Abstract Groups are inherently dynamic social entities whose function, adaptation, and outcomes are fundamentally driven by continuous changes in individual membership and affiliation patterns. Existing analytical frameworks often treat the group as the primary unit of analysis, overlooking the complexity of individual trajectories within these shifting structures. To address this gap, we introduce a unified, granular framework that shifts the unit of analysis from the group to the individual, formally representing their presence across multiple groups over time. Building on this formalization, we define a comprehensive set of individual-level and affiliation-level metrics to quantify the dynamics of participation. We validate the framework using three real-world datasets from distinct domains: online gaming (Travian) and professional sports (Football and NBA). The empirical results demonstrate the framework’s flexibility and explanatory power, revealing generalizable structural regularities. This framework provides a robust quantitative tool for characterizing and predicting individual behavior within dynamic group systems.
Ghawi et al. (Tue,) studied this question.