  The times of temporal-network events and their correlations contain
information on the function of the network and they influence dynamical
processes taking place on it. To extract information out of correlated event
times, techniques such as the analysis of temporal motifs have been developed.
We discuss a recently-introduced, more general framework that maps
temporal-network structure into static graphs while retaining information on
time-respecting paths and the time differences between their consequent events.
This framework builds on weighted temporal event graphs: directed, acyclic
graphs (DAGs) that contain a superposition of all temporal paths. We introduce
the reader to the temporal event-graph mapping and associated computational
methods and illustrate its use by applying the framework to temporal-network
percolation.
