Multiscale Analysis of Spreading in a Large Communication Network

TL;DR

This paper investigates how the timing and correlations of interaction events in large temporal networks influence the speed of spreading processes, providing a multi-scale analysis using empirical data and reference models.

Contribution

It introduces a multi-scale analytical framework for understanding spreading dynamics in temporal networks, emphasizing the role of event timings and correlations.

Findings

Event timings significantly affect spreading speed.

Temporal correlations can slow down or speed up transmission.

Reference models help isolate effects of specific temporal features.

Abstract

In temporal networks, both the topology of the underlying network and the timings of interaction events can be crucial in determining how some dynamic process mediated by the network unfolds. We have explored the limiting case of the speed of spreading in the SI model, set up such that an event between an infectious and susceptible individual always transmits the infection. The speed of this process sets an upper bound for the speed of any dynamic process that is mediated through the interaction events of the network. With the help of temporal networks derived from large scale time-stamped data on mobile phone calls, we extend earlier results that point out the slowing-down effects of burstiness and temporal inhomogeneities. In such networks, links are not permanently active, but dynamic processes are mediated by recurrent events taking place on the links at specific points in time. We perform a multi-scale analysis and pinpoint the importance of the timings of event sequences on individual links, their correlations with neighboring sequences, and the temporal pathways taken by the network-scale spreading process. This is achieved by studying empirically and analytically different characteristic relay times of links, relevant to the respective scales, and a set of temporal reference models that allow for removing selected time-domain correlations one by one.