A latent space model for cognitive social structures data.

• Model based fully Bayesian analysis of cognitive social structure data. • Bilinear structure to model transitivity effects within networks, and a hierarchical specification on the bilinear effects to borrow information across networks. • Formal evaluation of the agreement across actors' perception of their social roles. • Graphical representation in social space. This paper introduces a novel approach for modeling a set of directed, binary networks in the context of cognitive social structures (CSSs) data. We adopt a relativist approach in which no assumption is made about the existence of an underlying true network. More specifically, we rely on a generalized linear model that incorporates a bilinear structure to model transitivity effects within networks, and a hierarchical specification on the bilinear effects to borrow information across networks. This is a spatial model, in which the perception of each individual about the strength of the relationships can be explained by the perceived position of the actors (themselves and others) on a latent social space. A key goal of the model is to provide a mechanism to formally assess the agreement between each actors' perception of their own social roles with that of the rest of the group. Our experiments with both real and simulated data show that the capabilities of our model are comparable with or, even superior to, other models for CSS data reported in the literature. [ABSTRACT FROM AUTHOR]