Our identities are composed of multiple categories—such as ethnicity, gender, and socioeconomic status—that shape our social networks through group-based connection preferences.

As most research focuses on one-dimensional interactions, a fundamental question remains: How do individuals integrate multidimensional identity information when forming social ties?

Addressing this question is crucial for understanding social dynamics, mitigating segregation, and designing integration-promoting interventions.

Here, we develop a principled theoretical framework to model multidimensional social interactions. Using Bayesian model selection, we compare competing preference aggregation mechanisms in two empirical systems (high-school friendship networks and marriages) and find that a simple latent preference model outperforms more complex alternatives: people evaluate each identity dimension independently and form ties mainly when all evaluations are favorable.

The model yields interpretable preference estimates and principled measures of dimension salience, providing a practical tool for analyzing social choices and identifying which aspects of identity matter most for tie formation.

Our work reveals how social structures emerge from intersecting identities, with broad implications for understanding social cohesion and addressing intersectional inequalities in networks.

S. Martin-Gutierrez, M.N. Cartier van Dissel, F. Karimi, A simple preference aggregation rule explains how multidimensional identities shape social networks, Communication Physics (2026).