  When the focus is on the relationships or interactions between entities,
graphs offer an intuitive model for many real-world data. Such graphs are
usually large and change over time, thus, requiring models and strategies that
explore their evolution. We study the evolution of aggregated graphs and
introduce the GraphTempo model that allows temporal and attribute aggregation
not only on node level by grouping individual nodes, but on a pattern level as
well, where subgraphs are grouped together. Furthermore, We propose an
efficient strategy for exploring the evolution of the graph based on
identifying time intervals of significant growth, shrinkage or stability.
Finally, we evaluate the efficiency and effectiveness of the proposed approach
using three real graphs.
