A bipartite graph, in which the nodes (or actors in a social network) are partitioned into two sets, can be studied using recent statistical models for dyadic interactions. These models, which are loglinear for the probabilities of dyadic choices or interactions, allow not only arcs or...

Kraemer and Jacklin (1979) proposed a method of analysis of univariate dyadic social interactions or relational data, and Mendoza and Graziano (1982) extended this method to multivariate relations. Their approach is based on an analysis-of- variance-type model that contains parameters...

Social interaction data record the intensity of the relationship, or frequency of interaction, between two individual actors. Recent methods for analysing such data have treated these relational variables as continuous. A more appropriate method, described here, views these dyadic interactions...

A new method is proposed for the statistical analysis of dyadic social interaction data measured over time. The data to be studied are assumed to be realizations of a social network of a fixed set of actors interacting on a single relation. The method is based on loglinear models for the...

Traditional network research analyzes relational ties within a single group of actors; the models presented in this paper involve relational ties that exist between two distinct sets of actors. Statistical models for traditional networks in which relations are measured within a group simplify...

This article presents a simulation study that compares several methods for deriving empirical subgroups from sociometric data. The Monte Carlo study was used to investigate how well the methods recovered the subgroup structure that had been built into the actors' and partners' modes. Forty eight...