Distinguishing simple and complex contagion processes on networks

TL;DR

This paper introduces a method to differentiate simple and complex contagion processes on networks by analyzing infection order and local topology correlations, aiding understanding of spreading mechanisms with limited data.

Contribution

The paper proposes a novel strategy to identify contagion mechanisms from a single observed spreading process using infection order and network topology correlations.

Findings

Effective discrimination between contagion types using limited data

Method distinguishes simple, threshold, and group-driven contagions

Enhances understanding of spreading dynamics on networks

Abstract

Contagion processes on networks, including disease spreading, information diffusion, or social behaviors propagation, can be modeled as simple contagion, i.e. involving one connection at a time, or as complex contagion, in which multiple interactions are needed for a contagion event. Empirical data on spreading processes however, even when available, do not easily allow to uncover which of these underlying contagion mechanisms is at work. We propose a strategy to discriminate between these mechanisms upon the observation of a single instance of a spreading process. The strategy is based on the observation of the order in which network nodes are infected, and on its correlations with their local topology: these correlations differ between processes of simple contagion, processes involving threshold mechanisms and processes driven by group interactions (i.e., by "higher-order" mechanisms). Our results improve our understanding of contagion processes and provide a method using only limited information to distinguish between several possible contagion mechanisms.