Reference to Global State and Social Contagion Dynamics

TL;DR

This paper introduces a model incorporating global information into social contagion dynamics across various network types, revealing optimal global info levels and the substitutive role of random links in accelerating cascades.

Contribution

It extends traditional local-interaction models by integrating global information, analyzing its effects on contagion speed and thresholds across six network classes.

Findings

Optimal global information minimizes cascade time in clustered networks.

Global information delays the tipping point but accelerates cascade completion.

Random links can substitute for global information in contagion regulation.

Abstract

The network-based model of social contagion has revolved around information on local interactions; its central focus has been on network topological properties shaping the local interactions and, ultimately, social contagion outcomes. We extend this approach by introducing information on the global state, or global information, into the network-based model and analyzing how it alters social contagion dynamics in six different classes of networks: a two-dimensional square lattice, small-world networks, Erd\H{o}s-R\'{e}nyi networks, regular random networks, Holme-Kim networks, and Barab\'{a}si-Albert networks. We find that there is an optimal amount of global information that minimizes the time to reach global cascades in highly clustered networks. We also find that global information prolongs the time to hit the tipping point but substantially compresses the time to reach global cascades after then, so that the overall time to reach global cascades can even be shortened under certain conditions. Finally, we show that random links substitute for global information in regulating the social contagion dynamics.