaling. Our methodological approach culminates in a clear and compact spatial representation of network similarities and differences. We find that while many of the same factors shape membership networks, religious, labor, and political organizations do not share the same structure as each other or as the business, civic and professional groups.