Abstract – As the amount of available research data has exploded, methods for managing, analyzing and visualizing that information have not kept up, especially in the case of graph or relational data sets. This work will focus on a key task in improving analysis of graph data: the identification and tracking of overlapping groups of similar entities (e.g. people, samples, genes) over time.  We will develop new methods for forming a hierarchy of overlapping groups from a combination of the k-core and tree decompositions of a network, and explore its evolution in time-dependent graph data. The goal is to develop new algorithms that will improve data analysis and workflow in fields as diverse as network analysis, healthcare policy, materials science, climate simulation, fluid dynamics, bioinformatics, and cyber security.