Abstract
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.
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Acknowledgements
J.S. acknowledges support from the Academy of Finland, project “Digital Daily Rhythms” (project n:o 297195). M.K. acknowledges support from the Aalto Science Institute and the SoSweet ANR project (ANR-15-CE38-0011-01).
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Saramäki, J., Kivelä, M., Karsai, M. (2019). Weighted Temporal Event Graphs. In: Holme, P., Saramäki, J. (eds) Temporal Network Theory. Computational Social Sciences. Springer, Cham. https://doi.org/10.1007/978-3-030-23495-9_6
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