Descendant distributions for the impact of mutant contagion on networks

TL;DR

This paper analyzes how a single mutation in a contagion spreads through networks, revealing that the distribution of descendants follows a power law decay, which aligns with observed meme propagation on social media.

Contribution

It introduces a model for the downstream impact of mutations in contagion processes and predicts a universal power law distribution across various network types.

Findings

Descendant distribution decays as d^{-2} in various networks.

Model aligns with Facebook meme propagation data.

Applicable to infinite-dimensional networks like the global contact network.

Abstract

Contagion, broadly construed, refers to anything that can spread infectiously from peer to peer. Examples include communicable diseases, rumors, misinformation, ideas, innovations, bank failures, and electrical blackouts. Sometimes, as in the 1918 Spanish flu epidemic, a contagion mutates at some point as it spreads through a network. Here, using a simple susceptible-infected (SI) model of contagion, we explore the downstream impact of a single mutation event. Assuming that this mutation occurs at a random node in the contact network, we calculate the distribution of the number of "descendants," $d$, downstream from the initial "Patient Zero" mutant. We find that the tail of the distribution decays as $d^{-2}$ for complete graphs, random graphs, small-world networks, networks with block-like structure, and other infinite-dimensional networks. This prediction agrees with the observed statistics of memes propagating and mutating on Facebook, and is expected to hold for other effectively infinite-dimensional networks, such as the global human contact network. In a wider context, our approach suggests a possible starting point for a mesoscopic theory of contagion. Such a theory would focus on the paths traced by a spreading contagion, thereby furnishing an intermediate level of description between that of individual nodes and the total infected population. We anticipate that contagion pathways will hold valuable lessons, given their role as the conduits through which single mutations, innovations, or failures can sweep through a network as a whole.