Understanding evolving group structures in time-varying networks

This paper presents a framework for identifying persistent groups and individuals across multiple time granularities in dynamic graphs. Understanding the longevity of groups and the relevance of individuals within a group is important in many fields, including sociology, biology, economics, psychology, and political science. Different clustering algorithms have been proposed for static and dynamic graphs. However, using the clustering results to understand the changing dynamics of groups can be difficult. In order to better understand how groups evolve and the level of cohesion within these groups, we propose a holistic dynamic clustering framework that allows the user to adjust the underlying algorithms for clustering nodes in a graph that changes over time and then use the final clusters to produce a time hierarchy that highlights the groups and individuals persistent during different time periods. We test our framework and algorithm both on synthetic and real world data. Our findings indicate that our approach not only yields highly accurate results, but also detects unexpected variations in group structure.