Predicting Homophily and Social Network Connectivity From Dyadic Behavioral Similarity Trajectory Clusters


dyad similarity, dyad evolution, network time series, activity sensors, Fitbit


The similarity between pairs of people is often measured on relatively static traits and at a given point in time. Moving beyond this approach, a burgeoning line of research is investigating temporal dyadic similarity on traits and behaviors, such as health activities. Our study contributes to this line of inquiry by using fine-grained longitudinal data obtained from sensors, mobile devices, and surveys to examine whether we can observe distinct types of dyadic similarity trajectories based on physical activity, and if so, what dyad-level properties predict membership in each trajectory class. Treating daily differences in the steps for dyads as time series, we use k-shape clustering to identify classes of similarity trajectories and logistic regression to examine the link between trajectory class and key dyad-level factors. We identify 21 dyadic trajectory clusters and find that trajectory membership predicts dyadic connectivity in the communication network, as well as racial and religious—but not gender-based—similarity. We conclude by noting how research on dyadic similarity trajectories can be further integrated with ongoing work in social network analysis.

Original Publication Citation

Brandon Sepulvado, Michael Wood, Ethan Fridmanski, Cheng Wang, Matthew J. Chandler, Omar Lizardo, David Hachen. 2020. "Predicting Homophily and Social Network Connectivity From Dyadic Behavioral Similarity Trajectory Clusters." Social Science Computer Review

Document Type

Peer-Reviewed Article

Publication Date


Permanent URL


Social Science Computer Review




Family, Home, and Social Sciences



University Standing at Time of Publication

Assistant Professor