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. In this Chapter, 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.
Original languageEnglish
Title of host publicationTemporal Network Theory
Place of PublicationCham
ISBN (Electronic)978-3-030-23495-9
ISBN (Print)978-3-030-23494-2
Publication statusPublished - 2019
MoE publication typeA3 Book section, Chapters in research books

Publication series

NameComputational Social Sciences
ISSN (Print)2509-9574
ISSN (Electronic)2509-9582


  • Temporal correlations
  • Interevent times
  • Line graphs
  • Directed acyclic graphs
  • Percolation
  • Temporal motifs


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