A Latent Space Model for Cognitive Social Structures Data

TL;DR

This paper presents a new latent space model for analyzing cognitive social structures, capturing individual perceptions of social networks without assuming an underlying true network, and demonstrating superior performance on real and simulated data.

Contribution

It introduces a bilinear spatial model for CSS data that assesses perception agreement and borrows information across networks, advancing existing modeling approaches.

Findings

Model performs comparably or better than existing CSS models.

Effectively captures individual perceptions and social roles.

Demonstrates robustness on real and simulated datasets.

Abstract

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.