Weighted temporal event graphs

TL;DR

This paper introduces weighted temporal event graphs, a framework that transforms temporal network data into static graphs to analyze temporal paths and correlations, aiding understanding of network function and dynamics.

Contribution

It presents a novel approach using weighted temporal event graphs to capture temporal correlations and paths in networks, extending previous motif analysis methods.

Findings

Framework effectively maps temporal networks into static graphs.

Application to temporal-network percolation demonstrates utility.

Provides computational tools for analyzing temporal paths.

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. 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.