Threshold model of cascades in temporal networks

TL;DR

This paper extends the classic threshold model to temporal networks, exploring how influence depends on contact timing and history, and analyzing the effects on cascade dynamics using real and randomized datasets.

Contribution

It introduces a temporal threshold model considering influence from contacts within a time window, advancing understanding of social spreading in dynamic contact networks.

Findings

Burstiness does not always slow cascades in threshold models.

Influence depends on contact timing and history within a specified window.

Model behavior varies between real and randomized datasets.

Abstract

Threshold models try to explain the consequences of social influence like the spread of fads and opinions. Along with models of epidemics, they constitute a major theoretical framework of social spreading processes. In threshold models on static networks, an individual changes her state if a certain fraction of her neighbors has done the same. When there are strong correlations in the temporal aspects of contact patterns, it is useful to represent the system as a temporal network. In such a system, not only contacts but also the time of the contacts are represented explicitly. There is a consensus that bursty temporal patterns slow down disease spreading. However, as we will see, this is not a universal truth for threshold models. In this work, we propose an extension of Watts' classic threshold model to temporal networks. We do this by assuming that an agent is influenced by contacts which lie a certain time into the past. I.e., the individuals are affected by contacts within a time window. In addition to thresholds as the fraction of contacts, we also investigate the number of contacts within the time window as a basis for influence. To elucidate the model's behavior, we run the model on real and randomized empirical contact datasets.