Modeling social networks with homophily via multi-dimensional social distance attachment

The tendency of individuals to connect with similar others (i.e., homophily) is a cornerstone of social networks. Individuals are characterized by multiple attributes interacting in complex ways, challenging the unidimensional view on homophilic relationship formation. We introduce a multidimensional social distance attachment model integrating both exclusive (e.g., gender) and non-exclusive (e.g., language) features to capture homo- and hetero-philic behaviors. By embedding attributes as vectors within a metric space, we estimate the relative contribution of each attribute to the overall network features, and evaluate how their correlations shape inclusion dynamics. We test our model on two empirical networks of cultural production: the correspondence of the International Institute of Intellectual Cooperation (IIIC) and contemporary global music collaborations. In both cases, heterophily enhances the centrality of minorities, albeit the enhancement depends on the attributes' internal composition and interdependence. Our findings demonstrate that inclusion policies must account for the multidimensional nature of social identities, and can open alternative and non-trivial pathways toward more equitable visibility in social systems.