  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.
