A temporal proximity network dataset from a wedding cocktail hour with relationship category labels - Joshua Z. Stadlan; Richard B. Kahn; Michelle Birkett (Discover Networks, 2026)
Objectives We captured a fine-grained dataset of unstructured social interaction with socially meaningful group labels to fill a gap in the study of face-to-face interaction. Prior interaction data from conferences, classrooms, hospitals, and workplaces exhibit network signatures such as heterogeneous contact rates, clustering, and bursty dynamics. However, schedules, room assignments, and authority roles in these settings may obscure unstructured social group dynamics. Studies on group mixing often rely on demographic proxies like gender, or assigned categories like school classes, rather than relationship-based groups. We aim to understand if temporal network signatures of institutionally structured settings generalize to unstructured social interaction. Data description We present the first public temporal proximity network dataset of a privately hosted social event with contextual relationship-based group membership. At the outdoor cocktail hour of a wedding, 95 participants wore proximity sensor badges that detected other badge-wearers within approximately 1.5 m in 5 s intervals. This dataset, coarsened to 10 s temporal bins, contains 7,213 contact events over 2,760 observed dyads. Participants self-reported their relationship category with respect to the wedding couple, enabling group mixing analysis. Beyond implications for the generalizability of interaction patterns, this dataset supports social event modeling for applications from contact tracing to social-space design.