  Leadership and followership are essential parts of collective decision and
organization in social animals, including humans. In nature, relationships of
leaders and followers are dynamic and vary with context or temporal factors.
Understanding dynamics of leadership and followership, such as how leaders and
followers change, emerge, or converge, allows scientists to gain more insight
into group decision-making and collective behavior in general. However, given
only data of individual activities, it is challenging to infer the dynamics of
leaders and followers. In this paper, we focus on mining and modeling frequent
patterns of leading and following. We formalize new computational problems and
propose a framework that can be used to address several questions regarding
group movement. We use the leadership inference framework, mFLICA, to infer the
time series of leaders and their factions from movement datasets and then
propose an approach to mine and model frequent patterns of both leadership and
followership dynamics. We evaluate our framework performance by using several
simulated datasets, as well as the real-world dataset of baboon movement to
demonstrate the applications of our framework. These are novel computational
problems and, to the best of our knowledge, there are no existing comparable
methods to address them. Thus, we modify and extend an existing leadership
inference framework to provide a non-trivial baseline for comparison. Our
framework performs better than this baseline in all datasets. Our framework
opens the opportunities for scientists to generate testable scientific
hypotheses about the dynamics of leadership in movement data.
