Mining and Modeling Complex Leadership Dynamics of Movement data

Leadership is an essential part of collective decision and organization in social animals, including humans. In nature, leadership is dynamic and varies with context or temporal factors. Understanding dynamics of leadership, such as how leaders 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 these dynamic leadership events. In this paper, we focus on mining and modeling frequent patterns of leadership dynamics. We formalize a new computational problem, Mining Patterns Of Leadership Dynamics, as well as propose a framework as a solution of this problem. Our framework can be used to address several questions regarding leadership dynamics of group movement. We use the leadership inference framework, mFLICA, to infer the time series of leaders from movement datasets, then propose the approach to mine and model frequent patterns of leadership dynamics. We evaluate our framework performance by using several simulated datasets, as well as using the real-world dataset of baboon movement to demonstrate the application of our framework. There are no existing methods to address this problem, thus, we modify and extend the existing leadership inference framework to provide a non-trivial baseline. Our framework performs better than this baseline in all datasets. Moreover, we also propose a method to perform statistical significance tests, comparing inferred frequent patterns of leadership dynamics with our proposed null hypotheses. Our framework opens the opportunities for scientists to generate scientific hypotheses that can be tested statistically regarding dynamics of leadership in movement data.