Graph theory is featured as an important analytical tool for comprehending the relationships and structures underlying complex systems. Classical studies are mainly based on static graphs where it is relatively easy to analyse the connectivity, clustering and centrality as nodes and edges do not change. But this is a static view, and cannot represent the ongoing changing engagements between the entities that we see in many actual systems.

To fill this gap, the notion of dynamic or evolving graphs that are subject to change over time in structure was introduced by researchers. Dynamic graphs are also a more realistic model in social media interactions, communication currents, transportation movements and biological processes with links being created or destroyed over time. This move to the temporal domain has considerably expanded the frontiers of network science and allowed for a deeper understanding of how structure evolves and temporally varying complexities arise.