  Groups -- such as clusters of points or communities of nodes -- are
fundamental when addressing various data mining tasks. In temporal data, the
predominant approach for characterizing group evolution has been through the
identification of ``events". However, the events usually described in the
literature, e.g., shrinks/growths, splits/merges, are often arbitrarily
defined, creating a gap between such theoretical/predefined types and real-data
group observations. Moving beyond existing taxonomies, we think of events as
``archetypes" characterized by a unique combination of quantitative dimensions
that we call ``facets". Group dynamics are defined by their position within the
facet space, where archetypal events occupy extremities. Thus, rather than
enforcing strict event types, our approach can allow for hybrid descriptions of
dynamics involving group proximity to multiple archetypes. We apply our
framework to evolving groups from several face-to-face interaction datasets,
showing it enables richer, more reliable characterization of group dynamics
with respect to state-of-the-art methods, especially when the groups are
subject to complex relationships. Our approach also offers intuitive solutions
to common tasks related to dynamic group analysis, such as choosing an
appropriate aggregation scale, quantifying partition stability, and evaluating
event quality.
