Intersectional Homophily: A New Measure of Multi-Dimensional Homophily in Social Network

Abstract

Homophily in social life has been an increasingly important focus of social network research. However, most studies only measure homophily one dimension at a time, reducing an individual's identity to only one attribute. In reality, individuals belong to multiple social groups simultaneously. Knowing that homophily in one dimension usually spills over into homophily in a correlated dimension, recent scholarly works call for explicitly examining multiple dimensions simultaneously and also refining homophily measures to better match the theoretical intent. I use intersectionality as the theoretical foundation to bring socially constructed meaning among multiple social identities into homophily literature. I use a random data generator process to develop and test a new homophily measure that can simultaneously measure homophily on multiple dimensions such as race, gender, and age. The findings in this simulated setting provide evidence that intersectionality affects homophily because of intersecting identities; multiple identities interact in a complex way rather as opposed to being simple additives. In addition, I examine my newly developed measure on actual network datasets and the findings are consistent with those of the simulated scenarios. I conclude with a discussion of the implications of this measure and how this measure contributes to the deepening of our understanding of social interactions and the processes that create sociodemographic structures.