Effects of temporal correlations in social multiplex networks

TL;DR

This paper investigates the role of temporal correlations in social multiplex networks, revealing their impact on predictability, agent multitasking behavior, and epidemic dynamics, thus advancing understanding of complex temporal multilayer systems.

Contribution

It introduces a novel analysis of temporal correlations in multiplex networks, linking them to agent multitasking and epidemic process dynamics, which was not previously explored.

Findings

Temporal correlations increase contact predictability.

Agents exhibit multitasking with frequent social activity switching.

Epidemic spreading dynamics are significantly affected by temporal correlations.

Abstract

Multi-layered networks represent a major advance in the description of natural complex systems, and their study has shed light on new physical phenomena. Despite its importance, however, the role of the temporal dimension in their structure and function has not been investigated in much detail so far. Here we study the temporal correlations between layers exhibited by real social multiplex networks. At a basic level, the presence of such correlations implies a certain degree of predictability in the contact pattern, as we quantify by an extension of the entropy and mutual information analyses proposed for the single-layer case. At a different level, we demonstrate that temporal correlations are a signature of a 'multitasking' behavior of network agents, characterized by a higher level of switching between different social activities than expected in a uncorrelated pattern. Moreover, temporal correlations significantly affect the dynamics of coupled epidemic processes unfolding on the network. Our work opens the way for the systematic study of temporal multiplex networks and we anticipate it will be of interest to researchers in a broad array of fields.