Statistical models for social networks as dependent variables must represent the typical
network dependencies between tie variables such as reciprocity, homophily, transitivity, etc …

This chapter reviews software for the analysis of social networks. Both commercial and freely
available packages are considered. Based on the software page on the INSNA website …

" Exponential random graph models (ERGMs) are a class of statistical models for social
networks. They account for the presence (and absence) of network ties and so provide a …

Social Network Analysis and Education: Theory, Methods & Applications provides an
introduction to the theories, methods, and applications that constitute the social network …

This article provides an introductory summary to the formulation and application of
exponential random graph models for social networks. The possible ties among nodes of a …

The most promising class of statistical models for expressing structural properties of social
networks observed at one moment in time is the class of exponential random graph models …

Network models are widely used to represent relations between interacting units or actors.
Network data often exhibit transitivity, meaning that two actors that have ties to a third actor …

Dynamic networks are used in a variety of fields to represent the structure and evolution of
the relationships between entities. We present a model which embeds longitudinal network …

This article discusses the use of a symmetric multiplicative interaction effect to capture
certain types of third-order dependence patterns often present in social networks and other …

What do sociologists mean when they describe culture as founded on “shared
understandings”? Sharing an understanding does not necessarily imply having the same …