Correlation-Based Diagnostics of Social Contagion Dynamics in Multiplex Networks

TL;DR

This paper demonstrates that temporal autocorrelations in multiplex networks effectively indicate contagion localization and activation regimes, providing a lightweight method to analyze spreading dynamics even with limited data.

Contribution

It introduces a mean-field expression for node autocorrelations in social contagion multiplex models and validates their use as indicators of dynamic regimes.

Findings

Lag-one autocorrelations signal activation and localization transitions.

Temporal correlations serve as structure-agnostic probes of spreading dynamics.

Validated through simulations, these indicators are effective in partially observable systems.

Abstract

Multiplex contagion dynamics display localization phenomena in which spreading activity concentrates on a subset of layers, as well as delocalized regimes where layers behave collectively. We investigate how these regimes are encoded in temporal correlations of node activity. By deriving a closed-form mean-field expression for node autocorrelations in a contact-based social contagion multiplex model and validating it through simulations, we show that lag-one autocorrelations act as sensitive indicators of both activation and localization transitions. Our results establish temporal correlations as lightweight, structure-agnostic probes of multiplex spreading dynamics, particularly valuable in partially observable systems.