Activity clocks: spreading dynamics on temporal networks of human contact

TL;DR

This paper investigates how activity clocks based on human contact data influence spreading dynamics on temporal networks, revealing robust patterns and the impact of heterogeneity and correlations.

Contribution

It introduces node-specific activity clocks to analyze spreading processes, uncovering statistical patterns and the effects of heterogeneity and correlations in empirical data.

Findings

Activity clocks reveal robust statistical patterns in spreading times.

Heterogeneous inter-event times and edge activity influence spreading dynamics.

Strong activity correlations can cause significant deviations from typical patterns.

Abstract

Dynamical processes on time-varying complex networks are key to understanding and modeling a broad variety of processes in socio-technical systems. Here we focus on empirical temporal networks of human proximity and we aim at understanding the factors that, in simulation, shape the arrival time distribution of simple spreading processes. Abandoning the notion of wall-clock time in favour of node-specific clocks based on activity exposes robust statistical patterns in the arrival times across different social contexts. Using randomization strategies and generative models constrained by data, we show that these patterns can be understood in terms of heterogeneous inter-event time distributions coupled with heterogeneous numbers of events per edge. We also show, both empirically and by using a synthetic dataset, that significant deviations from the above behavior can be caused by the presence of edge classes with strong activity correlations.