IDENTIFYING COHESIVE SUBGROUPS

Cohesive subgroups have always represented an important construct for sociologists who study individuals and organizations. In this article, I apply recent advances in the statistical modelling of social network data to the task of identifying cohesive subgroups from social network data. Further, through simulated data, I describe a process for obtaining the probability that a given sample of data could have been obtained from a network in which actors were no more likely to engage in interaction with subgroup members than with members of other subgroups. I obtain the probability for a specific data set, and then, through further simulations, develop a model which can be applied to future data sets. Also through simulated data, I characterize the extent to which a simple hill-climbing algorithm recovers known subgroup memberships. I apply the algorithm to data indicating the extent of professional discussion among teachers in a high school, and I show the relationship between membership in cohesive subgroups and teachers' orientations towards teaching.