Modeling self-sustained activity cascades in socio-technical networks

TL;DR

This paper introduces a mechanistic model using integrate-and-fire oscillators to simulate and analyze the emergence and evolution of activity cascades in social networks, capturing their statistical properties.

Contribution

It presents a novel dynamic model that incorporates individual state evolution and time-varying activity, advancing understanding of cascade formation in socio-technical systems.

Findings

Reproduces statistical features of real cascades

Models cascade dynamics with temporal state evolution

Provides a framework for studying cascade time-evolution

Abstract

The ability to understand and eventually predict the emergence of information and activation cascades in social networks is core to complex socio-technical systems research. However, the complexity of social interactions makes this a challenging enterprise. Previous works on cascade models assume that the emergence of this collective phenomenon is related to the activity observed in the local neighborhood of individuals, but do not consider what determines the willingness to spread information in a time-varying process. Here we present a mechanistic model that accounts for the temporal evolution of the individual state in a simplified setup. We model the activity of the individuals as a complex network of interacting integrate-and-fire oscillators. The model reproduces the statistical characteristics of the cascades in real systems, and provides a framework to study time-evolution of cascades in a state-dependent activity scenario.