Worth the Weight: Conceptualizing and Measuring Homophily in Weighted Social Networks
NetSI Speaker Series
Past Talk
Cassie McMillan
Assistant Professor
Feb 25, 2021
Watch video
12:00 pm
177 Huntington Ave
Join Talk (Zoom)
11th floor

Homophily, or the tendency for social contact to occur among those who are similar, plays a crucial role in structuring our social networks. However, empirical research almost always assumes that homophily is an unvarying process that operates similarly for all social ties, regardless of their level of intimacy or frequency of interaction. As data on weighted networks, or networks where ties are assigned quantitative measures of strength, become increasingly available, network researchers need to consider whether homophily processes operate differently for ties with varying weights. Here, I take this approach by first defining two variants of homophily that can arise in weighted networks: (1) strong tie homophily, or the tendency for ties with high values to cluster together similar peers, and (2) weak tie homophily, or the tendency for ties with low edge weights to connect same-attribute actors. Then, I apply valued ERGMs to demonstrate the utility of differentiating between the two variants across simulated and observed networks. In most networks, I find that there are observable differences in the magnitude of strong versus weak tie homophily. Additionally, when there are low levels of clustering on the attribute of interest, distinguishing between strong and weak tie homophily can reveal that these processes operate in opposite directions. Since strong and weak ties carry substantively different implications, I argue that differentiating between the two homophily variants has the potential to uncover novel insights on a variety of social processes.

About the speaker
About the speaker

Cassie McMillan is an assistant professor of Sociology and Criminology & Criminal Justice at Northeastern University.  She received her PhD in Sociology and Demography with a minor in Social Data Analytics from Pennsylvania State University. Her research applies a social networks perspective to disentangle how our connections both reproduce and challenge systems of social inequality. Her work is focussed on developing computational methodologies that can better address these issues and applying these methods to study social phenomena such as immigration, health, delinquency, and bullying.