Correlated bursts in temporal networks slow down spreading

TL;DR

This paper investigates how correlations between bursty interaction patterns in temporal networks influence spreading dynamics, finding that positive correlations slow down the spread of infection.

Contribution

It introduces the study of correlated interevent times in spreading processes, revealing their impact on transmission speed in temporal networks.

Findings

Positive correlation between IETs slows down spreading.

Correlated bursts increase transmission times.

Shortest transmission time explains the slowdown.

Abstract

Spreading dynamics has been considered to take place in temporal networks, where temporal interaction patterns between nodes show non-Poissonian bursty nature. The effects of inhomogeneous interevent times (IETs) on the spreading have been extensively studied in recent years, yet little is known about the effects of correlations between IETs on the spreading. In order to investigate those effects, we study two-step deterministic susceptible-infected (SI) and probabilistic SI dynamics when the interaction patterns are modeled by inhomogeneous and correlated IETs, i.e., correlated bursts. By analyzing the transmission time statistics in a single-link setup and by simulating the spreading in Bethe lattices and random graphs, we conclude that the positive correlation between IETs slows down the spreading. We also argue that the shortest transmission time from one infected node to its susceptible neighbors can successfully explain our numerical results.