Network Modularity Controls the Speed of Information Diffusion

TL;DR

This paper investigates how the modular structure of networks influences the speed of information diffusion, revealing an optimal modularity level that maximizes spreading efficiency across various contexts.

Contribution

It demonstrates the existence of an optimal network modularity for efficient information spread and analyzes how this varies with seed and cascade sizes, using the linear threshold model.

Findings

Optimal modularity maximizes diffusion speed.

Both too high or too low modularity hinder spreading.

Analytical approximation confirms simulation results.

Abstract

The rapid diffusion of information and the adoption of social behaviors are of critical importance in situations as diverse as collective actions, pandemic prevention, or advertising and marketing. Although the dynamics of large cascades have been extensively studied in various contexts, few have systematically examined the impact of network topology on the efficiency of information diffusion. Here, by employing the linear threshold model on networks with communities, we demonstrate that a prominent network feature---the modular structure---strongly affects the speed of information diffusion in complex contagion. Our simulations show that there always exists an optimal network modularity for the most efficient spreading process. Beyond this critical value, either a stronger or a weaker modular structure actually hinders the diffusion speed. These results are confirmed by an analytical approximation. We further demonstrate that the optimal modularity varies with both the seed size and the target cascade size, and is ultimately dependent on the network under investigation. We underscore the importance of our findings in applications from marketing to epidemiology, from neuroscience to engineering, where the understanding of the structural design of complex systems focuses on the efficiency of information propagation.