The hidden architecture of connections: How do multidimensional identities shape our social networks?

TL;DR

This paper models how multidimensional identities influence social network formation, revealing that simple latent preference models best explain observed connection patterns and identifying key identity aspects in social interactions.

Contribution

It introduces a network model for multidimensional social interactions, compares various preference structures, and demonstrates the effectiveness of simple models using empirical data.

Findings

Simple latent preference models outperform complex ones.

The calibrated model quantifies latent connection preferences.

Identifies which identity aspects are most relevant for social connections.

Abstract

Our multidimensional identities determine how we interact with each other, shaping social networks through group-based connection preferences. While interactions along single dimensions have been extensively studied, the dynamics driving multidimensional connection preferences remain largely unexplored. In this work, we develop a network model of multidimensional social interactions to tackle two crucial questions: What is the structure of our latent connection preferences, and how do we integrate information from our multidimensional identities to connect with others? To answer these questions, we systematically model different latent preference structures and preference aggregation mechanisms. Then, we compare them using Bayesian model selection by fitting empirical data from high school friendship networks. We find that a simple latent preference model consistently outperforms more complex alternatives. The calibrated model provides robust measures of latent connection preferences in real-world networks, bringing insights into how one- and multidimensional groups interact. Finally, we develop natural operationalizations of dimension salience, revealing which aspects of identity are most relevant for individuals when forming connections.