Scaling law for the impact of mutant contagion
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 as it propagates. Here, using a simple mathematical model, we quantify the downstream impact of a contagion that mutates exactly once as it travels. 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 and other infinite-dimensional networks. This prediction agrees with the observed statistics of memes propagating and mutating on Facebook, and is expected to hold universally 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. For every discipline concerned with contagion and its prevention, we anticipate that contagion pathways hold valuable lessons, given their role as the conduits through which single mutations, innovations, or failures can sweep through a network as a whole.